pipen.channel
Provide some function for creating and modifying channels (dataframes)
collapse_files(data,col)(DataFrame) — Collapse a Channel according to the files in,other cols will use the values in row 0. </> expand_dir(data,col,pattern,ftype,sortby,reverse)(DataFrame) — Expand a Channel according to the files in,other cols will keep the same. </>
pipen.channel.Channel(data=None, index=None, columns=None, dtype=None, copy=None)
A DataFrame wrapper with creators
data(optional) — Dict can contain Series, arrays, constants, dataclass or list-like objects. Ifdata is a dict, column order follows insertion-order. If a dict contains Series which have an index defined, it is aligned by its index. This alignment also occurs if data is a Series or a DataFrame itself. Alignment is done on Series/DataFrame inputs.
If data is a list of dicts, column order follows insertion-order.index(Axes | None, optional) — Index to use for resulting frame. Will default to RangeIndex ifno indexing information part of input data and no index provided.columns(Axes | None, optional) — Column labels to use for resulting frame when data does not have them,defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels, will perform column selection instead.dtype(Dtype | None, optional) — Data type to force. Only a single dtype is allowed. If None, infer.copy(bool | none, optional) — Copy data from inputs.For dict data, the default of None behaves likecopy=True. For DataFrame or 2d ndarray input, the default of None behaves likecopy=False. If data is a dict containing one or more Series (possibly of different dtypes),copy=Falsewill ensure that these inputs are not copied.
.. versionchanged:: 1.3.0
T— The transpose of the DataFrame.</>at(_AtIndexer) — Access a single value for a row/column label pair.
Similar toloc, in that both provide label-based lookups. Useatif you only need to get or set a single value in a DataFrame or Series. </>attrs(dict[Hashable, Any]) — Dictionary of global attributes of this dataset.
.. warning::
attrs is experimental and may change without warning. </>axes(list) — Return a list representing the axes of the DataFrame.
It has the row axis labels and column axis labels as the only members. They are returned in that order. </>dtypes— Return the dtypes in the DataFrame.
This returns a Series with the data type of each column. The result's index is the original DataFrame's columns. Columns with mixed types are stored with theobjectdtype. See :ref:the User Guide <basics.dtypes>for more. </>empty— Indicator whether Series/DataFrame is empty.
True if Series/DataFrame is entirely empty (no items), meaning any of the axes are of length 0. </>flags(Flags) — Get the properties associated with this pandas object.
The available flags are
- :attr:
Flags.allows_duplicate_labels
- :attr:
iat(_iAtIndexer) — Access a single value for a row/column pair by integer position.
Similar toiloc, in that both provide integer-based lookups. Useiatif you only need to get or set a single value in a DataFrame or Series. </>iloc(_iLocIndexer) — Purely integer-location based indexing for selection by position.
.. deprecated:: 2.2.0
Returning a tuple from a callable is deprecated.
.iloc[]is primarily integer position based (from0tolength-1of the axis), but may also be used with a boolean array.
Allowed inputs are:
- An integer, e.g.
5. - A list or array of integers, e.g.
[4, 3, 0]. - A slice object with ints, e.g.
1:7. - A boolean array.
- A
callablefunction with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above). This is useful in method chains, when you don't have a reference to the calling object, but would like to base your selection on some value. - A tuple of row and column indexes. The tuple elements consist of one of the
above inputs, e.g.
(0, 1).
.ilocwill raiseIndexErrorif a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing (this conforms with python/numpy slice semantics).
See more at :ref:Selection by Position <indexing.integer>. </>- An integer, e.g.
loc(_LocIndexer) — Access a group of rows and columns by label(s) or a boolean array..loc[]is primarily label based, but may also be used with a boolean array.
Allowed inputs are:
- A single label, e.g.
5or'a', (note that5is interpreted as a label of the index, and never as an integer position along the index). - A list or array of labels, e.g.
['a', 'b', 'c']. - A slice object with labels, e.g.
'a':'f'.
- A boolean array of the same length as the axis being sliced,
e.g.
[True, False, True]. - An alignable boolean Series. The index of the key will be aligned before masking.
- An alignable Index. The Index of the returned selection will be the input.
- A
callablefunction with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above)
Selection by Label <indexing.label>. </>- A single label, e.g.
ndim(int) — Return an int representing the number of axes / array dimensions.
Return 1 if Series. Otherwise return 2 if DataFrame. </>shape(tuple) — Return a tuple representing the dimensionality of the DataFrame.</>size(int) — Return an int representing the number of elements in this object.
Return the number of rows if Series. Otherwise return the number of rows times number of columns if DataFrame. </>style(Styler) — Returns a Styler object.
Contains methods for building a styled HTML representation of the DataFrame. </>values— Return a Numpy representation of the DataFrame.
.. warning::
We recommend using :meth:DataFrame.to_numpyinstead.
Only the values in the DataFrame will be returned, the axes labels will be removed. </>
__add__(other)(DataFrame) — Get Addition of DataFrame and other, column-wise.</>__arrow_c_stream__(requested_schema)(PyCapsule) — Export the pandas DataFrame as an Arrow C stream PyCapsule.</>__contains__(key)(bool) — True if the key is in the info axis</>__dataframe__(nan_as_null,allow_copy)(DataFrame interchange object) — Return the dataframe interchange object implementing the interchange protocol.</>__dataframe_consortium_standard__(api_version)(Any) — Provide entry point to the Consortium DataFrame Standard API.</>__delitem__(key)— Delete item</>__dir__()(list) — Provide method name lookup and completion.</>__finalize__(other,method,**kwargs)— Propagate metadata from other to self.</>__getattr__(name)— After regular attribute access, try looking up the nameThis allows simpler access to columns for interactive use. </>__iter__()(iterator) — Iterate over info axis.</>__len__()(int) — Returns length of info axis, but here we use the index.</>__matmul__(other)(pandas.core.frame.dataframe | pandas.core.series.series) — Matrix multiplication using binary@operator.</>__repr__()(str) — Return a string representation for a particular DataFrame.</>__rmatmul__(other)(DataFrame) — Matrix multiplication using binary@operator.</>__setattr__(name,value)— After regular attribute access, try setting the nameThis allows simpler access to columns for interactive use. </>__sizeof__()(int) — Generates the total memory usage for an object that returnseither a value or Series of values </>abs()(abs) — Return a Series/DataFrame with absolute numeric value of each element.</>add(other,axis,level,fill_value)(DataFrame) — Get Addition of dataframe and other, element-wise (binary operatoradd).</>add_prefix(prefix,axis)(Series or DataFrame) — Prefix labels with stringprefix.</>add_suffix(suffix,axis)(Series or DataFrame) — Suffix labels with stringsuffix.</>aggregate(func,axis,*args,**kwargs)(scalar, Series or DataFrame) — Aggregate using one or more operations over the specified axis.</>align(other,join,axis,level,copy,fill_value,method,limit,fill_axis,broadcast_axis)(tuple of (Series/DataFrame, type of other)) — Align two objects on their axes with the specified join method.</>all(axis,bool_only,skipna,**kwargs)(Series or DataFrame) — Return whether all elements are True, potentially over an axis.</>any(axis,bool_only,skipna,**kwargs)(Series or DataFrame) — Return whether any element is True, potentially over an axis.</>apply(func,axis,raw,result_type,args,by_row,engine,engine_kwargs,**kwargs)(Series or DataFrame) — Apply a function along an axis of the DataFrame.</>applymap(func,na_action,**kwargs)(DataFrame) — Apply a function to a Dataframe elementwise.</>asfreq(freq,method,how,normalize,fill_value)(Series/DataFrame) — Convert time series to specified frequency.</>asof(where,subset)(scalar, Series, or DataFrame) — Return the last row(s) without any NaNs beforewhere.</>assign(**kwargs)(DataFrame) — Assign new columns to a DataFrame.</>astype(dtype,copy,errors)(same type as caller) — Cast a pandas object to a specified dtypedtype.</>at_time(time,asof,axis)(Series or DataFrame) — Select values at particular time of day (e.g., 9:30AM).</>backfill(axis,inplace,limit,downcast)(Series/DataFrame or None) — Fill NA/NaN values by using the next valid observation to fill the gap.</>between_time(start_time,end_time,inclusive,axis)(Series or DataFrame) — Select values between particular times of the day (e.g., 9:00-9:30 AM).</>bfill(axis,inplace,limit,limit_area,downcast)(Series/DataFrame or None) — Fill NA/NaN values by using the next valid observation to fill the gap.</>bool()(bool) — Return the bool of a single element Series or DataFrame.</>clip(lower,upper,axis,inplace,**kwargs)(Series or DataFrame or None) — Trim values at input threshold(s).</>combine(other,func,fill_value,overwrite)(DataFrame) — Perform column-wise combine with another DataFrame.</>combine_first(other)(DataFrame) — Update null elements with value in the same location inother.</>compare(other,align_axis,keep_shape,keep_equal,result_names)(DataFrame) — Compare to another DataFrame and show the differences.</>convert_dtypes(infer_objects,convert_string,convert_integer,convert_boolean,convert_floating,dtype_backend)(Series or DataFrame) — Convert columns to the best possible dtypes using dtypes supportingpd.NA.</>copy(deep)(Series or DataFrame) — Make a copy of this object's indices and data.</>corr(method,min_periods,numeric_only)(DataFrame) — Compute pairwise correlation of columns, excluding NA/null values.</>corrwith(other,axis,drop,method,numeric_only)(Series) — Compute pairwise correlation.</>count(axis,numeric_only)(Series) — Count non-NA cells for each column or row.</>cov(min_periods,ddof,numeric_only)(DataFrame) — Compute pairwise covariance of columns, excluding NA/null values.</>create(value)(DataFrame) — Create a channel from a list.</>cummax(axis,skipna,*args,**kwargs)(Series or DataFrame) — Return cumulative maximum over a DataFrame or Series axis.</>cummin(axis,skipna,*args,**kwargs)(Series or DataFrame) — Return cumulative minimum over a DataFrame or Series axis.</>cumprod(axis,skipna,*args,**kwargs)(Series or DataFrame) — Return cumulative product over a DataFrame or Series axis.</>cumsum(axis,skipna,*args,**kwargs)(Series or DataFrame) — Return cumulative sum over a DataFrame or Series axis.</>describe(percentiles,include,exclude)(Series or DataFrame) — Generate descriptive statistics.</>diff(periods,axis)(DataFrame) — First discrete difference of element.</>dot(other)(Series or DataFrame) — Compute the matrix multiplication between the DataFrame and other.</>drop(labels,axis,index,columns,level,inplace,errors)(DataFrame or None) — Drop specified labels from rows or columns.</>drop_duplicates(subset,keep,inplace,ignore_index)(DataFrame or None) — Return DataFrame with duplicate rows removed.</>droplevel(level,axis)(Series/DataFrame) — Return Series/DataFrame with requested index / column level(s) removed.</>dropna(axis,how,thresh,subset,inplace,ignore_index)(DataFrame or None) — Remove missing values.</>duplicated(subset,keep)(Series) — Return boolean Series denoting duplicate rows.</>eq(other,axis,level)(DataFrame of bool) — Get Equal to of dataframe and other, element-wise (binary operatoreq).</>equals(other)(bool) — Test whether two objects contain the same elements.</>eval(expr,inplace,**kwargs)(ndarray, scalar, pandas object, or None) — Evaluate a string describing operations on DataFrame columns.</>ewm(com,span,halflife,alpha,min_periods,adjust,ignore_na,axis,times,method)(pandas.api.typing.ExponentialMovingWindow) — Provide exponentially weighted (EW) calculations.</>expanding(min_periods,axis,method)(pandas.api.typing.Expanding) — Provide expanding window calculations.</>explode(column,ignore_index)(DataFrame) — Transform each element of a list-like to a row, replicating index values.</>ffill(axis,inplace,limit,limit_area,downcast)(Series/DataFrame or None) — Fill NA/NaN values by propagating the last valid observation to next valid.</>fillna(value,method,axis,inplace,limit,downcast)(Series/DataFrame or None) — Fill NA/NaN values using the specified method.</>filter(items,like,regex,axis)(same type as input object) — Subset the dataframe rows or columns according to the specified index labels.</>first(offset)(Series or DataFrame) — Select initial periods of time series data based on a date offset.</>first_valid_index()(type of index) — Return index for first non-NA value or None, if no non-NA value is found.</>floordiv(other,axis,level,fill_value)(DataFrame) — Get Integer division of dataframe and other, element-wise (binary operatorfloordiv).</>from_csv(*args,**kwargs)— Create a channel from a csv file</>from_dict(data,orient,dtype,columns)(DataFrame) — Construct DataFrame from dict of array-like or dicts.</>from_excel(*args,**kwargs)— Create a channel from an excel file.</>from_glob(pattern,ftype,sortby,reverse)(DataFrame) — Create a channel with a glob pattern</>from_pairs(pattern,ftype,sortby,reverse)(DataFrame) — Create a width=2 channel with a glob pattern</>from_records(data,index,exclude,columns,coerce_float,nrows)(DataFrame) — Convert structured or record ndarray to DataFrame.</>from_table(*args,**kwargs)— Create a channel from a table file.</>ge(other,axis,level)(DataFrame of bool) — Get Greater than or equal to of dataframe and other, element-wise (binary operatorge).</>get(key,default)(same type as items contained in object) — Get item from object for given key (ex: DataFrame column).</>groupby(by,axis,level,as_index,sort,group_keys,observed,dropna)(pandas.api.typing.DataFrameGroupBy) — Group DataFrame using a mapper or by a Series of columns.</>gt(other,axis,level)(DataFrame of bool) — Get Greater than of dataframe and other, element-wise (binary operatorgt).</>head(n)(same type as caller) — Return the firstnrows.</>idxmax(axis,skipna,numeric_only)(Series) — Return index of first occurrence of maximum over requested axis.</>idxmin(axis,skipna,numeric_only)(Series) — Return index of first occurrence of minimum over requested axis.</>infer_objects(copy)(same type as input object) — Attempt to infer better dtypes for object columns.</>info(verbose,buf,max_cols,memory_usage,show_counts)(None) — Print a concise summary of a DataFrame.</>insert(loc,column,value,allow_duplicates)— Insert column into DataFrame at specified location.</>interpolate(method,axis,limit,inplace,limit_direction,limit_area,downcast,**kwargs)(Series or DataFrame or None) — Fill NaN values using an interpolation method.</>isetitem(loc,value)— Set the given value in the column with positionloc.</>isin(values)(DataFrame) — Whether each element in the DataFrame is contained in values.</>isna()(DataFrame) — Detect missing values.</>isnull()(DataFrame) — DataFrame.isnull is an alias for DataFrame.isna.</>items()(label : object) — Iterate over (column name, Series) pairs.</>iterrows()(index : label or tuple of label) — Iterate over DataFrame rows as (index, Series) pairs.</>itertuples(index,name)(iterator) — Iterate over DataFrame rows as namedtuples.</>join(other,on,how,lsuffix,rsuffix,sort,validate)(DataFrame) — Join columns of another DataFrame.</>keys()(Index) — Get the 'info axis' (see Indexing for more).</>kurt(axis,skipna,numeric_only,**kwargs)(Series or scalar) — Return unbiased kurtosis over requested axis.</>last(offset)(Series or DataFrame) — Select final periods of time series data based on a date offset.</>last_valid_index()(type of index) — Return index for last non-NA value or None, if no non-NA value is found.</>le(other,axis,level)(DataFrame of bool) — Get Less than or equal to of dataframe and other, element-wise (binary operatorle).</>lt(other,axis,level)(DataFrame of bool) — Get Less than of dataframe and other, element-wise (binary operatorlt).</>map(func,na_action,**kwargs)(DataFrame) — Apply a function to a Dataframe elementwise.</>mask(cond,other,inplace,axis,level)(Same type as caller or None if ``inplace=True``.) — Replace values where the condition is True.</>max(axis,skipna,numeric_only,**kwargs)(Series or scalar) — Return the maximum of the values over the requested axis.</>mean(axis,skipna,numeric_only,**kwargs)(Series or scalar) — Return the mean of the values over the requested axis.</>median(axis,skipna,numeric_only,**kwargs)(Series or scalar) — Return the median of the values over the requested axis.</>melt(id_vars,value_vars,var_name,value_name,col_level,ignore_index)(DataFrame) — Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.</>memory_usage(index,deep)(Series) — Return the memory usage of each column in bytes.</>merge(right,how,on,left_on,right_on,left_index,right_index,sort,suffixes,copy,indicator,validate)(DataFrame) — Merge DataFrame or named Series objects with a database-style join.</>min(axis,skipna,numeric_only,**kwargs)(Series or scalar) — Return the minimum of the values over the requested axis.</>mod(other,axis,level,fill_value)(DataFrame) — Get Modulo of dataframe and other, element-wise (binary operatormod).</>mode(axis,numeric_only,dropna)(DataFrame) — Get the mode(s) of each element along the selected axis.</>mul(other,axis,level,fill_value)(DataFrame) — Get Multiplication of dataframe and other, element-wise (binary operatormul).</>ne(other,axis,level)(DataFrame of bool) — Get Not equal to of dataframe and other, element-wise (binary operatorne).</>nlargest(n,columns,keep)(DataFrame) — Return the firstnrows ordered bycolumnsin descending order.</>notna()(DataFrame) — Detect existing (non-missing) values.</>notnull()(DataFrame) — DataFrame.notnull is an alias for DataFrame.notna.</>nsmallest(n,columns,keep)(DataFrame) — Return the firstnrows ordered bycolumnsin ascending order.</>nunique(axis,dropna)(Series) — Count number of distinct elements in specified axis.</>pad(axis,inplace,limit,downcast)(Series/DataFrame or None) — Fill NA/NaN values by propagating the last valid observation to next valid.</>pct_change(periods,fill_method,limit,freq,**kwargs)(Series or DataFrame) — Fractional change between the current and a prior element.</>pipe(func,*args,**kwargs)(the return type of ``func``.) — Apply chainable functions that expect Series or DataFrames.</>pivot(columns,index,values)(DataFrame) — Return reshaped DataFrame organized by given index / column values.</>pivot_table(values,index,columns,aggfunc,fill_value,margins,dropna,margins_name,observed,sort)(DataFrame) — Create a spreadsheet-style pivot table as a DataFrame.</>pop(item)(Series) — Return item and drop from frame. Raise KeyError if not found.</>pow(other,axis,level,fill_value)(DataFrame) — Get Exponential power of dataframe and other, element-wise (binary operatorpow).</>prod(axis,skipna,numeric_only,min_count,**kwargs)(Series or scalar) — Return the product of the values over the requested axis.</>quantile(q,axis,numeric_only,interpolation,method)(Series or DataFrame) — Return values at the given quantile over requested axis.</>query(expr,inplace,**kwargs)(DataFrame or None) — Query the columns of a DataFrame with a boolean expression.</>radd(other,axis,level,fill_value)(DataFrame) — Get Addition of dataframe and other, element-wise (binary operatorradd).</>rank(axis,method,numeric_only,na_option,ascending,pct)(same type as caller) — Compute numerical data ranks (1 through n) along axis.</>reindex(labels,index,columns,axis,method,copy,level,fill_value,limit,tolerance)(DataFrame with changed index.) — Conform DataFrame to new index with optional filling logic.</>reindex_like(other,method,copy,limit,tolerance)(Series or DataFrame) — Return an object with matching indices as other object.</>rename(mapper,index,columns,axis,copy,inplace,level,errors)(DataFrame or None) — Rename columns or index labels.</>rename_axis(mapper,index,columns,axis,copy,inplace)(Series, DataFrame, or None) — Set the name of the axis for the index or columns.</>reorder_levels(order,axis)(DataFrame) — Rearrange index levels using input order. May not drop or duplicate levels.</>replace(to_replace,value,inplace,limit,regex,method)(Series/DataFrame) — Replace values given into_replacewithvalue.</>resample(rule,axis,closed,label,convention,kind,on,level,origin,offset,group_keys)(pandas.api.typing.Resampler) — Resample time-series data.</>reset_index(level,drop,inplace,col_level,col_fill,allow_duplicates,names)(DataFrame or None) — Reset the index, or a level of it.</>rfloordiv(other,axis,level,fill_value)(DataFrame) — Get Integer division of dataframe and other, element-wise (binary operatorrfloordiv).</>rmod(other,axis,level,fill_value)(DataFrame) — Get Modulo of dataframe and other, element-wise (binary operatorrmod).</>rmul(other,axis,level,fill_value)(DataFrame) — Get Multiplication of dataframe and other, element-wise (binary operatorrmul).</>rolling(window,min_periods,center,win_type,on,axis,closed,step,method)(pandas.api.typing.Window or pandas.api.typing.Rolling) — Provide rolling window calculations.</>round(decimals,*args,**kwargs)(DataFrame) — Round a DataFrame to a variable number of decimal places.</>rpow(other,axis,level,fill_value)(DataFrame) — Get Exponential power of dataframe and other, element-wise (binary operatorrpow).</>rsub(other,axis,level,fill_value)(DataFrame) — Get Subtraction of dataframe and other, element-wise (binary operatorrsub).</>rtruediv(other,axis,level,fill_value)(DataFrame) — Get Floating division of dataframe and other, element-wise (binary operatorrtruediv).</>sample(n,frac,replace,weights,random_state,axis,ignore_index)(Series or DataFrame) — Return a random sample of items from an axis of object.</>select_dtypes(include,exclude)(DataFrame) — Return a subset of the DataFrame's columns based on the column dtypes.</>sem(axis,skipna,ddof,numeric_only,**kwargs)(Series or DataFrame (if level specified)) — Return unbiased standard error of the mean over requested axis.</>set_axis(labels,axis,copy)(DataFrame) — Assign desired index to given axis.</>set_flags(copy,allows_duplicate_labels)(Series or DataFrame) — Return a new object with updated flags.</>set_index(keys,drop,append,inplace,verify_integrity)(DataFrame or None) — Set the DataFrame index using existing columns.</>shift(periods,freq,axis,fill_value,suffix)(DataFrame) — Shift index by desired number of periods with an optional timefreq.</>skew(axis,skipna,numeric_only,**kwargs)(Series or scalar) — Return unbiased skew over requested axis.</>sort_index(axis,level,ascending,inplace,kind,na_position,sort_remaining,ignore_index,key)(DataFrame or None) — Sort object by labels (along an axis).</>sort_values(by,axis,ascending,inplace,kind,na_position,ignore_index,key)(DataFrame or None) — Sort by the values along either axis.</>squeeze(axis)(DataFrame, Series, or scalar) — Squeeze 1 dimensional axis objects into scalars.</>stack(level,dropna,sort,future_stack)(DataFrame or Series) — Stack the prescribed level(s) from columns to index.</>std(axis,skipna,ddof,numeric_only,**kwargs)(Series or DataFrame (if level specified)) — Return sample standard deviation over requested axis.</>sub(other,axis,level,fill_value)(DataFrame) — Get Subtraction of dataframe and other, element-wise (binary operatorsub).</>sum(axis,skipna,numeric_only,min_count,**kwargs)(Series or scalar) — Return the sum of the values over the requested axis.</>swapaxes(axis1,axis2,copy)(same as input) — Interchange axes and swap values axes appropriately.</>swaplevel(i,j,axis)(DataFrame) — Swap levels i and j in a :class:MultiIndex.</>tail(n)(type of caller) — Return the lastnrows.</>take(indices,axis,**kwargs)(same type as caller) — Return the elements in the given positional indices along an axis.</>to_clipboard(excel,sep,**kwargs)— Copy object to the system clipboard.</>to_csv(path_or_buf,sep,na_rep,float_format,columns,header,index,index_label,mode,encoding,compression,quoting,quotechar,lineterminator,chunksize,date_format,doublequote,escapechar,decimal,errors,storage_options)(None or str) — Write object to a comma-separated values (csv) file.</>to_dict(orient,into,index)(dict, list or collections.abc.MutableMapping) — Convert the DataFrame to a dictionary.</>to_excel(excel_writer,sheet_name,na_rep,float_format,columns,header,index,index_label,startrow,startcol,engine,merge_cells,inf_rep,freeze_panes,storage_options,engine_kwargs)— Write object to an Excel sheet.</>to_feather(path,**kwargs)— Write a DataFrame to the binary Feather format.</>to_gbq(destination_table,project_id,chunksize,reauth,if_exists,auth_local_webserver,table_schema,location,progress_bar,credentials)— Write a DataFrame to a Google BigQuery table.</>to_hdf(path_or_buf,key,mode,complevel,complib,append,format,index,min_itemsize,nan_rep,dropna,data_columns,errors,encoding)— Write the contained data to an HDF5 file using HDFStore.</>to_html(buf,columns,col_space,header,index,na_rep,formatters,float_format,sparsify,index_names,justify,max_rows,max_cols,show_dimensions,decimal,bold_rows,classes,escape,notebook,border,table_id,render_links,encoding)(str or None) — Render a DataFrame as an HTML table.</>to_json(path_or_buf,orient,date_format,double_precision,force_ascii,date_unit,default_handler,lines,compression,index,indent,storage_options,mode)(None or str) — Convert the object to a JSON string.</>to_latex(buf,columns,header,index,na_rep,formatters,float_format,sparsify,index_names,bold_rows,column_format,longtable,escape,encoding,decimal,multicolumn,multicolumn_format,multirow,caption,label,position)(str or None) — Render object to a LaTeX tabular, longtable, or nested table.</>to_markdown(buf,mode,index,storage_options,**kwargs)(str) — Print DataFrame in Markdown-friendly format.</>to_numpy(dtype,copy,na_value)(numpy.ndarray) — Convert the DataFrame to a NumPy array.</>to_orc(path,engine,index,engine_kwargs)(bytes if no path argument is provided else None) — Write a DataFrame to the ORC format.</>to_parquet(path,engine,compression,index,partition_cols,storage_options,**kwargs)(bytes if no path argument is provided else None) — Write a DataFrame to the binary parquet format.</>to_period(freq,axis,copy)(DataFrame) — Convert DataFrame from DatetimeIndex to PeriodIndex.</>to_pickle(path,compression,protocol,storage_options)— Pickle (serialize) object to file.</>to_records(index,column_dtypes,index_dtypes)(numpy.rec.recarray) — Convert DataFrame to a NumPy record array.</>to_sql(name,con,schema,if_exists,index,index_label,chunksize,dtype,method)(None or int) — Write records stored in a DataFrame to a SQL database.</>to_stata(path,convert_dates,write_index,byteorder,time_stamp,data_label,variable_labels,version,convert_strl,compression,storage_options,value_labels)— Export DataFrame object to Stata dta format.</>to_string(buf,columns,col_space,header,index,na_rep,formatters,float_format,sparsify,index_names,justify,max_rows,max_cols,show_dimensions,decimal,line_width,min_rows,max_colwidth,encoding)(str or None) — Render a DataFrame to a console-friendly tabular output.</>to_timestamp(freq,how,axis,copy)(DataFrame) — Cast to DatetimeIndex of timestamps, at beginning of period.</>to_xarray()(xarray.DataArray or xarray.Dataset) — Return an xarray object from the pandas object.</>to_xml(path_or_buffer,index,root_name,row_name,na_rep,attr_cols,elem_cols,namespaces,prefix,encoding,xml_declaration,pretty_print,parser,stylesheet,compression,storage_options)(None or str) — Render a DataFrame to an XML document.</>transform(func,axis,*args,**kwargs)(DataFrame) — Callfuncon self producing a DataFrame with the same axis shape as self.</>transpose(*args,copy)(DataFrame) — Transpose index and columns.</>truediv(other,axis,level,fill_value)(DataFrame) — Get Floating division of dataframe and other, element-wise (binary operatortruediv).</>truncate(before,after,axis,copy)(type of caller) — Truncate a Series or DataFrame before and after some index value.</>tz_convert(tz,axis,level,copy)(Series/DataFrame) — Convert tz-aware axis to target time zone.</>tz_localize(tz,axis,level,copy,ambiguous,nonexistent)(Series/DataFrame) — Localize tz-naive index of a Series or DataFrame to target time zone.</>unstack(level,fill_value,sort)(Series or DataFrame) — Pivot a level of the (necessarily hierarchical) index labels.</>update(other,join,overwrite,filter_func,errors)(None) — Modify in place using non-NA values from another DataFrame.</>value_counts(subset,normalize,sort,ascending,dropna)(Series) — Return a Series containing the frequency of each distinct row in the Dataframe.</>var(axis,skipna,ddof,numeric_only,**kwargs)(Series or DataFrame (if level specified)) — Return unbiased variance over requested axis.</>where(cond,other,inplace,axis,level)(Same type as caller or None if ``inplace=True``.) — Replace values where the condition is False.</>xs(key,axis,level,drop_level)(Series or DataFrame) — Return cross-section from the Series/DataFrame.</>
__add__(other)
Get Addition of DataFrame and other, column-wise.
Equivalent to DataFrame.add(other).
other(scalar, sequence, Series, dict or DataFrame) — Object to be added to the DataFrame.
The result of adding other to DataFrame.
DataFrame.add : Add a DataFrame and another object, with option for index- or column-oriented addition.
>>> df = pd.DataFrame({'height': [1.5, 2.6], 'weight': [500, 800]},... index=['elk', 'moose'])
>>> df
height weight
elk 1.5 500
moose 2.6 800
Adding a scalar affects all rows and columns.
>>> df[['height', 'weight']] + 1.5
height weight
elk 3.0 501.5
moose 4.1 801.5
Each element of a list is added to a column of the DataFrame, in order.
>>> df[['height', 'weight']] + [0.5, 1.5]
height weight
elk 2.0 501.5
moose 3.1 801.5
Keys of a dictionary are aligned to the DataFrame, based on column names; each value in the dictionary is added to the corresponding column.
>>> df[['height', 'weight']] + {'height': 0.5, 'weight': 1.5}
height weight
elk 2.0 501.5
moose 3.1 801.5
When other is a :class:Series, the index of other is aligned with the
columns of the DataFrame.
>>> s1 = pd.Series([0.5, 1.5], index=['weight', 'height'])
>>> df[['height', 'weight']] + s1
height weight
elk 3.0 500.5
moose 4.1 800.5
Even when the index of other is the same as the index of the DataFrame,
the :class:Series will not be reoriented. If index-wise alignment is desired,
:meth:DataFrame.add should be used with axis='index'.
>>> s2 = pd.Series([0.5, 1.5], index=['elk', 'moose'])
>>> df[['height', 'weight']] + s2
elk height moose weight
elk NaN NaN NaN NaN
moose NaN NaN NaN NaN
>>> df[['height', 'weight']].add(s2, axis='index')
height weight
elk 2.0 500.5
moose 4.1 801.5
When other is a :class:DataFrame, both columns names and the
index are aligned.
>>> other = pd.DataFrame({'height': [0.2, 0.4, 0.6]},
... index=['elk', 'moose', 'deer'])
>>> df[['height', 'weight']] + other
height weight
deer NaN NaN
elk 1.7 NaN
moose 3.0 NaN
__dir__() → list
Provide method name lookup and completion.
Notes
Only provide 'public' methods.
__sizeof__() → int
Generates the total memory usage for an object that returnseither a value or Series of values
set_flags(copy=False, allows_duplicate_labels=None)
Return a new object with updated flags.
copy(bool, default False) — Specify if a copy of the object should be made.
.. note:: Thecopykeyword will change behavior in pandas 3.0.Copy-on-Write <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>__ will be enabled by default, which means that all methods with acopykeyword will use a lazy copy mechanism to defer the copy and ignore thecopykeyword. Thecopykeyword will be removed in a future version of pandas.
You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True``allows_duplicate_labels(bool, optional) — Whether the returned object allows duplicate labels.
The same type as the caller.
DataFrame.attrs : Global metadata applying to this dataset.DataFrame.flags : Global flags applying to this object.
Notes
This method returns a new object that's a view on the same data as the input. Mutating the input or the output values will be reflected in the other.
This method is intended to be used in method chains.
"Flags" differ from "metadata". Flags reflect properties of the
pandas object (the Series or DataFrame). Metadata refer to properties
of the dataset, and should be stored in :attr:DataFrame.attrs.
>>> df = pd.DataFrame({"A": [1, 2]})>>> df.flags.allows_duplicate_labels
True
>>> df2 = df.set_flags(allows_duplicate_labels=False)
>>> df2.flags.allows_duplicate_labels
False
swapaxes(axis1, axis2, copy=None)
Interchange axes and swap values axes appropriately.
.. deprecated:: 2.1.0
swapaxes is deprecated and will be removed.
Please use transpose instead.
Please see examples for :meth:DataFrame.transpose.
droplevel(level, axis=0)
Return Series/DataFrame with requested index / column level(s) removed.
level(int, str, or list-like) — If a string is given, must be the name of a levelIf list-like, elements must be names or positional indexes of levels.axis({0 or 'index', 1 or 'columns'}, default 0) — Axis along which the level(s) is removed:- 0 or 'index': remove level(s) in column.
- 1 or 'columns': remove level(s) in row.
Seriesthis parameter is unused and defaults to 0.
Series/DataFrame with requested index / column level(s) removed.
>>> df = pd.DataFrame([... [1, 2, 3, 4],
... [5, 6, 7, 8],
... [9, 10, 11, 12]
... ]).set_index([0, 1]).rename_axis(['a', 'b'])
>>> df.columns = pd.MultiIndex.from_tuples([
... ('c', 'e'), ('d', 'f')
... ], names=['level_1', 'level_2'])
>>> df
level_1 c d
level_2 e f
a b
1 2 3 4
5 6 7 8
9 10 11 12
>>> df.droplevel('a')
level_1 c d
level_2 e f
b
2 3 4
6 7 8
10 11 12
>>> df.droplevel('level_2', axis=1)
level_1 c d
a b
1 2 3 4
5 6 7 8
9 10 11 12
squeeze(axis=None)
Squeeze 1 dimensional axis objects into scalars.
Series or DataFrames with a single element are squeezed to a scalar. DataFrames with a single column or a single row are squeezed to a Series. Otherwise the object is unchanged.
This method is most useful when you don't know if your
object is a Series or DataFrame, but you do know it has just a single
column. In that case you can safely call squeeze to ensure you have a
Series.
axis({0 or 'index', 1 or 'columns', None}, default None) — A specific axis to squeeze. By default, all length-1 axes aresqueezed. ForSeriesthis parameter is unused and defaults toNone.
The projection after squeezing axis or all the axes.
Series.iloc : Integer-location based indexing for selecting scalars.DataFrame.iloc : Integer-location based indexing for selecting Series. Series.to_frame : Inverse of DataFrame.squeeze for a single-column DataFrame.
>>> primes = pd.Series([2, 3, 5, 7])Slicing might produce a Series with a single value:
>>> even_primes = primes[primes % 2 == 0]
>>> even_primes
0 2
dtype: int64
>>> even_primes.squeeze()
2
Squeezing objects with more than one value in every axis does nothing:
>>> odd_primes = primes[primes % 2 == 1]
>>> odd_primes
1 3
2 5
3 7
dtype: int64
>>> odd_primes.squeeze()
1 3
2 5
3 7
dtype: int64
Squeezing is even more effective when used with DataFrames.
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'])
>>> df
a b
0 1 2
1 3 4
Slicing a single column will produce a DataFrame with the columns having only one value:
>>> df_a = df[['a']]
>>> df_a
a
0 1
1 3
So the columns can be squeezed down, resulting in a Series:
>>> df_a.squeeze('columns')
0 1
1 3
Name: a, dtype: int64
Slicing a single row from a single column will produce a single scalar DataFrame:
>>> df_0a = df.loc[df.index < 1, ['a']]
>>> df_0a
a
0 1
Squeezing the rows produces a single scalar Series:
>>> df_0a.squeeze('rows')
a 1
Name: 0, dtype: int64
Squeezing all axes will project directly into a scalar:
>>> df_0a.squeeze()
1
rename_axis(mapper=<no_default>, index=<no_default>, columns=<no_default>, axis=0, copy=None, inplace=False)
Set the name of the axis for the index or columns.
mapper(scalar, list-like, optional) — Value to set the axis name attribute.axis({0 or 'index', 1 or 'columns'}, default 0) — The axis to rename. ForSeriesthis parameter is unused and defaults to 0.copy(bool, default None) — Also copy underlying data.
.. note:: Thecopykeyword will change behavior in pandas 3.0.Copy-on-Write <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>__ will be enabled by default, which means that all methods with acopykeyword will use a lazy copy mechanism to defer the copy and ignore thecopykeyword. Thecopykeyword will be removed in a future version of pandas.
You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True``inplace(bool, default False) — Modifies the object directly, instead of creating a new Seriesor DataFrame.
The same type as the caller or None if inplace=True.
Series.rename : Alter Series index labels or name.DataFrame.rename : Alter DataFrame index labels or name. Index.rename : Set new names on index.
Notes
DataFrame.rename_axis supports two calling conventions
(index=index_mapper, columns=columns_mapper, ...)(mapper, axis={'index', 'columns'}, ...)
The first calling convention will only modify the names of
the index and/or the names of the Index object that is the columns.
In this case, the parameter copy is ignored.
The second calling convention will modify the names of the corresponding index if mapper is a list or a scalar. However, if mapper is dict-like or a function, it will use the deprecated behavior of modifying the axis labels.
We highly recommend using keyword arguments to clarify your intent.
Series
>>> s = pd.Series(["dog", "cat", "monkey"])
>>> s
0 dog
1 cat
2 monkey
dtype: object
>>> s.rename_axis("animal")
animal
0 dog
1 cat
2 monkey
dtype: object
DataFrame
>>> df = pd.DataFrame({"num_legs": [4, 4, 2],
... "num_arms": [0, 0, 2]},
... ["dog", "cat", "monkey"])
>>> df
num_legs num_arms
dog 4 0
cat 4 0
monkey 2 2
>>> df = df.rename_axis("animal")
>>> df
num_legs num_arms
animal
dog 4 0
cat 4 0
monkey 2 2
>>> df = df.rename_axis("limbs", axis="columns")
>>> df
limbs num_legs num_arms
animal
dog 4 0
cat 4 0
monkey 2 2
MultiIndex
>>> df.index = pd.MultiIndex.from_product([['mammal'],
... ['dog', 'cat', 'monkey']],
... names=['type', 'name'])
>>> df
limbs num_legs num_arms
type name
mammal dog 4 0
cat 4 0
monkey 2 2
>>> df.rename_axis(index={'type': 'class'})
limbs num_legs num_arms
class name
mammal dog 4 0
cat 4 0
monkey 2 2
>>> df.rename_axis(columns=str.upper)
LIMBS num_legs num_arms
type name
mammal dog 4 0
cat 4 0
monkey 2 2
equals(other)
Test whether two objects contain the same elements.
This function allows two Series or DataFrames to be compared against each other to see if they have the same shape and elements. NaNs in the same location are considered equal.
The row/column index do not need to have the same type, as long as the values are considered equal. Corresponding columns and index must be of the same dtype.
other(Series or DataFrame) — The other Series or DataFrame to be compared with the first.
True if all elements are the same in both objects, Falseotherwise.
Series.eq : Compare two Series objects of the same length and return a Series where each element is True if the element in each Series is equal, False otherwise. DataFrame.eq : Compare two DataFrame objects of the same shape and return a DataFrame where each element is True if the respective element in each DataFrame is equal, False otherwise. testing.assert_series_equal : Raises an AssertionError if left and right are not equal. Provides an easy interface to ignore inequality in dtypes, indexes and precision among others. testing.assert_frame_equal : Like assert_series_equal, but targets DataFrames. numpy.array_equal : Return True if two arrays have the same shape and elements, False otherwise.
>>> df = pd.DataFrame({1: [10], 2: [20]})>>> df
1 2
0 10 20
DataFrames df and exactly_equal have the same types and values for their elements and column labels, which will return True.
>>> exactly_equal = pd.DataFrame({1: [10], 2: [20]})
>>> exactly_equal
1 2
0 10 20
>>> df.equals(exactly_equal)
True
DataFrames df and different_column_type have the same element types and values, but have different types for the column labels, which will still return True.
>>> different_column_type = pd.DataFrame({1.0: [10], 2.0: [20]})
>>> different_column_type
1.0 2.0
0 10 20
>>> df.equals(different_column_type)
True
DataFrames df and different_data_type have different types for the same values for their elements, and will return False even though their column labels are the same values and types.
>>> different_data_type = pd.DataFrame({1: [10.0], 2: [20.0]})
>>> different_data_type
1 2
0 10.0 20.0
>>> df.equals(different_data_type)
False
bool()
Return the bool of a single element Series or DataFrame.
.. deprecated:: 2.1.0
bool is deprecated and will be removed in future version of pandas.
For Series use pandas.Series.item.
This must be a boolean scalar value, either True or False. It will raise a ValueError if the Series or DataFrame does not have exactly 1 element, or that element is not boolean (integer values 0 and 1 will also raise an exception).
The value in the Series or DataFrame.
Series.astype : Change the data type of a Series, including to boolean.DataFrame.astype : Change the data type of a DataFrame, including to boolean. numpy.bool_ : NumPy boolean data type, used by pandas for boolean values.
The method will only work for single element objects with a boolean value:
>>> pd.Series([True]).bool() # doctest: +SKIP
True
>>> pd.Series([False]).bool() # doctest: +SKIP
False
>>> pd.DataFrame({'col': [True]}).bool() # doctest: +SKIP
True
>>> pd.DataFrame({'col': [False]}).bool() # doctest: +SKIP
False
This is an alternative method and will only work for single element objects with a boolean value:
>>> pd.Series([True]).item() # doctest: +SKIP
True
>>> pd.Series([False]).item() # doctest: +SKIP
False
abs()
Return a Series/DataFrame with absolute numeric value of each element.
This function only applies to elements that are all numeric.
Series/DataFrame containing the absolute value of each element.
numpy.absolute : Calculate the absolute value element-wise.
Notes
For complex inputs, 1.2 + 1j, the absolute value is
:math:\sqrt{ a^2 + b^2 }.
Absolute numeric values in a Series.
>>> s = pd.Series([-1.10, 2, -3.33, 4])
>>> s.abs()
0 1.10
1 2.00
2 3.33
3 4.00
dtype: float64
Absolute numeric values in a Series with complex numbers.
>>> s = pd.Series([1.2 + 1j])
>>> s.abs()
0 1.56205
dtype: float64
Absolute numeric values in a Series with a Timedelta element.
>>> s = pd.Series([pd.Timedelta('1 days')])
>>> s.abs()
0 1 days
dtype: timedelta64[ns]
Select rows with data closest to certain value using argsort (from
StackOverflow <https://stackoverflow.com/a/17758115>__).
>>> df = pd.DataFrame({
... 'a': [4, 5, 6, 7],
... 'b': [10, 20, 30, 40],
... 'c': [100, 50, -30, -50]
... })
>>> df
a b c
0 4 10 100
1 5 20 50
2 6 30 -30
3 7 40 -50
>>> df.loc[(df.c - 43).abs().argsort()]
a b c
1 5 20 50
0 4 10 100
2 6 30 -30
3 7 40 -50
__iter__()
Iterate over info axis.
Info axis as iterator.
>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})>>> for x in df:
... print(x)
A
B
keys()
Get the 'info axis' (see Indexing for more).
This is index for Series, columns for DataFrame.
Info axis.
>>> d = pd.DataFrame(data={'A': [1, 2, 3], 'B': [0, 4, 8]},... index=['a', 'b', 'c'])
>>> d
A B
a 1 0
b 2 4
c 3 8
>>> d.keys()
Index(['A', 'B'], dtype='object')
__contains__(key) → bool
True if the key is in the info axis
to_excel(excel_writer, sheet_name='Sheet1', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, inf_rep='inf', freeze_panes=None, storage_options=None, engine_kwargs=None)
Write object to an Excel sheet.
To write a single object to an Excel .xlsx file it is only necessary to
specify a target file name. To write to multiple sheets it is necessary to
create an ExcelWriter object with a target file name, and specify a sheet
in the file to write to.
Multiple sheets may be written to by specifying unique sheet_name.
With all data written to the file it is necessary to save the changes.
Note that creating an ExcelWriter object with a file name that already
exists will result in the contents of the existing file being erased.
excel_writer(path-like, file-like, or ExcelWriter object) — File path or existing ExcelWriter.sheet_name(str, default 'Sheet1') — Name of sheet which will contain DataFrame.na_rep(str, default '') — Missing data representation.float_format(str, optional) — Format string for floating point numbers. For examplefloat_format="%.2f"will format 0.1234 to 0.12.columns(sequence or list of str, optional) — Columns to write.header(bool or list of str, default True) — Write out the column names. If a list of string is given it isassumed to be aliases for the column names.index(bool, default True) — Write row names (index).index_label(str or sequence, optional) — Column label for index column(s) if desired. If not specified, andheaderandindexare True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex.startrow(int, default 0) — Upper left cell row to dump data frame.startcol(int, default 0) — Upper left cell column to dump data frame.engine(str, optional) — Write engine to use, 'openpyxl' or 'xlsxwriter'. You can also set thisvia the optionsio.excel.xlsx.writerorio.excel.xlsm.writer.merge_cells(bool, default True) — Write MultiIndex and Hierarchical Rows as merged cells.inf_rep(str, default 'inf') — Representation for infinity (there is no native representation forinfinity in Excel).freeze_panes(tuple of int (length 2), optional) — Specifies the one-based bottommost row and rightmost column thatis to be frozen.storage_options(dict, optional) — Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded tourllib.request.Requestas header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded tofsspec.open. Please seefsspecandurllibfor more details, and for more examples on storage options referhere <https://pandas.pydata.org/docs/user_guide/io.html? highlight=storage_options#reading-writing-remote-files>_.
.. versionadded:: 1.2.0engine_kwargs(dict, optional) — Arbitrary keyword arguments passed to excel engine.
to_csv : Write DataFrame to a comma-separated values (csv) file.ExcelWriter : Class for writing DataFrame objects into excel sheets. read_excel : Read an Excel file into a pandas DataFrame. read_csv : Read a comma-separated values (csv) file into DataFrame. io.formats.style.Styler.to_excel : Add styles to Excel sheet.
Notes
For compatibility with :meth:~DataFrame.to_csv,
to_excel serializes lists and dicts to strings before writing.
Once a workbook has been saved it is not possible to write further data without rewriting the whole workbook.
:
, , ) P
:
, P
s :
) P ) )
:
, P )
, s :
P
to_json(path_or_buf=None, orient=None, date_format=None, double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False, compression='infer', index=None, indent=None, storage_options=None, mode='w')
Convert the object to a JSON string.
Note NaN's and None will be converted to null and datetime objects will be converted to UNIX timestamps.
path_or_buf(str, path object, file-like object, or None, default None) — String, path object (implementing os.PathLike[str]), or file-likeobject implementing a write() function. If None, the result is returned as a string.orient(str) — Indication of expected JSON string format.-
Series:
- default is 'index'
- allowed values are: {'split', 'records', 'index', 'table'}.
-
DataFrame:
- default is 'columns'
- allowed values are: {'split', 'records', 'index', 'columns', 'values', 'table'}.
-
The format of the JSON string:
- 'split' : dict like {'index' -> [index], 'columns' -> [columns], 'data' -> [values]}
- 'records' : list like [{column -> value}, ... , {column -> value}]
- 'index' : dict like {index -> {column -> value}}
- 'columns' : dict like {column -> {index -> value}}
- 'values' : just the values array
- 'table' : dict like {'schema': {schema}, 'data': {data}}
orient='records'.
-
Series:
date_format({None, 'epoch', 'iso'}) — Type of date conversion. 'epoch' = epoch milliseconds,'iso' = ISO8601. The default depends on theorient. Fororient='table', the default is 'iso'. For all other orients, the default is 'epoch'.double_precision(int, default 10) — The number of decimal places to use when encodingfloating point values. The possible maximal value is 15. Passing double_precision greater than 15 will raise a ValueError.force_ascii(bool, default True) — Force encoded string to be ASCII.date_unit(str, default 'ms' (milliseconds)) — The time unit to encode to, governs timestamp and ISO8601precision. One of 's', 'ms', 'us', 'ns' for second, millisecond, microsecond, and nanosecond respectively.default_handler(callable, default None) — Handler to call if object cannot otherwise be converted to asuitable format for JSON. Should receive a single argument which is the object to convert and return a serialisable object.lines(bool, default False) — If 'orient' is 'records' write out line-delimited json format. Willthrow ValueError if incorrect 'orient' since others are not list-like.compression(str or dict, default 'infer') — For on-the-fly compression of the output data. If 'infer' and 'path_or_buf' ispath-like, then detect compression from the following extensions: '.gz', '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2' (otherwise no compression). Set toNonefor no compression. Can also be a dict with key'method'set to one of {'zip','gzip','bz2','zstd','xz','tar'} and other key-value pairs are forwarded tozipfile.ZipFile,gzip.GzipFile,bz2.BZ2File,zstandard.ZstdCompressor,lzma.LZMAFileortarfile.TarFile, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive:compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}.
.. versionadded:: 1.5.0 Added support for.tarfiles.
.. versionchanged:: 1.4.0 Zstandard support.index(bool or None, default None) — The index is only used when 'orient' is 'split', 'index', 'column',or 'table'. Of these, 'index' and 'column' do not supportindex=False.indent(int, optional) — Length of whitespace used to indent each record.storage_options(dict, optional) — Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded tourllib.request.Requestas header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded tofsspec.open. Please seefsspecandurllibfor more details, and for more examples on storage options referhere <https://pandas.pydata.org/docs/user_guide/io.html? highlight=storage_options#reading-writing-remote-files>_.mode(str, default 'w' (writing)) — Specify the IO mode for output when supplying a path_or_buf.Accepted args are 'w' (writing) and 'a' (append) only. mode='a' is only supported when lines is True and orient is 'records'.
If path_or_buf is None, returns the resulting json format as astring. Otherwise returns None.
read_json : Convert a JSON string to pandas object.
Notes
The behavior of indent=0 varies from the stdlib, which does not
indent the output but does insert newlines. Currently, indent=0
and the default indent=None are equivalent in pandas, though this
may change in a future release.
orient='table' contains a 'pandas_version' field under 'schema'.
This stores the version of pandas used in the latest revision of the
schema.
>>> from json import loads, dumps>>> df = pd.DataFrame(
... [["a", "b"], ["c", "d"]],
... index=["row 1", "row 2"],
... columns=["col 1", "col 2"],
... )
>>> result = df.to_json(orient="split")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4) # doctest: +SKIP
{
"columns": [
"col 1",
"col 2"
],
"index": [
"row 1",
"row 2"
],
"data": [
[
"a",
"b"
],
[
"c",
"d"
]
]
}
Encoding/decoding a Dataframe using 'records' formatted JSON.
Note that index labels are not preserved with this encoding.
>>> result = df.to_json(orient="records")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4) # doctest: +SKIP
[
{
"col 1": "a",
"col 2": "b"
},
{
"col 1": "c",
"col 2": "d"
}
]
Encoding/decoding a Dataframe using 'index' formatted JSON:
>>> result = df.to_json(orient="index")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4) # doctest: +SKIP
{
"row 1": {
"col 1": "a",
"col 2": "b"
},
"row 2": {
"col 1": "c",
"col 2": "d"
}
}
Encoding/decoding a Dataframe using 'columns' formatted JSON:
>>> result = df.to_json(orient="columns")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4) # doctest: +SKIP
{
"col 1": {
"row 1": "a",
"row 2": "c"
},
"col 2": {
"row 1": "b",
"row 2": "d"
}
}
Encoding/decoding a Dataframe using 'values' formatted JSON:
>>> result = df.to_json(orient="values")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4) # doctest: +SKIP
[
[
"a",
"b"
],
[
"c",
"d"
]
]
Encoding with Table Schema:
>>> result = df.to_json(orient="table")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4) # doctest: +SKIP
{
"schema": {
"fields": [
{
"name": "index",
"type": "string"
},
{
"name": "col 1",
"type": "string"
},
{
"name": "col 2",
"type": "string"
}
],
"primaryKey": [
"index"
],
"pandas_version": "1.4.0"
},
"data": [
{
"index": "row 1",
"col 1": "a",
"col 2": "b"
},
{
"index": "row 2",
"col 1": "c",
"col 2": "d"
}
]
}
to_hdf(path_or_buf, key, mode='a', complevel=None, complib=None, append=False, format=None, index=True, min_itemsize=None, nan_rep=None, dropna=None, data_columns=None, errors='strict', encoding='UTF-8')
Write the contained data to an HDF5 file using HDFStore.
Hierarchical Data Format (HDF) is self-describing, allowing an application to interpret the structure and contents of a file with no outside information. One HDF file can hold a mix of related objects which can be accessed as a group or as individual objects.
In order to add another DataFrame or Series to an existing HDF file please use append mode and a different a key.
.. warning::
One can store a subclass of DataFrame or Series to HDF5,
but the type of the subclass is lost upon storing.
For more information see the :ref:user guide <io.hdf5>.
path_or_buf(str or pandas.HDFStore) — File path or HDFStore object.key(str) — Identifier for the group in the store.mode({'a', 'w', 'r+'}, default 'a') — Mode to open file:- 'w': write, a new file is created (an existing file with the same name would be deleted).
- 'a': append, an existing file is opened for reading and writing, and if the file does not exist it is created.
- 'r+': similar to 'a', but the file must already exist.
complevel({0-9}, default None) — Specifies a compression level for data.A value of 0 or None disables compression.complib({'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib') — Specifies the compression library to be used.These additional compressors for Blosc are supported (default if no compressor specified: 'blosc:blosclz'): {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy', 'blosc:zlib', 'blosc:zstd'}. Specifying a compression library which is not available issues a ValueError.append(bool, default False) — For Table formats, append the input data to the existing.format({'fixed', 'table', None}, default 'fixed') — Possible values:- 'fixed': Fixed format. Fast writing/reading. Not-appendable, nor searchable.
- 'table': Table format. Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data.
- If None, pd.get_option('io.hdf.default_format') is checked, followed by fallback to "fixed".
index(bool, default True) — Write DataFrame index as a column.min_itemsize(dict or int, optional) — Map column names to minimum string sizes for columns.nan_rep(Any, optional) — How to represent null values as str.Not allowed with append=True.dropna(bool, default False, optional) — Remove missing values.data_columns(list of columns or True, optional) — List of columns to create as indexed data columns for on-diskqueries, or True to use all columns. By default only the axes of the object are indexed. See :ref:Query via data columns<io.hdf5-query-data-columns>. for more information. Applicable only to format='table'.errors(str, default 'strict') — Specifies how encoding and decoding errors are to be handled.See the errors argument for :func:openfor a full list of options.
read_hdf : Read from HDF file.DataFrame.to_orc : Write a DataFrame to the binary orc format. DataFrame.to_parquet : Write a DataFrame to the binary parquet format. DataFrame.to_sql : Write to a SQL table. DataFrame.to_feather : Write out feather-format for DataFrames. DataFrame.to_csv : Write out to a csv file.
>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]},... index=['a', 'b', 'c']) # doctest: +SKIP
>>> df.to_hdf('data.h5', key='df', mode='w') # doctest: +SKIP
We can add another object to the same file:
>>> s = pd.Series([1, 2, 3, 4]) # doctest: +SKIP
>>> s.to_hdf('data.h5', key='s') # doctest: +SKIP
Reading from HDF file:
>>> pd.read_hdf('data.h5', 'df') # doctest: +SKIP
A B
a 1 4
b 2 5
c 3 6
>>> pd.read_hdf('data.h5', 's') # doctest: +SKIP
0 1
1 2
2 3
3 4
dtype: int64
to_sql(name, con, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None, method=None)
Write records stored in a DataFrame to a SQL database.
Databases supported by SQLAlchemy [1]_ are supported. Tables can be newly created, appended to, or overwritten.
name(str) — Name of SQL table.con(sqlalchemy.engine.(Engine or Connection) or sqlite3.Connection) — Using SQLAlchemy makes it possible to use any DB supported by thatlibrary. Legacy support is provided for sqlite3.Connection objects. The user is responsible for engine disposal and connection closure for the SQLAlchemy connectable. Seehere <https://docs.sqlalchemy.org/en/20/core/connections.html>_. If passing a sqlalchemy.engine.Connection which is already in a transaction, the transaction will not be committed. If passing a sqlite3.Connection, it will not be possible to roll back the record insertion.schema(str, optional) — Specify the schema (if database flavor supports this). If None, usedefault schema.if_exists({'fail', 'replace', 'append'}, default 'fail') — How to behave if the table already exists.- fail: Raise a ValueError.
- replace: Drop the table before inserting new values.
- append: Insert new values to the existing table.
index(bool, default True) — Write DataFrame index as a column. Usesindex_labelas the columnname in the table. Creates a table index for this column.index_label(str or sequence, default None) — Column label for index column(s). If None is given (default) andindexis True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex.chunksize(int, optional) — Specify the number of rows in each batch to be written at a time.By default, all rows will be written at once.dtype(dict or scalar, optional) — Specifying the datatype for columns. If a dictionary is used, thekeys should be the column names and the values should be the SQLAlchemy types or strings for the sqlite3 legacy mode. If a scalar is provided, it will be applied to all columns.method({None, 'multi', callable}, optional) — Controls the SQL insertion clause used:- None : Uses standard SQL
INSERTclause (one per row). - 'multi': Pass multiple values in a single
INSERTclause. - callable with signature
(pd_table, conn, keys, data_iter).
insert method <io.sql.method>.- None : Uses standard SQL
Number of rows affected by to_sql. None is returned if the callablepassed into method does not return an integer number of rows.
The number of returned rows affected is the sum of the rowcount
attribute of sqlite3.Cursor or SQLAlchemy connectable which may not
reflect the exact number of written rows as stipulated in the
sqlite3 <https://docs.python.org/3/library/sqlite3.html#sqlite3.Cursor.rowcount> or
SQLAlchemy <https://docs.sqlalchemy.org/en/20/core/connections.html#sqlalchemy.engine.CursorResult.rowcount>.
.. versionadded:: 1.4.0
ValueError— When the table already exists andif_existsis 'fail' (thedefault).
read_sql : Read a DataFrame from a table.
Notes
Timezone aware datetime columns will be written as
Timestamp with timezone type with SQLAlchemy if supported by the
database. Otherwise, the datetimes will be stored as timezone unaware
timestamps local to the original timezone.
Not all datastores support method="multi". Oracle, for example,
does not support multi-value insert.
.. [1] https://docs.sqlalchemy.org.. [2] https://www.python.org/dev/peps/pep-0249/
Create an in-memory SQLite database.
>>> from sqlalchemy import create_engine
>>> engine = create_engine('sqlite://', echo=False)
Create a table from scratch with 3 rows.
>>> df = pd.DataFrame({'name' : ['User 1', 'User 2', 'User 3']})
>>> df
name
0 User 1
1 User 2
2 User 3
>>> df.to_sql(name='users', con=engine)
3
>>> from sqlalchemy import text
>>> with engine.connect() as conn:
... conn.execute(text("SELECT * FROM users")).fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3')]
An sqlalchemy.engine.Connection can also be passed to con:
>>> with engine.begin() as connection:
... df1 = pd.DataFrame({'name' : ['User 4', 'User 5']})
... df1.to_sql(name='users', con=connection, if_exists='append')
2
This is allowed to support operations that require that the same DBAPI connection is used for the entire operation.
>>> df2 = pd.DataFrame({'name' : ['User 6', 'User 7']})
>>> df2.to_sql(name='users', con=engine, if_exists='append')
2
>>> with engine.connect() as conn:
... conn.execute(text("SELECT * FROM users")).fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3'),
(0, 'User 4'), (1, 'User 5'), (0, 'User 6'),
(1, 'User 7')]
Overwrite the table with just df2.
>>> df2.to_sql(name='users', con=engine, if_exists='replace',
... index_label='id')
2
>>> with engine.connect() as conn:
... conn.execute(text("SELECT * FROM users")).fetchall()
[(0, 'User 6'), (1, 'User 7')]
Use method to define a callable insertion method to do nothing
if there's a primary key conflict on a table in a PostgreSQL database.
>>> from sqlalchemy.dialects.postgresql import insert
>>> def insert_on_conflict_nothing(table, conn, keys, data_iter):
... # "a" is the primary key in "conflict_table"
... data = [dict(zip(keys, row)) for row in data_iter]
... stmt = insert(table.table).values(data).on_conflict_do_nothing(index_elements=["a"])
... result = conn.execute(stmt)
... return result.rowcount
>>> df_conflict.to_sql(name="conflict_table", con=conn, if_exists="append", method=insert_on_conflict_nothing) # doctest: +SKIP
0
For MySQL, a callable to update columns b and c if there's a conflict
on a primary key.
>>> from sqlalchemy.dialects.mysql import insert
>>> def insert_on_conflict_update(table, conn, keys, data_iter):
... # update columns "b" and "c" on primary key conflict
... data = [dict(zip(keys, row)) for row in data_iter]
... stmt = (
... insert(table.table)
... .values(data)
... )
... stmt = stmt.on_duplicate_key_update(b=stmt.inserted.b, c=stmt.inserted.c)
... result = conn.execute(stmt)
... return result.rowcount
>>> df_conflict.to_sql(name="conflict_table", con=conn, if_exists="append", method=insert_on_conflict_update) # doctest: +SKIP
2
Specify the dtype (especially useful for integers with missing values). Notice that while pandas is forced to store the data as floating point, the database supports nullable integers. When fetching the data with Python, we get back integer scalars.
>>> df = pd.DataFrame({"A": [1, None, 2]})
>>> df
A
0 1.0
1 NaN
2 2.0
>>> from sqlalchemy.types import Integer
>>> df.to_sql(name='integers', con=engine, index=False,
... dtype={"A": Integer()})
3
>>> with engine.connect() as conn:
... conn.execute(text("SELECT * FROM integers")).fetchall()
[(1,), (None,), (2,)]
to_pickle(path, compression='infer', protocol=5, storage_options=None)
Pickle (serialize) object to file.
path(str, path object, or file-like object) — String, path object (implementingos.PathLike[str]), or file-likeobject implementing a binarywrite()function. File path where the pickled object will be stored.compression(str or dict, default 'infer') — For on-the-fly compression of the output data. If 'infer' and 'path' ispath-like, then detect compression from the following extensions: '.gz', '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2' (otherwise no compression). Set toNonefor no compression. Can also be a dict with key'method'set to one of {'zip','gzip','bz2','zstd','xz','tar'} and other key-value pairs are forwarded tozipfile.ZipFile,gzip.GzipFile,bz2.BZ2File,zstandard.ZstdCompressor,lzma.LZMAFileortarfile.TarFile, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive:compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}.
.. versionadded:: 1.5.0 Added support for.tarfiles.protocol(int) — Int which indicates which protocol should be used by the pickler,default HIGHEST_PROTOCOL (see [1]_ paragraph 12.1.2). The possible values are 0, 1, 2, 3, 4, 5. A negative value for the protocol parameter is equivalent to setting its value to HIGHEST_PROTOCOL.
.. [1] https://docs.python.org/3/library/pickle.html.storage_options(dict, optional) — Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded tourllib.request.Requestas header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded tofsspec.open. Please seefsspecandurllibfor more details, and for more examples on storage options referhere <https://pandas.pydata.org/docs/user_guide/io.html? highlight=storage_options#reading-writing-remote-files>_.
read_pickle : Load pickled pandas object (or any object) from file.DataFrame.to_hdf : Write DataFrame to an HDF5 file. DataFrame.to_sql : Write DataFrame to a SQL database. DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
>>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)}) # doctest: +SKIP>>> original_df # doctest: +SKIP
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
>>> original_df.to_pickle("./dummy.pkl") # doctest: +SKIP
>>> unpickled_df = pd.read_pickle("./dummy.pkl") # doctest: +SKIP
>>> unpickled_df # doctest: +SKIP
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
to_clipboard(excel=True, sep=None, **kwargs)
Copy object to the system clipboard.
Write a text representation of object to the system clipboard. This can be pasted into Excel, for example.
excel(bool, default True) — Produce output in a csv format for easy pasting into excel.- True, use the provided separator for csv pasting.
- False, write a string representation of the object to the clipboard.
sep(str, default ``'\t'``) — Field delimiter.**kwargs— These parameters will be passed to DataFrame.to_csv.
DataFrame.to_csv : Write a DataFrame to a comma-separated values (csv) file. read_clipboard : Read text from clipboard and pass to read_csv.
Notes
Requirements for your platform.
- Linux :
xclip, orxsel(withPyQt4modules) - Windows : none
- macOS : none
This method uses the processes developed for the package pyperclip. A
solution to render any output string format is given in the examples.
Copy the contents of a DataFrame to the clipboard.
>>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])
>>> df.to_clipboard(sep=',') # doctest: +SKIP
... # Wrote the following to the system clipboard:
... # ,A,B,C
... # 0,1,2,3
... # 1,4,5,6
We can omit the index by passing the keyword index and setting
it to false.
>>> df.to_clipboard(sep=',', index=False) # doctest: +SKIP
... # Wrote the following to the system clipboard:
... # A,B,C
... # 1,2,3
... # 4,5,6
Using the original pyperclip package for any string output format.
.. code-block:: python
import pyperclip html = df.style.to_html() pyperclip.copy(html)
to_xarray()
Return an xarray object from the pandas object.
Data in the pandas structure converted to Dataset if the object isa DataFrame, or a DataArray if the object is a Series.
DataFrame.to_hdf : Write DataFrame to an HDF5 file.DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
Notes
See the xarray docs <https://xarray.pydata.org/en/stable/>__
>>> df = pd.DataFrame([('falcon', 'bird', 389.0, 2),... ('parrot', 'bird', 24.0, 2),
... ('lion', 'mammal', 80.5, 4),
... ('monkey', 'mammal', np.nan, 4)],
... columns=['name', 'class', 'max_speed',
... 'num_legs'])
>>> df
name class max_speed num_legs
0 falcon bird 389.0 2
1 parrot bird 24.0 2
2 lion mammal 80.5 4
3 monkey mammal NaN 4
>>> df.to_xarray() # doctest: +SKIP
<xarray.Dataset>
Dimensions: (index: 4)
Coordinates:
* index (index) int64 32B 0 1 2 3
Data variables:
name (index) object 32B 'falcon' 'parrot' 'lion' 'monkey'
class (index) object 32B 'bird' 'bird' 'mammal' 'mammal'
max_speed (index) float64 32B 389.0 24.0 80.5 nan
num_legs (index) int64 32B 2 2 4 4
>>> df['max_speed'].to_xarray() # doctest: +SKIP
<xarray.DataArray 'max_speed' (index: 4)>
array([389. , 24. , 80.5, nan])
Coordinates:
* index (index) int64 0 1 2 3
>>> dates = pd.to_datetime(['2018-01-01', '2018-01-01',
... '2018-01-02', '2018-01-02'])
>>> df_multiindex = pd.DataFrame({'date': dates,
... 'animal': ['falcon', 'parrot',
... 'falcon', 'parrot'],
... 'speed': [350, 18, 361, 15]})
>>> df_multiindex = df_multiindex.set_index(['date', 'animal'])
>>> df_multiindex
speed
date animal
2018-01-01 falcon 350
parrot 18
2018-01-02 falcon 361
parrot 15
>>> df_multiindex.to_xarray() # doctest: +SKIP
<xarray.Dataset>
Dimensions: (date: 2, animal: 2)
Coordinates:
* date (date) datetime64[ns] 2018-01-01 2018-01-02
* animal (animal) object 'falcon' 'parrot'
Data variables:
speed (date, animal) int64 350 18 361 15
to_latex(buf=None, columns=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, bold_rows=False, column_format=None, longtable=None, escape=None, encoding=None, decimal='.', multicolumn=None, multicolumn_format=None, multirow=None, caption=None, label=None, position=None)
Render object to a LaTeX tabular, longtable, or nested table.
Requires \usepackage{{booktabs}}. The output can be copy/pasted
into a main LaTeX document or read from an external file
with \input{{table.tex}}.
.. versionchanged:: 2.0.0 Refactored to use the Styler implementation via jinja2 templating.
buf(str, Path or StringIO-like, optional, default None) — Buffer to write to. If None, the output is returned as a string.columns(list of label, optional) — The subset of columns to write. Writes all columns by default.header(bool or list of str, default True) — Write out the column names. If a list of strings is given,it is assumed to be aliases for the column names.index(bool, default True) — Write row names (index).na_rep(str, default 'NaN') — Missing data representation.formatters(list of functions or dict of {{str: function}}, optional) — Formatter functions to apply to columns' elements by position orname. The result of each function must be a unicode string. List must be of length equal to the number of columns.float_format(one-parameter function or str, optional, default None) — Formatter for floating point numbers. For examplefloat_format="%.2f"andfloat_format="{{:0.2f}}".formatwill both result in 0.1234 being formatted as 0.12.sparsify(bool, optional) — Set to False for a DataFrame with a hierarchical index to printevery multiindex key at each row. By default, the value will be read from the config module.index_names(bool, default True) — Prints the names of the indexes.bold_rows(bool, default False) — Make the row labels bold in the output.column_format(str, optional) — The columns format as specified inLaTeX table format<https://en.wikibooks.org/wiki/LaTeX/Tables>__ e.g. 'rcl' for 3 columns. By default, 'l' will be used for all columns except columns of numbers, which default to 'r'.longtable(bool, optional) — Use a longtable environment instead of tabular. Requiresadding a \usepackage{{longtable}} to your LaTeX preamble. By default, the value will be read from the pandas config module, and set toTrueif the optionstyler.latex.environmentis"longtable".
.. versionchanged:: 2.0.0 The pandas option affecting this argument has changed.escape(bool, optional) — By default, the value will be read from the pandas configmodule and set toTrueif the optionstyler.format.escapeis"latex". When set to False prevents from escaping latex special characters in column names.
.. versionchanged:: 2.0.0 The pandas option affecting this argument has changed, as has the default value toFalse.encoding(str, optional) — A string representing the encoding to use in the output file,defaults to 'utf-8'.decimal(str, default '.') — Character recognized as decimal separator, e.g. ',' in Europe.multicolumn(bool, default True) — Use \multicolumn to enhance MultiIndex columns.The default will be read from the config module, and is set as the optionstyler.sparse.columns.
.. versionchanged:: 2.0.0 The pandas option affecting this argument has changed.multicolumn_format(str, default 'r') — The alignment for multicolumns, similar tocolumn_formatThe default will be read from the config module, and is set as the optionstyler.latex.multicol_align.
.. versionchanged:: 2.0.0 The pandas option affecting this argument has changed, as has the default value to "r".multirow(bool, default True) — Use \multirow to enhance MultiIndex rows. Requires adding a\usepackage{{multirow}} to your LaTeX preamble. Will print centered labels (instead of top-aligned) across the contained rows, separating groups via clines. The default will be read from the pandas config module, and is set as the optionstyler.sparse.index.
.. versionchanged:: 2.0.0 The pandas option affecting this argument has changed, as has the default value toTrue.caption(str or tuple, optional) — Tuple (full_caption, short_caption),which results in\caption[short_caption]{{full_caption}}; if a single string is passed, no short caption will be set.label(str, optional) — The LaTeX label to be placed inside\label{{}}in the output.This is used with\ref{{}}in the main.texfile.position(str, optional) — The LaTeX positional argument for tables, to be placed after\begin{{}}in the output.
If buf is None, returns the result as a string. Otherwise returns None.
io.formats.style.Styler.to_latex : Render a DataFrame to LaTeX with conditional formatting. DataFrame.to_string : Render a DataFrame to a console-friendly tabular output. DataFrame.to_html : Render a DataFrame as an HTML table.
Notes
As of v2.0.0 this method has changed to use the Styler implementation as
part of :meth:.Styler.to_latex via jinja2 templating. This means
that jinja2 is a requirement, and needs to be installed, for this method
to function. It is advised that users switch to using Styler, since that
implementation is more frequently updated and contains much more
flexibility with the output.
Convert a general DataFrame to LaTeX with formatting:
>>> df = pd.DataFrame(dict(name=['Raphael', 'Donatello'],
... age=[26, 45],
... height=[181.23, 177.65]))
>>> print(df.to_latex(index=False,
... formatters={"name": str.upper},
... float_format="{:.1f}".format,
... )) # doctest: +SKIP
\begin{tabular}{lrr}
\toprule
name & age & height \\
\midrule
RAPHAEL & 26 & 181.2 \\
DONATELLO & 45 & 177.7 \\
\bottomrule
\end{tabular}
to_csv(path_or_buf=None, sep=',', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, mode='w', encoding=None, compression='infer', quoting=None, quotechar='"', lineterminator=None, chunksize=None, date_format=None, doublequote=True, escapechar=None, decimal='.', errors='strict', storage_options=None)
Write object to a comma-separated values (csv) file.
path_or_buf(str, path object, file-like object, or None, default None) — String, path object (implementing os.PathLike[str]), or file-likeobject implementing a write() function. If None, the result is returned as a string. If a non-binary file object is passed, it should be opened withnewline='', disabling universal newlines. If a binary file object is passed,modemight need to contain a'b'.sep(str, default ',') — String of length 1. Field delimiter for the output file.na_rep(str, default '') — Missing data representation.float_format(str, Callable, default None) — Format string for floating point numbers. If a Callable is given, it takesprecedence over other numeric formatting parameters, like decimal.columns(sequence, optional) — Columns to write.header(bool or list of str, default True) — Write out the column names. If a list of strings is given it isassumed to be aliases for the column names.index(bool, default True) — Write row names (index).index_label(str or sequence, or False, default None) — Column label for index column(s) if desired. If None is given, andheaderandindexare True, then the index names are used. A sequence should be given if the object uses MultiIndex. If False do not print fields for index names. Use index_label=False for easier importing in R.mode({'w', 'x', 'a'}, default 'w') — Forwarded to eitheropen(mode=)orfsspec.open(mode=)to controlthe file opening. Typical values include:
- 'w', truncate the file first.
- 'x', exclusive creation, failing if the file already exists.
- 'a', append to the end of file if it exists.
encoding(str, optional) — A string representing the encoding to use in the output file,defaults to 'utf-8'.encodingis not supported ifpath_or_bufis a non-binary file object.compression(str or dict, default 'infer') — For on-the-fly compression of the output data. If 'infer' and 'path_or_buf' ispath-like, then detect compression from the following extensions: '.gz', '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2' (otherwise no compression). Set toNonefor no compression. Can also be a dict with key'method'set to one of {'zip','gzip','bz2','zstd','xz','tar'} and other key-value pairs are forwarded tozipfile.ZipFile,gzip.GzipFile,bz2.BZ2File,zstandard.ZstdCompressor,lzma.LZMAFileortarfile.TarFile, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive:compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}.
.. versionadded:: 1.5.0 Added support for.tarfiles.
May be a dict with key 'method' as compression mode and other entries as additional compression options if compression mode is 'zip'.
Passing compression options as keys in dict is supported for compression modes 'gzip', 'bz2', 'zstd', and 'zip'.quoting(optional constant from csv module) — Defaults to csv.QUOTE_MINIMAL. If you have set afloat_formatthen floats are converted to strings and thus csv.QUOTE_NONNUMERIC will treat them as non-numeric.quotechar(str, default '\"') — String of length 1. Character used to quote fields.lineterminator(str, optional) — The newline character or character sequence to use in the outputfile. Defaults toos.linesep, which depends on the OS in which this method is called ('\n' for linux, '\r\n' for Windows, i.e.).
.. versionchanged:: 1.5.0
Previously was line_terminator, changed for consistency with read_csv and the standard library 'csv' module.chunksize(int or None) — Rows to write at a time.date_format(str, default None) — Format string for datetime objects.doublequote(bool, default True) — Control quoting ofquotecharinside a field.escapechar(str, default None) — String of length 1. Character used to escapesepandquotecharwhen appropriate.decimal(str, default '.') — Character recognized as decimal separator. E.g. use ',' forEuropean data.errors(str, default 'strict') — Specifies how encoding and decoding errors are to be handled.See the errors argument for :func:openfor a full list of options.storage_options(dict, optional) — Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded tourllib.request.Requestas header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded tofsspec.open. Please seefsspecandurllibfor more details, and for more examples on storage options referhere <https://pandas.pydata.org/docs/user_guide/io.html? highlight=storage_options#reading-writing-remote-files>_.
If path_or_buf is None, returns the resulting csv format as astring. Otherwise returns None.
read_csv : Load a CSV file into a DataFrame.to_excel : Write DataFrame to an Excel file.
Create 'out.csv' containing 'df' without indices
>>> df = pd.DataFrame({'name': ['Raphael', 'Donatello'],
... 'mask': ['red', 'purple'],
... 'weapon': ['sai', 'bo staff']})
>>> df.to_csv('out.csv', index=False) # doctest: +SKIP
Create 'out.zip' containing 'out.csv'
>>> df.to_csv(index=False)
'name,mask,weapon\nRaphael,red,sai\nDonatello,purple,bo staff\n'
>>> compression_opts = dict(method='zip',
... archive_name='out.csv') # doctest: +SKIP
>>> df.to_csv('out.zip', index=False,
... compression=compression_opts) # doctest: +SKIP
To write a csv file to a new folder or nested folder you will first need to create it using either Pathlib or os:
>>> from pathlib import Path # doctest: +SKIP
>>> filepath = Path('folder/subfolder/out.csv') # doctest: +SKIP
>>> filepath.parent.mkdir(parents=True, exist_ok=True) # doctest: +SKIP
>>> df.to_csv(filepath) # doctest: +SKIP
>>> import os # doctest: +SKIP
>>> os.makedirs('folder/subfolder', exist_ok=True) # doctest: +SKIP
>>> df.to_csv('folder/subfolder/out.csv') # doctest: +SKIP
take(indices, axis=0, **kwargs)
Return the elements in the given positional indices along an axis.
This means that we are not indexing according to actual values in the index attribute of the object. We are indexing according to the actual position of the element in the object.
indices(array-like) — An array of ints indicating which positions to take.axis({0 or 'index', 1 or 'columns', None}, default 0) — The axis on which to select elements.0means that we areselecting rows,1means that we are selecting columns. ForSeriesthis parameter is unused and defaults to 0.**kwargs— For compatibility with :meth:numpy.take. Has no effect on theoutput.
An array-like containing the elements taken from the object.
DataFrame.loc : Select a subset of a DataFrame by labels.DataFrame.iloc : Select a subset of a DataFrame by positions. numpy.take : Take elements from an array along an axis.
>>> df = pd.DataFrame([('falcon', 'bird', 389.0),... ('parrot', 'bird', 24.0),
... ('lion', 'mammal', 80.5),
... ('monkey', 'mammal', np.nan)],
... columns=['name', 'class', 'max_speed'],
... index=[0, 2, 3, 1])
>>> df
name class max_speed
0 falcon bird 389.0
2 parrot bird 24.0
3 lion mammal 80.5
1 monkey mammal NaN
Take elements at positions 0 and 3 along the axis 0 (default).
Note how the actual indices selected (0 and 1) do not correspond to our selected indices 0 and 3. That's because we are selecting the 0th and 3rd rows, not rows whose indices equal 0 and 3.
>>> df.take([0, 3])
name class max_speed
0 falcon bird 389.0
1 monkey mammal NaN
Take elements at indices 1 and 2 along the axis 1 (column selection).
>>> df.take([1, 2], axis=1)
class max_speed
0 bird 389.0
2 bird 24.0
3 mammal 80.5
1 mammal NaN
We may take elements using negative integers for positive indices, starting from the end of the object, just like with Python lists.
>>> df.take([-1, -2])
name class max_speed
1 monkey mammal NaN
3 lion mammal 80.5
xs(key, axis=0, level=None, drop_level=True)
Return cross-section from the Series/DataFrame.
This method takes a key argument to select data at a particular
level of a MultiIndex.
key(label or tuple of label) — Label contained in the index, or partially in a MultiIndex.axis({0 or 'index', 1 or 'columns'}, default 0) — Axis to retrieve cross-section on.level(object, defaults to first n levels (n=1 or len(key))) — In case of a key partially contained in a MultiIndex, indicatewhich levels are used. Levels can be referred by label or position.drop_level(bool, default True) — If False, returns object with same levels as self.
Cross-section from the original Series or DataFramecorresponding to the selected index levels.
DataFrame.loc : Access a group of rows and columns by label(s) or a boolean array. DataFrame.iloc : Purely integer-location based indexing for selection by position.
Notes
xs can not be used to set values.
MultiIndex Slicers is a generic way to get/set values on
any level or levels.
It is a superset of xs functionality, see
:ref:MultiIndex Slicers <advanced.mi_slicers>.
>>> d = {'num_legs': [4, 4, 2, 2],... 'num_wings': [0, 0, 2, 2],
... 'class': ['mammal', 'mammal', 'mammal', 'bird'],
... 'animal': ['cat', 'dog', 'bat', 'penguin'],
... 'locomotion': ['walks', 'walks', 'flies', 'walks']}
>>> df = pd.DataFrame(data=d)
>>> df = df.set_index(['class', 'animal', 'locomotion'])
>>> df
num_legs num_wings
class animal locomotion
mammal cat walks 4 0
dog walks 4 0
bat flies 2 2
bird penguin walks 2 2
Get values at specified index
>>> df.xs('mammal')
num_legs num_wings
animal locomotion
cat walks 4 0
dog walks 4 0
bat flies 2 2
Get values at several indexes
>>> df.xs(('mammal', 'dog', 'walks'))
num_legs 4
num_wings 0
Name: (mammal, dog, walks), dtype: int64
Get values at specified index and level
>>> df.xs('cat', level=1)
num_legs num_wings
class locomotion
mammal walks 4 0
Get values at several indexes and levels
>>> df.xs(('bird', 'walks'),
... level=[0, 'locomotion'])
num_legs num_wings
animal
penguin 2 2
Get values at specified column and axis
>>> df.xs('num_wings', axis=1)
class animal locomotion
mammal cat walks 0
dog walks 0
bat flies 2
bird penguin walks 2
Name: num_wings, dtype: int64
__delitem__(key)
Delete item
get(key, default=None)
Get item from object for given key (ex: DataFrame column).
Returns default value if not found.
>>> df = pd.DataFrame(... [
... [24.3, 75.7, "high"],
... [31, 87.8, "high"],
... [22, 71.6, "medium"],
... [35, 95, "medium"],
... ],
... columns=["temp_celsius", "temp_fahrenheit", "windspeed"],
... index=pd.date_range(start="2014-02-12", end="2014-02-15", freq="D"),
... )
>>> df
temp_celsius temp_fahrenheit windspeed
2014-02-12 24.3 75.7 high
2014-02-13 31.0 87.8 high
2014-02-14 22.0 71.6 medium
2014-02-15 35.0 95.0 medium
>>> df.get(["temp_celsius", "windspeed"])
temp_celsius windspeed
2014-02-12 24.3 high
2014-02-13 31.0 high
2014-02-14 22.0 medium
2014-02-15 35.0 medium
>>> ser = df['windspeed']
>>> ser.get('2014-02-13')
'high'
If the key isn't found, the default value will be used.
>>> df.get(["temp_celsius", "temp_kelvin"], default="default_value")
'default_value'
>>> ser.get('2014-02-10', '[unknown]')
'[unknown]'
reindex_like(other, method=None, copy=None, limit=None, tolerance=None)
Return an object with matching indices as other object.
Conform the object to the same index on all axes. Optional filling logic, placing NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False.
other(Object of the same data type) — Its row and column indices are used to define the new indicesof this object.method({None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}) — Method to use for filling holes in reindexed DataFrame.Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index.
- None (default): don't fill gaps
- pad / ffill: propagate last valid observation forward to next valid
- backfill / bfill: use next valid observation to fill gap
- nearest: use nearest valid observations to fill gap.
copy(bool, default True) — Return a new object, even if the passed indexes are the same.
.. note:: Thecopykeyword will change behavior in pandas 3.0.Copy-on-Write <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>__ will be enabled by default, which means that all methods with acopykeyword will use a lazy copy mechanism to defer the copy and ignore thecopykeyword. Thecopykeyword will be removed in a future version of pandas.
You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True``limit(int, default None) — Maximum number of consecutive labels to fill for inexact matches.tolerance(optional) — Maximum distance between original and new labels for inexactmatches. The values of the index at the matching locations must satisfy the equationabs(index[indexer] - target) <= tolerance.
Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index's type.
Same type as caller, but with changed indices on each axis.
DataFrame.set_index : Set row labels.DataFrame.reset_index : Remove row labels or move them to new columns. DataFrame.reindex : Change to new indices or expand indices.
Notes
Same as calling
.reindex(index=other.index, columns=other.columns,...).
>>> df1 = pd.DataFrame([[24.3, 75.7, 'high'],... [31, 87.8, 'high'],
... [22, 71.6, 'medium'],
... [35, 95, 'medium']],
... columns=['temp_celsius', 'temp_fahrenheit',
... 'windspeed'],
... index=pd.date_range(start='2014-02-12',
... end='2014-02-15', freq='D'))
>>> df1
temp_celsius temp_fahrenheit windspeed
2014-02-12 24.3 75.7 high
2014-02-13 31.0 87.8 high
2014-02-14 22.0 71.6 medium
2014-02-15 35.0 95.0 medium
>>> df2 = pd.DataFrame([[28, 'low'],
... [30, 'low'],
... [35.1, 'medium']],
... columns=['temp_celsius', 'windspeed'],
... index=pd.DatetimeIndex(['2014-02-12', '2014-02-13',
... '2014-02-15']))
>>> df2
temp_celsius windspeed
2014-02-12 28.0 low
2014-02-13 30.0 low
2014-02-15 35.1 medium
>>> df2.reindex_like(df1)
temp_celsius temp_fahrenheit windspeed
2014-02-12 28.0 NaN low
2014-02-13 30.0 NaN low
2014-02-14 NaN NaN NaN
2014-02-15 35.1 NaN medium
add_prefix(prefix, axis=None)
Prefix labels with string prefix.
For Series, the row labels are prefixed. For DataFrame, the column labels are prefixed.
prefix(str) — The string to add before each label.axis({0 or 'index', 1 or 'columns', None}, default None) — Axis to add prefix on
.. versionadded:: 2.0.0
New Series or DataFrame with updated labels.
Series.add_suffix: Suffix row labels with string suffix.DataFrame.add_suffix: Suffix column labels with string suffix.
>>> s = pd.Series([1, 2, 3, 4])>>> s
0 1
1 2
2 3
3 4
dtype: int64
>>> s.add_prefix('item_')
item_0 1
item_1 2
item_2 3
item_3 4
dtype: int64
>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
>>> df
A B
0 1 3
1 2 4
2 3 5
3 4 6
>>> df.add_prefix('col_')
col_A col_B
0 1 3
1 2 4
2 3 5
3 4 6
add_suffix(suffix, axis=None)
Suffix labels with string suffix.
For Series, the row labels are suffixed. For DataFrame, the column labels are suffixed.
suffix(str) — The string to add after each label.axis({0 or 'index', 1 or 'columns', None}, default None) — Axis to add suffix on
.. versionadded:: 2.0.0
New Series or DataFrame with updated labels.
Series.add_prefix: Prefix row labels with string prefix.DataFrame.add_prefix: Prefix column labels with string prefix.
>>> s = pd.Series([1, 2, 3, 4])>>> s
0 1
1 2
2 3
3 4
dtype: int64
>>> s.add_suffix('_item')
0_item 1
1_item 2
2_item 3
3_item 4
dtype: int64
>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
>>> df
A B
0 1 3
1 2 4
2 3 5
3 4 6
>>> df.add_suffix('_col')
A_col B_col
0 1 3
1 2 4
2 3 5
3 4 6
filter(items=None, like=None, regex=None, axis=None)
Subset the dataframe rows or columns according to the specified index labels.
Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index.
items(list-like) — Keep labels from axis which are in items.like(str) — Keep labels from axis for which "like in label == True".regex(str (regular expression)) — Keep labels from axis for which re.search(regex, label) == True.axis({0 or 'index', 1 or 'columns', None}, default None) — The axis to filter on, expressed either as an index (int)or axis name (str). By default this is the info axis, 'columns' for DataFrame. ForSeriesthis parameter is unused and defaults toNone.
DataFrame.loc : Access a group of rows and columns by label(s) or a boolean array.
Notes
The items, like, and regex parameters are
enforced to be mutually exclusive.
axis defaults to the info axis that is used when indexing
with [].
>>> df = pd.DataFrame(np.array(([1, 2, 3], [4, 5, 6])),... index=['mouse', 'rabbit'],
... columns=['one', 'two', 'three'])
>>> df
one two three
mouse 1 2 3
rabbit 4 5 6
>>> # select columns by name
>>> df.filter(items=['one', 'three'])
one three
mouse 1 3
rabbit 4 6
>>> # select columns by regular expression
>>> df.filter(regex='e$', axis=1)
one three
mouse 1 3
rabbit 4 6
>>> # select rows containing 'bbi'
>>> df.filter(like='bbi', axis=0)
one two three
rabbit 4 5 6
head(n=5)
Return the first n rows.
This function returns the first n rows for the object based
on position. It is useful for quickly testing if your object
has the right type of data in it.
For negative values of n, this function returns all rows except
the last |n| rows, equivalent to df[:n].
If n is larger than the number of rows, this function returns all rows.
n(int, default 5) — Number of rows to select.
The first n rows of the caller object.
DataFrame.tail: Returns the last n rows.
>>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',... 'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
animal
0 alligator
1 bee
2 falcon
3 lion
4 monkey
5 parrot
6 shark
7 whale
8 zebra
Viewing the first 5 lines
>>> df.head()
animal
0 alligator
1 bee
2 falcon
3 lion
4 monkey
Viewing the first n lines (three in this case)
>>> df.head(3)
animal
0 alligator
1 bee
2 falcon
For negative values of n
>>> df.head(-3)
animal
0 alligator
1 bee
2 falcon
3 lion
4 monkey
5 parrot
tail(n=5)
Return the last n rows.
This function returns last n rows from the object based on
position. It is useful for quickly verifying data, for example,
after sorting or appending rows.
For negative values of n, this function returns all rows except
the first |n| rows, equivalent to df[|n|:].
If n is larger than the number of rows, this function returns all rows.
n(int, default 5) — Number of rows to select.
The last n rows of the caller object.
DataFrame.head : The first n rows of the caller object.
>>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',... 'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
animal
0 alligator
1 bee
2 falcon
3 lion
4 monkey
5 parrot
6 shark
7 whale
8 zebra
Viewing the last 5 lines
>>> df.tail()
animal
4 monkey
5 parrot
6 shark
7 whale
8 zebra
Viewing the last n lines (three in this case)
>>> df.tail(3)
animal
6 shark
7 whale
8 zebra
For negative values of n
>>> df.tail(-3)
animal
3 lion
4 monkey
5 parrot
6 shark
7 whale
8 zebra
sample(n=None, frac=None, replace=False, weights=None, random_state=None, axis=None, ignore_index=False)
Return a random sample of items from an axis of object.
You can use random_state for reproducibility.
n(int, optional) — Number of items from axis to return. Cannot be used withfrac.Default = 1 iffrac= None.frac(float, optional) — Fraction of axis items to return. Cannot be used withn.replace(bool, default False) — Allow or disallow sampling of the same row more than once.weights(str or ndarray-like, optional) — Default 'None' results in equal probability weighting.If passed a Series, will align with target object on index. Index values in weights not found in sampled object will be ignored and index values in sampled object not in weights will be assigned weights of zero. If called on a DataFrame, will accept the name of a column when axis = 0. Unless weights are a Series, weights must be same length as axis being sampled. If weights do not sum to 1, they will be normalized to sum to 1. Missing values in the weights column will be treated as zero. Infinite values not allowed.random_state(int, array-like, BitGenerator, np.random.RandomState, np.random.Generator, optional) — If int, array-like, or BitGenerator, seed for random number generator.If np.random.RandomState or np.random.Generator, use as given.
.. versionchanged:: 1.4.0
np.random.Generator objects now acceptedaxis({0 or 'index', 1 or 'columns', None}, default None) — Axis to sample. Accepts axis number or name. Default is stat axisfor given data type. ForSeriesthis parameter is unused and defaults toNone.ignore_index(bool, default False) — If True, the resulting index will be labeled 0, 1, …, n - 1.
.. versionadded:: 1.3.0
A new object of same type as caller containing n items randomlysampled from the caller object.
DataFrameGroupBy.sample: Generates random samples from each group of a DataFrame object. SeriesGroupBy.sample: Generates random samples from each group of a Series object. numpy.random.choice: Generates a random sample from a given 1-D numpy array.
Notes
If frac > 1, replacement should be set to True.
>>> df = pd.DataFrame({'num_legs': [2, 4, 8, 0],... 'num_wings': [2, 0, 0, 0],
... 'num_specimen_seen': [10, 2, 1, 8]},
... index=['falcon', 'dog', 'spider', 'fish'])
>>> df
num_legs num_wings num_specimen_seen
falcon 2 2 10
dog 4 0 2
spider 8 0 1
fish 0 0 8
Extract 3 random elements from the Series df['num_legs']:
Note that we use random_state to ensure the reproducibility of
the examples.
>>> df['num_legs'].sample(n=3, random_state=1)
fish 0
spider 8
falcon 2
Name: num_legs, dtype: int64
A random 50% sample of the DataFrame with replacement:
>>> df.sample(frac=0.5, replace=True, random_state=1)
num_legs num_wings num_specimen_seen
dog 4 0 2
fish 0 0 8
An upsample sample of the DataFrame with replacement:
Note that replace parameter has to be True for frac parameter > 1.
>>> df.sample(frac=2, replace=True, random_state=1)
num_legs num_wings num_specimen_seen
dog 4 0 2
fish 0 0 8
falcon 2 2 10
falcon 2 2 10
fish 0 0 8
dog 4 0 2
fish 0 0 8
dog 4 0 2
Using a DataFrame column as weights. Rows with larger value in the
num_specimen_seen column are more likely to be sampled.
>>> df.sample(n=2, weights='num_specimen_seen', random_state=1)
num_legs num_wings num_specimen_seen
falcon 2 2 10
fish 0 0 8
pipe(func, *args, **kwargs)
Apply chainable functions that expect Series or DataFrames.
func(function) — Function to apply to the Series/DataFrame.args, andkwargsare passed intofunc. Alternatively a(callable, data_keyword)tuple wheredata_keywordis a string indicating the keyword ofcallablethat expects the Series/DataFrame.*args(iterable, optional) — Positional arguments passed intofunc.**kwargs(mapping, optional) — A dictionary of keyword arguments passed intofunc.
DataFrame.apply : Apply a function along input axis of DataFrame.DataFrame.map : Apply a function elementwise on a whole DataFrame.
Series.map : Apply a mapping correspondence on a
:class:~pandas.Series.
Notes
Use .pipe when chaining together functions that expect
Series, DataFrames or GroupBy objects.
Constructing a income DataFrame from a dictionary.
>>> data = [[8000, 1000], [9500, np.nan], [5000, 2000]]
>>> df = pd.DataFrame(data, columns=['Salary', 'Others'])
>>> df
Salary Others
0 8000 1000.0
1 9500 NaN
2 5000 2000.0
Functions that perform tax reductions on an income DataFrame.
>>> def subtract_federal_tax(df):
... return df * 0.9
>>> def subtract_state_tax(df, rate):
... return df * (1 - rate)
>>> def subtract_national_insurance(df, rate, rate_increase):
... new_rate = rate + rate_increase
... return df * (1 - new_rate)
Instead of writing
>>> subtract_national_insurance(
... subtract_state_tax(subtract_federal_tax(df), rate=0.12),
... rate=0.05,
... rate_increase=0.02) # doctest: +SKIP
You can write
>>> (
... df.pipe(subtract_federal_tax)
... .pipe(subtract_state_tax, rate=0.12)
... .pipe(subtract_national_insurance, rate=0.05, rate_increase=0.02)
... )
Salary Others
0 5892.48 736.56
1 6997.32 NaN
2 3682.80 1473.12
If you have a function that takes the data as (say) the second
argument, pass a tuple indicating which keyword expects the
data. For example, suppose national_insurance takes its data as df
in the second argument:
>>> def subtract_national_insurance(rate, df, rate_increase):
... new_rate = rate + rate_increase
... return df * (1 - new_rate)
>>> (
... df.pipe(subtract_federal_tax)
... .pipe(subtract_state_tax, rate=0.12)
... .pipe(
... (subtract_national_insurance, 'df'),
... rate=0.05,
... rate_increase=0.02
... )
... )
Salary Others
0 5892.48 736.56
1 6997.32 NaN
2 3682.80 1473.12
__finalize__(other, method=None, **kwargs)
Propagate metadata from other to self.
other(the object from which to get the attributes that we are going) — to propagatemethod(str, optional) — A passed method name providing context on where__finalize__was called.
.. warning::
The value passed asmethodare not currently considered stable across pandas releases.
__getattr__(name)
After regular attribute access, try looking up the nameThis allows simpler access to columns for interactive use.
__setattr__(name, value)
After regular attribute access, try setting the nameThis allows simpler access to columns for interactive use.
astype(dtype, copy=None, errors='raise')
Cast a pandas object to a specified dtype dtype.
dtype(str, data type, Series or Mapping of column name -> data type) — Use a str, numpy.dtype, pandas.ExtensionDtype or Python type tocast entire pandas object to the same type. Alternatively, use a mapping, e.g. {col: dtype, ...}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame's columns to column-specific types.copy(bool, default True) — Return a copy whencopy=True(be very careful settingcopy=Falseas changes to values then may propagate to other pandas objects).
.. note:: Thecopykeyword will change behavior in pandas 3.0.Copy-on-Write <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>__ will be enabled by default, which means that all methods with acopykeyword will use a lazy copy mechanism to defer the copy and ignore thecopykeyword. Thecopykeyword will be removed in a future version of pandas.
You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True``errors({'raise', 'ignore'}, default 'raise') — Control raising of exceptions on invalid data for provided dtype.raise: allow exceptions to be raisedignore: suppress exceptions. On error return original object.
to_datetime : Convert argument to datetime.to_timedelta : Convert argument to timedelta. to_numeric : Convert argument to a numeric type. numpy.ndarray.astype : Cast a numpy array to a specified type.
Notes
.. versionchanged:: 2.0.0
Using ``astype`` to convert from timezone-naive dtype to
timezone-aware dtype will raise an exception.
Use :meth:`Series.dt.tz_localize` instead.
Create a DataFrame:
>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df.dtypes
col1 int64
col2 int64
dtype: object
Cast all columns to int32:
>>> df.astype('int32').dtypes
col1 int32
col2 int32
dtype: object
Cast col1 to int32 using a dictionary:
>>> df.astype({'col1': 'int32'}).dtypes
col1 int32
col2 int64
dtype: object
Create a series:
>>> ser = pd.Series([1, 2], dtype='int32')
>>> ser
0 1
1 2
dtype: int32
>>> ser.astype('int64')
0 1
1 2
dtype: int64
Convert to categorical type:
>>> ser.astype('category')
0 1
1 2
dtype: category
Categories (2, int32): [1, 2]
Convert to ordered categorical type with custom ordering:
>>> from pandas.api.types import CategoricalDtype
>>> cat_dtype = CategoricalDtype(
... categories=[2, 1], ordered=True)
>>> ser.astype(cat_dtype)
0 1
1 2
dtype: category
Categories (2, int64): [2 < 1]
Create a series of dates:
>>> ser_date = pd.Series(pd.date_range('20200101', periods=3))
>>> ser_date
0 2020-01-01
1 2020-01-02
2 2020-01-03
dtype: datetime64[ns]
copy(deep=True)
Make a copy of this object's indices and data.
When deep=True (default), a new object will be created with a
copy of the calling object's data and indices. Modifications to
the data or indices of the copy will not be reflected in the
original object (see notes below).
When deep=False, a new object will be created without copying
the calling object's data or index (only references to the data
and index are copied). Any changes to the data of the original
will be reflected in the shallow copy (and vice versa).
.. note::
The deep=False behaviour as described above will change
in pandas 3.0. Copy-on-Write
<https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>__
will be enabled by default, which means that the "shallow" copy
is that is returned with deep=False will still avoid making
an eager copy, but changes to the data of the original will no
longer be reflected in the shallow copy (or vice versa). Instead,
it makes use of a lazy (deferred) copy mechanism that will copy
the data only when any changes to the original or shallow copy is
made.
You can already get the future behavior and improvements through
enabling copy on write ``pd.options.mode.copy_on_write = True``
deep(bool, default True) — Make a deep copy, including a copy of the data and the indices.Withdeep=Falseneither the indices nor the data are copied.
Object type matches caller.
Notes
When deep=True, data is copied but actual Python objects
will not be copied recursively, only the reference to the object.
This is in contrast to copy.deepcopy in the Standard Library,
which recursively copies object data (see examples below).
While Index objects are copied when deep=True, the underlying
numpy array is not copied for performance reasons. Since Index is
immutable, the underlying data can be safely shared and a copy
is not needed.
Since pandas is not thread safe, see the
:ref:gotchas <gotchas.thread-safety> when copying in a threading
environment.
When copy_on_write in pandas config is set to True, the
copy_on_write config takes effect even when deep=False.
This means that any changes to the copied data would make a new copy
of the data upon write (and vice versa). Changes made to either the
original or copied variable would not be reflected in the counterpart.
See :ref:Copy_on_Write <copy_on_write> for more information.
>>> s = pd.Series([1, 2], index=["a", "b"])>>> s
a 1
b 2
dtype: int64
>>> s_copy = s.copy()
>>> s_copy
a 1
b 2
dtype: int64
Shallow copy versus default (deep) copy:
>>> s = pd.Series([1, 2], index=["a", "b"])
>>> deep = s.copy()
>>> shallow = s.copy(deep=False)
Shallow copy shares data and index with original.
>>> s is shallow
False
>>> s.values is shallow.values and s.index is shallow.index
True
Deep copy has own copy of data and index.
>>> s is deep
False
>>> s.values is deep.values or s.index is deep.index
False
Updates to the data shared by shallow copy and original is reflected in both (NOTE: this will no longer be true for pandas >= 3.0); deep copy remains unchanged.
>>> s.iloc[0] = 3
>>> shallow.iloc[1] = 4
>>> s
a 3
b 4
dtype: int64
>>> shallow
a 3
b 4
dtype: int64
>>> deep
a 1
b 2
dtype: int64
Note that when copying an object containing Python objects, a deep copy will copy the data, but will not do so recursively. Updating a nested data object will be reflected in the deep copy.
>>> s = pd.Series([[1, 2], [3, 4]])
>>> deep = s.copy()
>>> s[0][0] = 10
>>> s
0 [10, 2]
1 [3, 4]
dtype: object
>>> deep
0 [10, 2]
1 [3, 4]
dtype: object
Copy-on-Write is set to true, the shallow copy is not modified when the original data is changed:
>>> with pd.option_context("mode.copy_on_write", True):
... s = pd.Series([1, 2], index=["a", "b"])
... copy = s.copy(deep=False)
... s.iloc[0] = 100
... s
a 100
b 2
dtype: int64
>>> copy
a 1
b 2
dtype: int64
__deepcopy__(memo=None)
infer_objects(copy=None)
Attempt to infer better dtypes for object columns.
Attempts soft conversion of object-dtyped columns, leaving non-object and unconvertible columns unchanged. The inference rules are the same as during normal Series/DataFrame construction.
copy(bool, default True) — Whether to make a copy for non-object or non-inferable columnsor Series.
.. note:: Thecopykeyword will change behavior in pandas 3.0.Copy-on-Write <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>__ will be enabled by default, which means that all methods with acopykeyword will use a lazy copy mechanism to defer the copy and ignore thecopykeyword. Thecopykeyword will be removed in a future version of pandas.
You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True``
to_datetime : Convert argument to datetime.to_timedelta : Convert argument to timedelta. to_numeric : Convert argument to numeric type. convert_dtypes : Convert argument to best possible dtype.
>>> df = pd.DataFrame({"A": ["a", 1, 2, 3]})>>> df = df.iloc[1:]
>>> df
A
1 1
2 2
3 3
>>> df.dtypes
A object
dtype: object
>>> df.infer_objects().dtypes
A int64
dtype: object
convert_dtypes(infer_objects=True, convert_string=True, convert_integer=True, convert_boolean=True, convert_floating=True, dtype_backend='numpy_nullable')
Convert columns to the best possible dtypes using dtypes supporting pd.NA.
infer_objects(bool, default True) — Whether object dtypes should be converted to the best possible types.convert_string(bool, default True) — Whether object dtypes should be converted toStringDtype().convert_integer(bool, default True) — Whether, if possible, conversion can be done to integer extension types.convert_boolean(bool, defaults True) — Whether object dtypes should be converted toBooleanDtypes().convert_floating(bool, defaults True) — Whether, if possible, conversion can be done to floating extension types.Ifconvert_integeris also True, preference will be give to integer dtypes if the floats can be faithfully casted to integers.dtype_backend({'numpy_nullable', 'pyarrow'}, default 'numpy_nullable') — Back-end data type applied to the resultant :class:DataFrame(still experimental). Behaviour is as follows:
"numpy_nullable": returns nullable-dtype-backed :class:DataFrame(default)."pyarrow": returns pyarrow-backed nullable :class:ArrowDtypeDataFrame.
Copy of input object with new dtype.
infer_objects : Infer dtypes of objects.to_datetime : Convert argument to datetime. to_timedelta : Convert argument to timedelta. to_numeric : Convert argument to a numeric type.
Notes
By default, convert_dtypes will attempt to convert a Series (or each
Series in a DataFrame) to dtypes that support pd.NA. By using the options
convert_string, convert_integer, convert_boolean and
convert_floating, it is possible to turn off individual conversions
to StringDtype, the integer extension types, BooleanDtype
or floating extension types, respectively.
For object-dtyped columns, if infer_objects is True, use the inference
rules as during normal Series/DataFrame construction. Then, if possible,
convert to StringDtype, BooleanDtype or an appropriate integer
or floating extension type, otherwise leave as object.
If the dtype is integer, convert to an appropriate integer extension type.
If the dtype is numeric, and consists of all integers, convert to an appropriate integer extension type. Otherwise, convert to an appropriate floating extension type.
In the future, as new dtypes are added that support pd.NA, the results
of this method will change to support those new dtypes.
>>> df = pd.DataFrame(... {
... "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")),
... "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")),
... "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")),
... "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")),
... "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")),
... "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")),
... }
... )
Start with a DataFrame with default dtypes.
>>> df
a b c d e f
0 1 x True h 10.0 NaN
1 2 y False i NaN 100.5
2 3 z NaN NaN 20.0 200.0
>>> df.dtypes
a int32
b object
c object
d object
e float64
f float64
dtype: object
Convert the DataFrame to use best possible dtypes.
>>> dfn = df.convert_dtypes()
>>> dfn
a b c d e f
0 1 x True h 10 <NA>
1 2 y False i <NA> 100.5
2 3 z <NA> <NA> 20 200.0
>>> dfn.dtypes
a Int32
b string[python]
c boolean
d string[python]
e Int64
f Float64
dtype: object
Start with a Series of strings and missing data represented by np.nan.
>>> s = pd.Series(["a", "b", np.nan])
>>> s
0 a
1 b
2 NaN
dtype: object
Obtain a Series with dtype StringDtype.
>>> s.convert_dtypes()
0 a
1 b
2 <NA>
dtype: string
fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=<no_default>)
Fill NA/NaN values using the specified method.
value(scalar, dict, Series, or DataFrame) — Value to use to fill holes (e.g. 0), alternately adict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the dict/Series/DataFrame will not be filled. This value cannot be a list.method({'backfill', 'bfill', 'ffill', None}, default None) — Method to use for filling holes in reindexed Series:- ffill: propagate last valid observation forward to next valid.
- backfill / bfill: use next valid observation to fill gap.
axis({0 or 'index'} for Series, {0 or 'index', 1 or 'columns'} for DataFrame) — Axis along which to fill missing values. ForSeriesthis parameter is unused and defaults to 0.inplace(bool, default False) — If True, fill in-place. Note: this will modify anyother views on this object (e.g., a no-copy slice for a column in a DataFrame).limit(int, default None) — If method is specified, this is the maximum number of consecutiveNaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.downcast(dict, default is None) — A dict of item->dtype of what to downcast if possible,or the string 'infer' which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible).
.. deprecated:: 2.2.0
Object with missing values filled or None if inplace=True.
ffill : Fill values by propagating the last valid observation to next valid.bfill : Fill values by using the next valid observation to fill the gap. interpolate : Fill NaN values using interpolation. reindex : Conform object to new index. asfreq : Convert TimeSeries to specified frequency.
>>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],... [3, 4, np.nan, 1],
... [np.nan, np.nan, np.nan, np.nan],
... [np.nan, 3, np.nan, 4]],
... columns=list("ABCD"))
>>> df
A B C D
0 NaN 2.0 NaN 0.0
1 3.0 4.0 NaN 1.0
2 NaN NaN NaN NaN
3 NaN 3.0 NaN 4.0
Replace all NaN elements with 0s.
>>> df.fillna(0)
A B C D
0 0.0 2.0 0.0 0.0
1 3.0 4.0 0.0 1.0
2 0.0 0.0 0.0 0.0
3 0.0 3.0 0.0 4.0
Replace all NaN elements in column 'A', 'B', 'C', and 'D', with 0, 1, 2, and 3 respectively.
>>> values = {"A": 0, "B": 1, "C": 2, "D": 3}
>>> df.fillna(value=values)
A B C D
0 0.0 2.0 2.0 0.0
1 3.0 4.0 2.0 1.0
2 0.0 1.0 2.0 3.0
3 0.0 3.0 2.0 4.0
Only replace the first NaN element.
>>> df.fillna(value=values, limit=1)
A B C D
0 0.0 2.0 2.0 0.0
1 3.0 4.0 NaN 1.0
2 NaN 1.0 NaN 3.0
3 NaN 3.0 NaN 4.0
When filling using a DataFrame, replacement happens along the same column names and same indices
>>> df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE"))
>>> df.fillna(df2)
A B C D
0 0.0 2.0 0.0 0.0
1 3.0 4.0 0.0 1.0
2 0.0 0.0 0.0 NaN
3 0.0 3.0 0.0 4.0
Note that column D is not affected since it is not present in df2.
ffill(axis=None, inplace=False, limit=None, limit_area=None, downcast=<no_default>)
Fill NA/NaN values by propagating the last valid observation to next valid.
axis({0 or 'index'} for Series, {0 or 'index', 1 or 'columns'} for DataFrame) — Axis along which to fill missing values. ForSeriesthis parameter is unused and defaults to 0.inplace(bool, default False) — If True, fill in-place. Note: this will modify anyother views on this object (e.g., a no-copy slice for a column in a DataFrame).limit(int, default None) — If method is specified, this is the maximum number of consecutiveNaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.limit_area({`None`, 'inside', 'outside'}, default None) — If limit is specified, consecutive NaNs will be filled with thisrestriction.
None: No fill restriction.- 'inside': Only fill NaNs surrounded by valid values (interpolate).
- 'outside': Only fill NaNs outside valid values (extrapolate).
downcast(dict, default is None) — A dict of item->dtype of what to downcast if possible,or the string 'infer' which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible).
.. deprecated:: 2.2.0
Object with missing values filled or None if inplace=True.
>>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],... [3, 4, np.nan, 1],
... [np.nan, np.nan, np.nan, np.nan],
... [np.nan, 3, np.nan, 4]],
... columns=list("ABCD"))
>>> df
A B C D
0 NaN 2.0 NaN 0.0
1 3.0 4.0 NaN 1.0
2 NaN NaN NaN NaN
3 NaN 3.0 NaN 4.0
>>> df.ffill()
A B C D
0 NaN 2.0 NaN 0.0
1 3.0 4.0 NaN 1.0
2 3.0 4.0 NaN 1.0
3 3.0 3.0 NaN 4.0
>>> ser = pd.Series([1, np.nan, 2, 3])
>>> ser.ffill()
0 1.0
1 1.0
2 2.0
3 3.0
dtype: float64
pad(axis=None, inplace=False, limit=None, downcast=<no_default>)
Fill NA/NaN values by propagating the last valid observation to next valid.
.. deprecated:: 2.0
Series/DataFrame.pad is deprecated. Use Series/DataFrame.ffill instead.
Object with missing values filled or None if inplace=True.
Please see examples for :meth:DataFrame.ffill or :meth:Series.ffill.
bfill(axis=None, inplace=False, limit=None, limit_area=None, downcast=<no_default>)
Fill NA/NaN values by using the next valid observation to fill the gap.
axis({0 or 'index'} for Series, {0 or 'index', 1 or 'columns'} for DataFrame) — Axis along which to fill missing values. ForSeriesthis parameter is unused and defaults to 0.inplace(bool, default False) — If True, fill in-place. Note: this will modify anyother views on this object (e.g., a no-copy slice for a column in a DataFrame).limit(int, default None) — If method is specified, this is the maximum number of consecutiveNaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.limit_area({`None`, 'inside', 'outside'}, default None) — If limit is specified, consecutive NaNs will be filled with thisrestriction.
None: No fill restriction.- 'inside': Only fill NaNs surrounded by valid values (interpolate).
- 'outside': Only fill NaNs outside valid values (extrapolate).
downcast(dict, default is None) — A dict of item->dtype of what to downcast if possible,or the string 'infer' which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible).
.. deprecated:: 2.2.0
Object with missing values filled or None if inplace=True.
For Series:
>>> s = pd.Series([1, None, None, 2])
>>> s.bfill()
0 1.0
1 2.0
2 2.0
3 2.0
dtype: float64
>>> s.bfill(limit=1)
0 1.0
1 NaN
2 2.0
3 2.0
dtype: float64
With DataFrame:
>>> df = pd.DataFrame({'A': [1, None, None, 4], 'B': [None, 5, None, 7]})
>>> df
A B
0 1.0 NaN
1 NaN 5.0
2 NaN NaN
3 4.0 7.0
>>> df.bfill()
A B
0 1.0 5.0
1 4.0 5.0
2 4.0 7.0
3 4.0 7.0
>>> df.bfill(limit=1)
A B
0 1.0 5.0
1 NaN 5.0
2 4.0 7.0
3 4.0 7.0
backfill(axis=None, inplace=False, limit=None, downcast=<no_default>)
Fill NA/NaN values by using the next valid observation to fill the gap.
.. deprecated:: 2.0
Series/DataFrame.backfill is deprecated. Use Series/DataFrame.bfill instead.
Object with missing values filled or None if inplace=True.
Please see examples for :meth:DataFrame.bfill or :meth:Series.bfill.
replace(to_replace=None, value=<no_default>, inplace=False, limit=None, regex=False, method=<no_default>)
Replace values given in to_replace with value.
Values of the Series/DataFrame are replaced with other values dynamically.
This differs from updating with .loc or .iloc, which require
you to specify a location to update with some value.
to_replace(str, regex, list, dict, Series, int, float, or None) — How to find the values that will be replaced.-
numeric, str or regex:
- numeric: numeric values equal to
to_replacewill be replaced withvalue - str: string exactly matching
to_replacewill be replaced withvalue - regex: regexs matching
to_replacewill be replaced withvalue
- numeric: numeric values equal to
-
list of str, regex, or numeric:
- First, if
to_replaceandvalueare both lists, they must be the same length. - Second, if
regex=Truethen all of the strings in both lists will be interpreted as regexs otherwise they will match directly. This doesn't matter much forvaluesince there are only a few possible substitution regexes you can use. - str, regex and numeric rules apply as above.
- First, if
-
dict:
- Dicts can be used to specify different replacement values
for different existing values. For example,
{'a': 'b', 'y': 'z'}replaces the value 'a' with 'b' and 'y' with 'z'. To use a dict in this way, the optionalvalueparameter should not be given. - For a DataFrame a dict can specify that different values
should be replaced in different columns. For example,
{'a': 1, 'b': 'z'}looks for the value 1 in column 'a' and the value 'z' in column 'b' and replaces these values with whatever is specified invalue. Thevalueparameter should not beNonein this case. You can treat this as a special case of passing two lists except that you are specifying the column to search in. - For a DataFrame nested dictionaries, e.g.,
{'a': {'b': np.nan}}, are read as follows: look in column 'a' for the value 'b' and replace it with NaN. The optionalvalueparameter should not be specified to use a nested dict in this way. You can nest regular expressions as well. Note that column names (the top-level dictionary keys in a nested dictionary) cannot be regular expressions.
- Dicts can be used to specify different replacement values
for different existing values. For example,
-
None:
- This means that the
regexargument must be a string, compiled regular expression, or list, dict, ndarray or Series of such elements. Ifvalueis alsoNonethen this must be a nested dictionary or Series.
- This means that the
-
numeric, str or regex:
value(scalar, dict, list, str, regex, default None) — Value to replace any values matchingto_replacewith.For a DataFrame a dict of values can be used to specify which value to use for each column (columns not in the dict will not be filled). Regular expressions, strings and lists or dicts of such objects are also allowed.inplace(bool, default False) — If True, performs operation inplace and returns None.limit(int, default None) — Maximum size gap to forward or backward fill.
.. deprecated:: 2.1.0regex(bool or same types as `to_replace`, default False) — Whether to interpretto_replaceand/orvalueas regularexpressions. Alternatively, this could be a regular expression or a list, dict, or array of regular expressions in which caseto_replacemust beNone.method({'pad', 'ffill', 'bfill'}) — The method to use when for replacement, whento_replaceis ascalar, list or tuple andvalueisNone.
.. deprecated:: 2.1.0
Object after replacement.
AssertionError—- If
regexis not aboolandto_replaceis notNone.
- If
TypeError—- If
to_replaceis not a scalar, array-like,dict, orNone - If
to_replaceis adictandvalueis not alist,dict,ndarray, orSeries - If
to_replaceisNoneandregexis not compilable into a regular expression or is a list, dict, ndarray, or Series. - When replacing multiple
boolordatetime64objects and the arguments toto_replacedoes not match the type of the value being replaced
- If
ValueError—- If a
listor anndarrayis passed toto_replaceandvaluebut they are not the same length.
- If a
Series.fillna : Fill NA values.DataFrame.fillna : Fill NA values. Series.where : Replace values based on boolean condition. DataFrame.where : Replace values based on boolean condition. DataFrame.map: Apply a function to a Dataframe elementwise. Series.map: Map values of Series according to an input mapping or function. Series.str.replace : Simple string replacement.
Notes
- Regex substitution is performed under the hood with
re.sub. The rules for substitution forre.subare the same. - Regular expressions will only substitute on strings, meaning you cannot provide, for example, a regular expression matching floating point numbers and expect the columns in your frame that have a numeric dtype to be matched. However, if those floating point numbers are strings, then you can do this.
- This method has a lot of options. You are encouraged to experiment and play with this method to gain intuition about how it works.
- When dict is used as the
to_replacevalue, it is like key(s) in the dict are the to_replace part and value(s) in the dict are the value parameter.
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interpolate(method='linear', axis=0, limit=None, inplace=False, limit_direction=None, limit_area=None, downcast=<no_default>, **kwargs)
Fill NaN values using an interpolation method.
Please note that only method='linear' is supported for
DataFrame/Series with a MultiIndex.
method(str, default 'linear') — Interpolation technique to use. One of:- 'linear': Ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes.
- 'time': Works on daily and higher resolution data to interpolate given length of interval.
- 'index', 'values': use the actual numerical values of the index.
- 'pad': Fill in NaNs using existing values.
- 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',
'barycentric', 'polynomial': Passed to
scipy.interpolate.interp1d, whereas 'spline' is passed toscipy.interpolate.UnivariateSpline. These methods use the numerical values of the index. Both 'polynomial' and 'spline' require that you also specify anorder(int), e.g.df.interpolate(method='polynomial', order=5). Note that,slinearmethod in Pandas refers to the Scipy first ordersplineinstead of Pandas first orderspline. - 'krogh', 'piecewise_polynomial', 'spline', 'pchip', 'akima',
'cubicspline': Wrappers around the SciPy interpolation methods of
similar names. See
Notes. - 'from_derivatives': Refers to
scipy.interpolate.BPoly.from_derivatives.
axis({{0 or 'index', 1 or 'columns', None}}, default None) — Axis to interpolate along. ForSeriesthis parameter is unusedand defaults to 0.limit(int, optional) — Maximum number of consecutive NaNs to fill. Must be greater than0.inplace(bool, default False) — Update the data in place if possible.limit_direction({{'forward', 'backward', 'both'}}, Optional) — Consecutive NaNs will be filled in this direction.
If limit is specified: * If 'method' is 'pad' or 'ffill', 'limit_direction' must be 'forward'. * If 'method' is 'backfill' or 'bfill', 'limit_direction' must be 'backwards'.
If 'limit' is not specified: * If 'method' is 'backfill' or 'bfill', the default is 'backward' * else the default is 'forward'
raises ValueError iflimit_directionis 'forward' or 'both' and method is 'backfill' or 'bfill'. raises ValueError iflimit_directionis 'backward' or 'both' and method is 'pad' or 'ffill'.limit_area({{`None`, 'inside', 'outside'}}, default None) — If limit is specified, consecutive NaNs will be filled with thisrestriction.
None: No fill restriction.- 'inside': Only fill NaNs surrounded by valid values (interpolate).
- 'outside': Only fill NaNs outside valid values (extrapolate).
downcast(optional, 'infer' or None, defaults to None) — Downcast dtypes if possible.
.. deprecated:: 2.1.0
Returns the same object type as the caller, interpolated atsome or all NaN values or None if inplace=True.
fillna : Fill missing values using different methods.scipy.interpolate.Akima1DInterpolator : Piecewise cubic polynomials (Akima interpolator). scipy.interpolate.BPoly.from_derivatives : Piecewise polynomial in the Bernstein basis. scipy.interpolate.interp1d : Interpolate a 1-D function. scipy.interpolate.KroghInterpolator : Interpolate polynomial (Krogh interpolator). scipy.interpolate.PchipInterpolator : PCHIP 1-d monotonic cubic interpolation. scipy.interpolate.CubicSpline : Cubic spline data interpolator.
Notes
The 'krogh', 'piecewise_polynomial', 'spline', 'pchip' and 'akima'
methods are wrappers around the respective SciPy implementations of
similar names. These use the actual numerical values of the index.
For more information on their behavior, see the
SciPy documentation
<https://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation>__.
Filling in NaN in a :class:~pandas.Series via linearinterpolation.
>>> s = pd.Series([0, 1, np.nan, 3])
>>> s
0 0.0
1 1.0
2 NaN
3 3.0
dtype: float64
>>> s.interpolate()
0 0.0
1 1.0
2 2.0
3 3.0
dtype: float64
Filling in NaN in a Series via polynomial interpolation or splines:
Both 'polynomial' and 'spline' methods require that you also specify
an order (int).
>>> s = pd.Series([0, 2, np.nan, 8])
>>> s.interpolate(method='polynomial', order=2)
0 0.000000
1 2.000000
2 4.666667
3 8.000000
dtype: float64
Fill the DataFrame forward (that is, going down) along each column using linear interpolation.
Note how the last entry in column 'a' is interpolated differently,
because there is no entry after it to use for interpolation.
Note how the first entry in column 'b' remains NaN, because there
is no entry before it to use for interpolation.
>>> df = pd.DataFrame([(0.0, np.nan, -1.0, 1.0),
... (np.nan, 2.0, np.nan, np.nan),
... (2.0, 3.0, np.nan, 9.0),
... (np.nan, 4.0, -4.0, 16.0)],
... columns=list('abcd'))
>>> df
a b c d
0 0.0 NaN -1.0 1.0
1 NaN 2.0 NaN NaN
2 2.0 3.0 NaN 9.0
3 NaN 4.0 -4.0 16.0
>>> df.interpolate(method='linear', limit_direction='forward', axis=0)
a b c d
0 0.0 NaN -1.0 1.0
1 1.0 2.0 -2.0 5.0
2 2.0 3.0 -3.0 9.0
3 2.0 4.0 -4.0 16.0
Using polynomial interpolation.
>>> df['d'].interpolate(method='polynomial', order=2)
0 1.0
1 4.0
2 9.0
3 16.0
Name: d, dtype: float64
asof(where, subset=None)
Return the last row(s) without any NaNs before where.
The last row (for each element in where, if list) without any
NaN is taken.
In case of a :class:~pandas.DataFrame, the last row without NaN
considering only the subset of columns (if not None)
If there is no good value, NaN is returned for a Series or a Series of NaN values for a DataFrame
where(date or array-like of dates) — Date(s) before which the last row(s) are returned.subset(str or array-like of str, default `None`) — For DataFrame, if notNone, only use these columns tocheck for NaNs.
:
r , r n e
merge_asof : Perform an asof merge. Similar to left join.
Notes
Dates are assumed to be sorted. Raises if this is not the case.
A Series and a scalar where.
>>> s = pd.Series([1, 2, np.nan, 4], index=[10, 20, 30, 40])
>>> s
10 1.0
20 2.0
30 NaN
40 4.0
dtype: float64
>>> s.asof(20)
2.0
For a sequence where, a Series is returned. The first value is
NaN, because the first element of where is before the first
index value.
>>> s.asof([5, 20])
5 NaN
20 2.0
dtype: float64
Missing values are not considered. The following is 2.0, not
NaN, even though NaN is at the index location for 30.
>>> s.asof(30)
2.0
Take all columns into consideration
>>> df = pd.DataFrame({'a': [10., 20., 30., 40., 50.],
... 'b': [None, None, None, None, 500]},
... index=pd.DatetimeIndex(['2018-02-27 09:01:00',
... '2018-02-27 09:02:00',
... '2018-02-27 09:03:00',
... '2018-02-27 09:04:00',
... '2018-02-27 09:05:00']))
>>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
... '2018-02-27 09:04:30']))
a b
2018-02-27 09:03:30 NaN NaN
2018-02-27 09:04:30 NaN NaN
Take a single column into consideration
>>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
... '2018-02-27 09:04:30']),
... subset=['a'])
a b
2018-02-27 09:03:30 30.0 NaN
2018-02-27 09:04:30 40.0 NaN
clip(lower=None, upper=None, axis=None, inplace=False, **kwargs)
Trim values at input threshold(s).
Assigns values outside boundary to boundary values. Thresholds can be singular values or array like, and in the latter case the clipping is performed element-wise in the specified axis.
lower(float or array-like, default None) — Minimum threshold value. All values below thisthreshold will be set to it. A missing threshold (e.gNA) will not clip the value.upper(float or array-like, default None) — Maximum threshold value. All values above thisthreshold will be set to it. A missing threshold (e.gNA) will not clip the value.axis({{0 or 'index', 1 or 'columns', None}}, default None) — Align object with lower and upper along the given axis.ForSeriesthis parameter is unused and defaults toNone.inplace(bool, default False) — Whether to perform the operation in place on the data.
Same type as calling object with the values outside theclip boundaries replaced or None if inplace=True.
Series.clip : Trim values at input threshold in series.DataFrame.clip : Trim values at input threshold in dataframe. numpy.clip : Clip (limit) the values in an array.
>>> data = {'col_0': [9, -3, 0, -1, 5], 'col_1': [-2, -7, 6, 8, -5]}>>> df = pd.DataFrame(data)
>>> df
col_0 col_1
0 9 -2
1 -3 -7
2 0 6
3 -1 8
4 5 -5
Clips per column using lower and upper thresholds:
>>> df.clip(-4, 6)
col_0 col_1
0 6 -2
1 -3 -4
2 0 6
3 -1 6
4 5 -4
Clips using specific lower and upper thresholds per column:
>>> df.clip([-2, -1], [4, 5])
col_0 col_1
0 4 -1
1 -2 -1
2 0 5
3 -1 5
4 4 -1
Clips using specific lower and upper thresholds per column element:
>>> t = pd.Series([2, -4, -1, 6, 3])
>>> t
0 2
1 -4
2 -1
3 6
4 3
dtype: int64
>>> df.clip(t, t + 4, axis=0)
col_0 col_1
0 6 2
1 -3 -4
2 0 3
3 6 8
4 5 3
Clips using specific lower threshold per column element, with missing values:
>>> t = pd.Series([2, -4, np.nan, 6, 3])
>>> t
0 2.0
1 -4.0
2 NaN
3 6.0
4 3.0
dtype: float64
>>> df.clip(t, axis=0)
col_0 col_1
0 9 2
1 -3 -4
2 0 6
3 6 8
4 5 3
asfreq(freq, method=None, how=None, normalize=False, fill_value=None)
Convert time series to specified frequency.
Returns the original data conformed to a new index with the specified frequency.
If the index of this Series/DataFrame is a :class:~pandas.PeriodIndex, the new index
is the result of transforming the original index with
:meth:PeriodIndex.asfreq <pandas.PeriodIndex.asfreq> (so the original index
will map one-to-one to the new index).
Otherwise, the new index will be equivalent to pd.date_range(start, end,
freq=freq) where start and end are, respectively, the first and
last entries in the original index (see :func:pandas.date_range). The
values corresponding to any timesteps in the new index which were not present
in the original index will be null (NaN), unless a method for filling
such unknowns is provided (see the method parameter below).
The :meth:resample method is more appropriate if an operation on each group of
timesteps (such as an aggregate) is necessary to represent the data at the new
frequency.
freq(DateOffset or str) — Frequency DateOffset or string.method({'backfill'/'bfill', 'pad'/'ffill'}, default None) — Method to use for filling holes in reindexed Series (note thisdoes not fill NaNs that already were present):
- 'pad' / 'ffill': propagate last valid observation forward to next valid
- 'backfill' / 'bfill': use NEXT valid observation to fill.
how({'start', 'end'}, default end) — For PeriodIndex only (see PeriodIndex.asfreq).normalize(bool, default False) — Whether to reset output index to midnight.fill_value(scalar, optional) — Value to use for missing values, applied during upsampling (notethis does not fill NaNs that already were present).
Series/DataFrame object reindexed to the specified frequency.
reindex : Conform DataFrame to new index with optional filling logic.
Notes
To learn more about the frequency strings, please see this link
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>__.
Start by creating a series with 4 one minute timestamps.
>>> index = pd.date_range('1/1/2000', periods=4, freq='min')
>>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
>>> df = pd.DataFrame({'s': series})
>>> df
s
2000-01-01 00:00:00 0.0
2000-01-01 00:01:00 NaN
2000-01-01 00:02:00 2.0
2000-01-01 00:03:00 3.0
Upsample the series into 30 second bins.
>>> df.asfreq(freq='30s')
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 NaN
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 NaN
2000-01-01 00:03:00 3.0
Upsample again, providing a fill value.
>>> df.asfreq(freq='30s', fill_value=9.0)
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 9.0
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 9.0
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 9.0
2000-01-01 00:03:00 3.0
Upsample again, providing a method.
>>> df.asfreq(freq='30s', method='bfill')
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 2.0
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 3.0
2000-01-01 00:03:00 3.0
at_time(time, asof=False, axis=None)
Select values at particular time of day (e.g., 9:30AM).
time(datetime.time or str) — The values to select.axis({0 or 'index', 1 or 'columns'}, default 0) — ForSeriesthis parameter is unused and defaults to 0.
TypeError— If the index is not a :class:DatetimeIndex
between_time : Select values between particular times of the day.first : Select initial periods of time series based on a date offset. last : Select final periods of time series based on a date offset. DatetimeIndex.indexer_at_time : Get just the index locations for values at particular time of the day.
>>> i = pd.date_range('2018-04-09', periods=4, freq='12h')>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
A
2018-04-09 00:00:00 1
2018-04-09 12:00:00 2
2018-04-10 00:00:00 3
2018-04-10 12:00:00 4
>>> ts.at_time('12:00')
A
2018-04-09 12:00:00 2
2018-04-10 12:00:00 4
between_time(start_time, end_time, inclusive='both', axis=None)
Select values between particular times of the day (e.g., 9:00-9:30 AM).
By setting start_time to be later than end_time,
you can get the times that are not between the two times.
start_time(datetime.time or str) — Initial time as a time filter limit.end_time(datetime.time or str) — End time as a time filter limit.inclusive({"both", "neither", "left", "right"}, default "both") — Include boundaries; whether to set each bound as closed or open.axis({0 or 'index', 1 or 'columns'}, default 0) — Determine range time on index or columns value.ForSeriesthis parameter is unused and defaults to 0.
Data from the original object filtered to the specified dates range.
TypeError— If the index is not a :class:DatetimeIndex
at_time : Select values at a particular time of the day.first : Select initial periods of time series based on a date offset. last : Select final periods of time series based on a date offset. DatetimeIndex.indexer_between_time : Get just the index locations for values between particular times of the day.
>>> i = pd.date_range('2018-04-09', periods=4, freq='1D20min')>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
A
2018-04-09 00:00:00 1
2018-04-10 00:20:00 2
2018-04-11 00:40:00 3
2018-04-12 01:00:00 4
>>> ts.between_time('0:15', '0:45')
A
2018-04-10 00:20:00 2
2018-04-11 00:40:00 3
You get the times that are not between two times by setting
start_time later than end_time:
>>> ts.between_time('0:45', '0:15')
A
2018-04-09 00:00:00 1
2018-04-12 01:00:00 4
resample(rule, axis=<no_default>, closed=None, label=None, convention=<no_default>, kind=<no_default>, on=None, level=None, origin='start_day', offset=None, group_keys=False)
Resample time-series data.
Convenience method for frequency conversion and resampling of time series.
The object must have a datetime-like index (DatetimeIndex, PeriodIndex,
or TimedeltaIndex), or the caller must pass the label of a datetime-like
series/index to the on/level keyword parameter.
rule(DateOffset, Timedelta or str) — The offset string or object representing target conversion.axis({0 or 'index', 1 or 'columns'}, default 0) — Which axis to use for up- or down-sampling. ForSeriesthis parameteris unused and defaults to 0. Must beDatetimeIndex,TimedeltaIndexorPeriodIndex.
.. deprecated:: 2.0.0 Use frame.T.resample(...) instead.closed({'right', 'left'}, default None) — Which side of bin interval is closed. The default is 'left'for all frequency offsets except for 'ME', 'YE', 'QE', 'BME', 'BA', 'BQE', and 'W' which all have a default of 'right'.label({'right', 'left'}, default None) — Which bin edge label to label bucket with. The default is 'left'for all frequency offsets except for 'ME', 'YE', 'QE', 'BME', 'BA', 'BQE', and 'W' which all have a default of 'right'.convention({'start', 'end', 's', 'e'}, default 'start') — ForPeriodIndexonly, controls whether to use the start orend ofrule.
.. deprecated:: 2.2.0 Convert PeriodIndex to DatetimeIndex before resampling instead.kind({'timestamp', 'period'}, optional, default None) — Pass 'timestamp' to convert the resulting index to aDateTimeIndexor 'period' to convert it to aPeriodIndex. By default the input representation is retained.
.. deprecated:: 2.2.0 Convert index to desired type explicitly instead.on(str, optional) — For a DataFrame, column to use instead of index for resampling.Column must be datetime-like.level(str or int, optional) — For a MultiIndex, level (name or number) to use forresampling.levelmust be datetime-like.origin(Timestamp or str, default 'start_day') — The timestamp on which to adjust the grouping. The timezone of originmust match the timezone of the index. If string, must be one of the following:
- 'epoch':
originis 1970-01-01 - 'start':
originis the first value of the timeseries -
'start_day':
originis the first day at midnight of the timeseries
-
'end':
originis the last value of the timeseries
- 'end_day':
originis the ceiling midnight of the last day
.. note::
Only takes effect for Tick-frequencies (i.e. fixed frequencies like days, hours, and minutes, rather than months or quarters).- 'epoch':
offset(Timedelta or str, default is None) — An offset timedelta added to the origin.group_keys(bool, default False) — Whether to include the group keys in the result index when using.apply()on the resampled object.
.. versionadded:: 1.5.0
.. versionchanged:: 2.0.0Not specifying ``group_keys`` will retain values-dependent behavior from pandas 1.4 and earlier (see :ref:`pandas 1.5.0 Release notes <whatsnew_150.enhancements.resample_group_keys>` for examples).
``group_keys`` now defaults to ``False``.
:class:~pandas.core.Resampler object.
Series.resample : Resample a Series.DataFrame.resample : Resample a DataFrame. groupby : Group Series/DataFrame by mapping, function, label, or list of labels. asfreq : Reindex a Series/DataFrame with the given frequency without grouping.
Notes
See the user guide
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling>__
for more.
To learn more about the offset strings, please see this link
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects>__.
Start by creating a series with 9 one minute timestamps.
>>> index = pd.date_range('1/1/2000', periods=9, freq='min')
>>> series = pd.Series(range(9), index=index)
>>> series
2000-01-01 00:00:00 0
2000-01-01 00:01:00 1
2000-01-01 00:02:00 2
2000-01-01 00:03:00 3
2000-01-01 00:04:00 4
2000-01-01 00:05:00 5
2000-01-01 00:06:00 6
2000-01-01 00:07:00 7
2000-01-01 00:08:00 8
Freq: min, dtype: int64
Downsample the series into 3 minute bins and sum the values of the timestamps falling into a bin.
>>> series.resample('3min').sum()
2000-01-01 00:00:00 3
2000-01-01 00:03:00 12
2000-01-01 00:06:00 21
Freq: 3min, dtype: int64
Downsample the series into 3 minute bins as above, but label each
bin using the right edge instead of the left. Please note that the
value in the bucket used as the label is not included in the bucket,
which it labels. For example, in the original series the
bucket 2000-01-01 00:03:00 contains the value 3, but the summed
value in the resampled bucket with the label 2000-01-01 00:03:00
does not include 3 (if it did, the summed value would be 6, not 3).
>>> series.resample('3min', label='right').sum()
2000-01-01 00:03:00 3
2000-01-01 00:06:00 12
2000-01-01 00:09:00 21
Freq: 3min, dtype: int64
To include this value close the right side of the bin interval, as shown below.
>>> series.resample('3min', label='right', closed='right').sum()
2000-01-01 00:00:00 0
2000-01-01 00:03:00 6
2000-01-01 00:06:00 15
2000-01-01 00:09:00 15
Freq: 3min, dtype: int64
Upsample the series into 30 second bins.
>>> series.resample('30s').asfreq()[0:5] # Select first 5 rows
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 1.0
2000-01-01 00:01:30 NaN
2000-01-01 00:02:00 2.0
Freq: 30s, dtype: float64
Upsample the series into 30 second bins and fill the NaN
values using the ffill method.
>>> series.resample('30s').ffill()[0:5]
2000-01-01 00:00:00 0
2000-01-01 00:00:30 0
2000-01-01 00:01:00 1
2000-01-01 00:01:30 1
2000-01-01 00:02:00 2
Freq: 30s, dtype: int64
Upsample the series into 30 second bins and fill the
NaN values using the bfill method.
>>> series.resample('30s').bfill()[0:5]
2000-01-01 00:00:00 0
2000-01-01 00:00:30 1
2000-01-01 00:01:00 1
2000-01-01 00:01:30 2
2000-01-01 00:02:00 2
Freq: 30s, dtype: int64
Pass a custom function via apply
>>> def custom_resampler(arraylike):
... return np.sum(arraylike) + 5
...
>>> series.resample('3min').apply(custom_resampler)
2000-01-01 00:00:00 8
2000-01-01 00:03:00 17
2000-01-01 00:06:00 26
Freq: 3min, dtype: int64
For DataFrame objects, the keyword on can be used to specify the
column instead of the index for resampling.
>>> d = {'price': [10, 11, 9, 13, 14, 18, 17, 19],
... 'volume': [50, 60, 40, 100, 50, 100, 40, 50]}
>>> df = pd.DataFrame(d)
>>> df['week_starting'] = pd.date_range('01/01/2018',
... periods=8,
... freq='W')
>>> df
price volume week_starting
0 10 50 2018-01-07
1 11 60 2018-01-14
2 9 40 2018-01-21
3 13 100 2018-01-28
4 14 50 2018-02-04
5 18 100 2018-02-11
6 17 40 2018-02-18
7 19 50 2018-02-25
>>> df.resample('ME', on='week_starting').mean()
price volume
week_starting
2018-01-31 10.75 62.5
2018-02-28 17.00 60.0
For a DataFrame with MultiIndex, the keyword level can be used to
specify on which level the resampling needs to take place.
>>> days = pd.date_range('1/1/2000', periods=4, freq='D')
>>> d2 = {'price': [10, 11, 9, 13, 14, 18, 17, 19],
... 'volume': [50, 60, 40, 100, 50, 100, 40, 50]}
>>> df2 = pd.DataFrame(
... d2,
... index=pd.MultiIndex.from_product(
... [days, ['morning', 'afternoon']]
... )
... )
>>> df2
price volume
2000-01-01 morning 10 50
afternoon 11 60
2000-01-02 morning 9 40
afternoon 13 100
2000-01-03 morning 14 50
afternoon 18 100
2000-01-04 morning 17 40
afternoon 19 50
>>> df2.resample('D', level=0).sum()
price volume
2000-01-01 21 110
2000-01-02 22 140
2000-01-03 32 150
2000-01-04 36 90
If you want to adjust the start of the bins based on a fixed timestamp:
>>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00'
>>> rng = pd.date_range(start, end, freq='7min')
>>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng)
>>> ts
2000-10-01 23:30:00 0
2000-10-01 23:37:00 3
2000-10-01 23:44:00 6
2000-10-01 23:51:00 9
2000-10-01 23:58:00 12
2000-10-02 00:05:00 15
2000-10-02 00:12:00 18
2000-10-02 00:19:00 21
2000-10-02 00:26:00 24
Freq: 7min, dtype: int64
>>> ts.resample('17min').sum()
2000-10-01 23:14:00 0
2000-10-01 23:31:00 9
2000-10-01 23:48:00 21
2000-10-02 00:05:00 54
2000-10-02 00:22:00 24
Freq: 17min, dtype: int64
>>> ts.resample('17min', origin='epoch').sum()
2000-10-01 23:18:00 0
2000-10-01 23:35:00 18
2000-10-01 23:52:00 27
2000-10-02 00:09:00 39
2000-10-02 00:26:00 24
Freq: 17min, dtype: int64
>>> ts.resample('17min', origin='2000-01-01').sum()
2000-10-01 23:24:00 3
2000-10-01 23:41:00 15
2000-10-01 23:58:00 45
2000-10-02 00:15:00 45
Freq: 17min, dtype: int64
If you want to adjust the start of the bins with an offset Timedelta, the two
following lines are equivalent:
>>> ts.resample('17min', origin='start').sum()
2000-10-01 23:30:00 9
2000-10-01 23:47:00 21
2000-10-02 00:04:00 54
2000-10-02 00:21:00 24
Freq: 17min, dtype: int64
>>> ts.resample('17min', offset='23h30min').sum()
2000-10-01 23:30:00 9
2000-10-01 23:47:00 21
2000-10-02 00:04:00 54
2000-10-02 00:21:00 24
Freq: 17min, dtype: int64
If you want to take the largest Timestamp as the end of the bins:
>>> ts.resample('17min', origin='end').sum()
2000-10-01 23:35:00 0
2000-10-01 23:52:00 18
2000-10-02 00:09:00 27
2000-10-02 00:26:00 63
Freq: 17min, dtype: int64
In contrast with the start_day, you can use end_day to take the ceiling
midnight of the largest Timestamp as the end of the bins and drop the bins
not containing data:
>>> ts.resample('17min', origin='end_day').sum()
2000-10-01 23:38:00 3
2000-10-01 23:55:00 15
2000-10-02 00:12:00 45
2000-10-02 00:29:00 45
Freq: 17min, dtype: int64
first(offset)
Select initial periods of time series data based on a date offset.
.. deprecated:: 2.1
:meth:.first is deprecated and will be removed in a future version.
Please create a mask and filter using .loc instead.
For a DataFrame with a sorted DatetimeIndex, this function can select the first few rows based on a date offset.
offset(str, DateOffset or dateutil.relativedelta) — The offset length of the data that will be selected. For instance,'1ME' will display all the rows having their index within the first month.
A subset of the caller.
TypeError— If the index is not a :class:DatetimeIndex
last : Select final periods of time series based on a date offset.at_time : Select values at a particular time of the day. between_time : Select values between particular times of the day.
>>> i = pd.date_range('2018-04-09', periods=4, freq='2D')>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
A
2018-04-09 1
2018-04-11 2
2018-04-13 3
2018-04-15 4
Get the rows for the first 3 days:
>>> ts.first('3D')
A
2018-04-09 1
2018-04-11 2
Notice the data for 3 first calendar days were returned, not the first 3 days observed in the dataset, and therefore data for 2018-04-13 was not returned.
last(offset)
Select final periods of time series data based on a date offset.
.. deprecated:: 2.1
:meth:.last is deprecated and will be removed in a future version.
Please create a mask and filter using .loc instead.
For a DataFrame with a sorted DatetimeIndex, this function selects the last few rows based on a date offset.
offset(str, DateOffset, dateutil.relativedelta) — The offset length of the data that will be selected. For instance,'3D' will display all the rows having their index within the last 3 days.
A subset of the caller.
TypeError— If the index is not a :class:DatetimeIndex
first : Select initial periods of time series based on a date offset.at_time : Select values at a particular time of the day. between_time : Select values between particular times of the day.
Notes
.. deprecated:: 2.1.0
Please create a mask and filter using .loc instead
>>> i = pd.date_range('2018-04-09', periods=4, freq='2D')>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
A
2018-04-09 1
2018-04-11 2
2018-04-13 3
2018-04-15 4
Get the rows for the last 3 days:
>>> ts.last('3D') # doctest: +SKIP
A
2018-04-13 3
2018-04-15 4
Notice the data for 3 last calendar days were returned, not the last 3 observed days in the dataset, and therefore data for 2018-04-11 was not returned.
rank(axis=0, method='average', numeric_only=False, na_option='keep', ascending=True, pct=False)
Compute numerical data ranks (1 through n) along axis.
By default, equal values are assigned a rank that is the average of the ranks of those values.
axis({0 or 'index', 1 or 'columns'}, default 0) — Index to direct ranking.ForSeriesthis parameter is unused and defaults to 0.method({'average', 'min', 'max', 'first', 'dense'}, default 'average') — How to rank the group of records that have the same value (i.e. ties):- average: average rank of the group
- min: lowest rank in the group
- max: highest rank in the group
- first: ranks assigned in order they appear in the array
- dense: like 'min', but rank always increases by 1 between groups.
numeric_only(bool, default False) — For DataFrame objects, rank only numeric columns if set to True.
.. versionchanged:: 2.0.0 The default value ofnumeric_onlyis nowFalse.na_option({'keep', 'top', 'bottom'}, default 'keep') — How to rank NaN values:- keep: assign NaN rank to NaN values
- top: assign lowest rank to NaN values
- bottom: assign highest rank to NaN values
ascending(bool, default True) — Whether or not the elements should be ranked in ascending order.pct(bool, default False) — Whether or not to display the returned rankings in percentileform.
Return a Series or DataFrame with data ranks as values.
core.groupby.DataFrameGroupBy.rank : Rank of values within each group.core.groupby.SeriesGroupBy.rank : Rank of values within each group.
>>> df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog',... 'spider', 'snake'],
... 'Number_legs': [4, 2, 4, 8, np.nan]})
>>> df
Animal Number_legs
0 cat 4.0
1 penguin 2.0
2 dog 4.0
3 spider 8.0
4 snake NaN
Ties are assigned the mean of the ranks (by default) for the group.
>>> s = pd.Series(range(5), index=list("abcde"))
>>> s["d"] = s["b"]
>>> s.rank()
a 1.0
b 2.5
c 4.0
d 2.5
e 5.0
dtype: float64
The following example shows how the method behaves with the above parameters:
- default_rank: this is the default behaviour obtained without using any parameter.
- max_rank: setting
method = 'max'the records that have the same values are ranked using the highest rank (e.g.: since 'cat' and 'dog' are both in the 2nd and 3rd position, rank 3 is assigned.) - NA_bottom: choosing
na_option = 'bottom', if there are records with NaN values they are placed at the bottom of the ranking. - pct_rank: when setting
pct = True, the ranking is expressed as percentile rank.
>>> df['default_rank'] = df['Number_legs'].rank()
>>> df['max_rank'] = df['Number_legs'].rank(method='max')
>>> df['NA_bottom'] = df['Number_legs'].rank(na_option='bottom')
>>> df['pct_rank'] = df['Number_legs'].rank(pct=True)
>>> df
Animal Number_legs default_rank max_rank NA_bottom pct_rank
0 cat 4.0 2.5 3.0 2.5 0.625
1 penguin 2.0 1.0 1.0 1.0 0.250
2 dog 4.0 2.5 3.0 2.5 0.625
3 spider 8.0 4.0 4.0 4.0 1.000
4 snake NaN NaN NaN 5.0 NaN
align(other, join='outer', axis=None, level=None, copy=None, fill_value=None, method=<no_default>, limit=<no_default>, fill_axis=<no_default>, broadcast_axis=<no_default>)
Align two objects on their axes with the specified join method.
Join method is specified for each axis Index.
join({'outer', 'inner', 'left', 'right'}, default 'outer') — Type of alignment to be performed.- left: use only keys from left frame, preserve key order.
- right: use only keys from right frame, preserve key order.
- outer: use union of keys from both frames, sort keys lexicographically.
- inner: use intersection of keys from both frames, preserve the order of the left keys.
axis(allowed axis of the other object, default None) — Align on index (0), columns (1), or both (None).level(int or level name, default None) — Broadcast across a level, matching Index values on thepassed MultiIndex level.copy(bool, default True) — Always returns new objects. If copy=False and no reindexing isrequired then original objects are returned.
.. note:: Thecopykeyword will change behavior in pandas 3.0.Copy-on-Write <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>__ will be enabled by default, which means that all methods with acopykeyword will use a lazy copy mechanism to defer the copy and ignore thecopykeyword. Thecopykeyword will be removed in a future version of pandas.
You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True``fill_value(scalar, default np.nan) — Value to use for missing values. Defaults to NaN, but can be any"compatible" value.method({'backfill', 'bfill', 'pad', 'ffill', None}, default None) — Method to use for filling holes in reindexed Series:- pad / ffill: propagate last valid observation forward to next valid.
- backfill / bfill: use NEXT valid observation to fill gap.
limit(int, default None) — If method is specified, this is the maximum number of consecutiveNaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.
.. deprecated:: 2.1fill_axis({0 or 'index'} for Series, {0 or 'index', 1 or 'columns'} for DataFrame, default 0) — Filling axis, method and limit.
.. deprecated:: 2.1broadcast_axis({0 or 'index'} for Series, {0 or 'index', 1 or 'columns'} for DataFrame, default None) — Broadcast values along this axis, if aligning two objects ofdifferent dimensions.
.. deprecated:: 2.1
Aligned objects.
>>> df = pd.DataFrame(... [[1, 2, 3, 4], [6, 7, 8, 9]], columns=["D", "B", "E", "A"], index=[1, 2]
... )
>>> other = pd.DataFrame(
... [[10, 20, 30, 40], [60, 70, 80, 90], [600, 700, 800, 900]],
... columns=["A", "B", "C", "D"],
... index=[2, 3, 4],
... )
>>> df
D B E A
1 1 2 3 4
2 6 7 8 9
>>> other
A B C D
2 10 20 30 40
3 60 70 80 90
4 600 700 800 900
Align on columns:
>>> left, right = df.align(other, join="outer", axis=1)
>>> left
A B C D E
1 4 2 NaN 1 3
2 9 7 NaN 6 8
>>> right
A B C D E
2 10 20 30 40 NaN
3 60 70 80 90 NaN
4 600 700 800 900 NaN
We can also align on the index:
>>> left, right = df.align(other, join="outer", axis=0)
>>> left
D B E A
1 1.0 2.0 3.0 4.0
2 6.0 7.0 8.0 9.0
3 NaN NaN NaN NaN
4 NaN NaN NaN NaN
>>> right
A B C D
1 NaN NaN NaN NaN
2 10.0 20.0 30.0 40.0
3 60.0 70.0 80.0 90.0
4 600.0 700.0 800.0 900.0
Finally, the default axis=None will align on both index and columns:
>>> left, right = df.align(other, join="outer", axis=None)
>>> left
A B C D E
1 4.0 2.0 NaN 1.0 3.0
2 9.0 7.0 NaN 6.0 8.0
3 NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN
>>> right
A B C D E
1 NaN NaN NaN NaN NaN
2 10.0 20.0 30.0 40.0 NaN
3 60.0 70.0 80.0 90.0 NaN
4 600.0 700.0 800.0 900.0 NaN
where(cond, other=nan, inplace=False, axis=None, level=None)
Replace values where the condition is False.
cond(bool Series/DataFrame, array-like, or callable) — Wherecondis True, keep the original value. WhereFalse, replace with corresponding value fromother. Ifcondis callable, it is computed on the Series/DataFrame and should return boolean Series/DataFrame or array. The callable must not change input Series/DataFrame (though pandas doesn't check it).other(scalar, Series/DataFrame, or callable) — Entries wherecondis False are replaced withcorresponding value fromother. If other is callable, it is computed on the Series/DataFrame and should return scalar or Series/DataFrame. The callable must not change input Series/DataFrame (though pandas doesn't check it). If not specified, entries will be filled with the corresponding NULL value (np.nanfor numpy dtypes,pd.NAfor extension dtypes).inplace(bool, default False) — Whether to perform the operation in place on the data.axis(int, default None) — Alignment axis if needed. ForSeriesthis parameter isunused and defaults to 0.level(int, default None) — Alignment level if needed.
:func:DataFrame.mask : Return an object of same shape as self.
Notes
The where method is an application of the if-then idiom. For each
element in the calling DataFrame, if cond is True the
element is used; otherwise the corresponding element from the DataFrame
other is used. If the axis of other does not align with axis of
cond Series/DataFrame, the misaligned index positions will be filled with
False.
The signature for :func:DataFrame.where differs from
:func:numpy.where. Roughly df1.where(m, df2) is equivalent to
np.where(m, df1, df2).
For further details and examples see the where documentation in
:ref:indexing <indexing.where_mask>.
The dtype of the object takes precedence. The fill value is casted to the object's dtype, if this can be done losslessly.
>>> s = pd.Series(range(5))>>> s.where(s > 0)
0 NaN
1 1.0
2 2.0
3 3.0
4 4.0
dtype: float64
>>> s.mask(s > 0)
0 0.0
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
>>> s = pd.Series(range(5))
>>> t = pd.Series([True, False])
>>> s.where(t, 99)
0 0
1 99
2 99
3 99
4 99
dtype: int64
>>> s.mask(t, 99)
0 99
1 1
2 99
3 99
4 99
dtype: int64
>>> s.where(s > 1, 10)
0 10
1 10
2 2
3 3
4 4
dtype: int64
>>> s.mask(s > 1, 10)
0 0
1 1
2 10
3 10
4 10
dtype: int64
>>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B'])
>>> df
A B
0 0 1
1 2 3
2 4 5
3 6 7
4 8 9
>>> m = df % 3 == 0
>>> df.where(m, -df)
A B
0 0 -1
1 -2 3
2 -4 -5
3 6 -7
4 -8 9
>>> df.where(m, -df) == np.where(m, df, -df)
A B
0 True True
1 True True
2 True True
3 True True
4 True True
>>> df.where(m, -df) == df.mask(~m, -df)
A B
0 True True
1 True True
2 True True
3 True True
4 True True
mask(cond, other=<no_default>, inplace=False, axis=None, level=None)
Replace values where the condition is True.
cond(bool Series/DataFrame, array-like, or callable) — Wherecondis False, keep the original value. WhereTrue, replace with corresponding value fromother. Ifcondis callable, it is computed on the Series/DataFrame and should return boolean Series/DataFrame or array. The callable must not change input Series/DataFrame (though pandas doesn't check it).other(scalar, Series/DataFrame, or callable) — Entries wherecondis True are replaced withcorresponding value fromother. If other is callable, it is computed on the Series/DataFrame and should return scalar or Series/DataFrame. The callable must not change input Series/DataFrame (though pandas doesn't check it). If not specified, entries will be filled with the corresponding NULL value (np.nanfor numpy dtypes,pd.NAfor extension dtypes).inplace(bool, default False) — Whether to perform the operation in place on the data.axis(int, default None) — Alignment axis if needed. ForSeriesthis parameter isunused and defaults to 0.level(int, default None) — Alignment level if needed.
:func:DataFrame.where : Return an object of same shape as self.
Notes
The mask method is an application of the if-then idiom. For each
element in the calling DataFrame, if cond is False the
element is used; otherwise the corresponding element from the DataFrame
other is used. If the axis of other does not align with axis of
cond Series/DataFrame, the misaligned index positions will be filled with
True.
The signature for :func:DataFrame.where differs from
:func:numpy.where. Roughly df1.where(m, df2) is equivalent to
np.where(m, df1, df2).
For further details and examples see the mask documentation in
:ref:indexing <indexing.where_mask>.
The dtype of the object takes precedence. The fill value is casted to the object's dtype, if this can be done losslessly.
>>> s = pd.Series(range(5))>>> s.where(s > 0)
0 NaN
1 1.0
2 2.0
3 3.0
4 4.0
dtype: float64
>>> s.mask(s > 0)
0 0.0
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
>>> s = pd.Series(range(5))
>>> t = pd.Series([True, False])
>>> s.where(t, 99)
0 0
1 99
2 99
3 99
4 99
dtype: int64
>>> s.mask(t, 99)
0 99
1 1
2 99
3 99
4 99
dtype: int64
>>> s.where(s > 1, 10)
0 10
1 10
2 2
3 3
4 4
dtype: int64
>>> s.mask(s > 1, 10)
0 0
1 1
2 10
3 10
4 10
dtype: int64
>>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B'])
>>> df
A B
0 0 1
1 2 3
2 4 5
3 6 7
4 8 9
>>> m = df % 3 == 0
>>> df.where(m, -df)
A B
0 0 -1
1 -2 3
2 -4 -5
3 6 -7
4 -8 9
>>> df.where(m, -df) == np.where(m, df, -df)
A B
0 True True
1 True True
2 True True
3 True True
4 True True
>>> df.where(m, -df) == df.mask(~m, -df)
A B
0 True True
1 True True
2 True True
3 True True
4 True True
truncate(before=None, after=None, axis=None, copy=None)
Truncate a Series or DataFrame before and after some index value.
This is a useful shorthand for boolean indexing based on index values above or below certain thresholds.
before(date, str, int) — Truncate all rows before this index value.after(date, str, int) — Truncate all rows after this index value.axis({0 or 'index', 1 or 'columns'}, optional) — Axis to truncate. Truncates the index (rows) by default.ForSeriesthis parameter is unused and defaults to 0.copy(bool, default is True,) — Return a copy of the truncated section.
.. note:: Thecopykeyword will change behavior in pandas 3.0.Copy-on-Write <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>__ will be enabled by default, which means that all methods with acopykeyword will use a lazy copy mechanism to defer the copy and ignore thecopykeyword. Thecopykeyword will be removed in a future version of pandas.
You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True``
The truncated Series or DataFrame.
DataFrame.loc : Select a subset of a DataFrame by label.DataFrame.iloc : Select a subset of a DataFrame by position.
Notes
If the index being truncated contains only datetime values,
before and after may be specified as strings instead of
Timestamps.
>>> df = pd.DataFrame({'A': ['a', 'b', 'c', 'd', 'e'],... 'B': ['f', 'g', 'h', 'i', 'j'],
... 'C': ['k', 'l', 'm', 'n', 'o']},
... index=[1, 2, 3, 4, 5])
>>> df
A B C
1 a f k
2 b g l
3 c h m
4 d i n
5 e j o
>>> df.truncate(before=2, after=4)
A B C
2 b g l
3 c h m
4 d i n
The columns of a DataFrame can be truncated.
>>> df.truncate(before="A", after="B", axis="columns")
A B
1 a f
2 b g
3 c h
4 d i
5 e j
For Series, only rows can be truncated.
>>> df['A'].truncate(before=2, after=4)
2 b
3 c
4 d
Name: A, dtype: object
The index values in truncate can be datetimes or string
dates.
>>> dates = pd.date_range('2016-01-01', '2016-02-01', freq='s')
>>> df = pd.DataFrame(index=dates, data={'A': 1})
>>> df.tail()
A
2016-01-31 23:59:56 1
2016-01-31 23:59:57 1
2016-01-31 23:59:58 1
2016-01-31 23:59:59 1
2016-02-01 00:00:00 1
>>> df.truncate(before=pd.Timestamp('2016-01-05'),
... after=pd.Timestamp('2016-01-10')).tail()
A
2016-01-09 23:59:56 1
2016-01-09 23:59:57 1
2016-01-09 23:59:58 1
2016-01-09 23:59:59 1
2016-01-10 00:00:00 1
Because the index is a DatetimeIndex containing only dates, we can
specify before and after as strings. They will be coerced to
Timestamps before truncation.
>>> df.truncate('2016-01-05', '2016-01-10').tail()
A
2016-01-09 23:59:56 1
2016-01-09 23:59:57 1
2016-01-09 23:59:58 1
2016-01-09 23:59:59 1
2016-01-10 00:00:00 1
Note that truncate assumes a 0 value for any unspecified time
component (midnight). This differs from partial string slicing, which
returns any partially matching dates.
>>> df.loc['2016-01-05':'2016-01-10', :].tail()
A
2016-01-10 23:59:55 1
2016-01-10 23:59:56 1
2016-01-10 23:59:57 1
2016-01-10 23:59:58 1
2016-01-10 23:59:59 1
tz_convert(tz, axis=0, level=None, copy=None)
Convert tz-aware axis to target time zone.
tz(str or tzinfo object or None) — Target time zone. PassingNonewill convert toUTC and remove the timezone information.axis({0 or 'index', 1 or 'columns'}, default 0) — The axis to convertlevel(int, str, default None) — If axis is a MultiIndex, convert a specific level. Otherwisemust be None.copy(bool, default True) — Also make a copy of the underlying data.
.. note:: Thecopykeyword will change behavior in pandas 3.0.Copy-on-Write <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>__ will be enabled by default, which means that all methods with acopykeyword will use a lazy copy mechanism to defer the copy and ignore thecopykeyword. Thecopykeyword will be removed in a future version of pandas.
You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True``
Object with time zone converted axis.
TypeError— If the axis is tz-naive.
Change to another time zone:
>>> s = pd.Series(
... [1],
... index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']),
... )
>>> s.tz_convert('Asia/Shanghai')
2018-09-15 07:30:00+08:00 1
dtype: int64
Pass None to convert to UTC and get a tz-naive index:
>>> s = pd.Series([1],
... index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']))
>>> s.tz_convert(None)
2018-09-14 23:30:00 1
dtype: int64
tz_localize(tz, axis=0, level=None, copy=None, ambiguous='raise', nonexistent='raise')
Localize tz-naive index of a Series or DataFrame to target time zone.
This operation localizes the Index. To localize the values in a
timezone-naive Series, use :meth:Series.dt.tz_localize.
tz(str or tzinfo or None) — Time zone to localize. PassingNonewill remove thetime zone information and preserve local time.axis({0 or 'index', 1 or 'columns'}, default 0) — The axis to localizelevel(int, str, default None) — If axis ia a MultiIndex, localize a specific level. Otherwisemust be None.copy(bool, default True) — Also make a copy of the underlying data.
.. note:: Thecopykeyword will change behavior in pandas 3.0.Copy-on-Write <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>__ will be enabled by default, which means that all methods with acopykeyword will use a lazy copy mechanism to defer the copy and ignore thecopykeyword. Thecopykeyword will be removed in a future version of pandas.
You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True``ambiguous('infer', bool-ndarray, 'NaT', default 'raise') — When clocks moved backward due to DST, ambiguous times may arise.For example in Central European Time (UTC+01), when going from 03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC and at 01:30:00 UTC. In such a situation, theambiguousparameter dictates how ambiguous times should be handled.
- 'infer' will attempt to infer fall dst-transition hours based on order
- bool-ndarray where True signifies a DST time, False designates a non-DST time (note that this flag is only applicable for ambiguous times)
- 'NaT' will return NaT where there are ambiguous times
- 'raise' will raise an AmbiguousTimeError if there are ambiguous times.
nonexistent(str, default 'raise') — A nonexistent time does not exist in a particular timezonewhere clocks moved forward due to DST. Valid values are:
- 'shift_forward' will shift the nonexistent time forward to the closest existing time
- 'shift_backward' will shift the nonexistent time backward to the closest existing time
- 'NaT' will return NaT where there are nonexistent times
- timedelta objects will shift nonexistent times by the timedelta
- 'raise' will raise an NonExistentTimeError if there are nonexistent times.
Same type as the input.
TypeError— If the TimeSeries is tz-aware and tz is not None.
Localize local times:
>>> s = pd.Series(
... [1],
... index=pd.DatetimeIndex(['2018-09-15 01:30:00']),
... )
>>> s.tz_localize('CET')
2018-09-15 01:30:00+02:00 1
dtype: int64
Pass None to convert to tz-naive index and preserve local time:
>>> s = pd.Series([1],
... index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']))
>>> s.tz_localize(None)
2018-09-15 01:30:00 1
dtype: int64
Be careful with DST changes. When there is sequential data, pandas can infer the DST time:
>>> s = pd.Series(range(7),
... index=pd.DatetimeIndex(['2018-10-28 01:30:00',
... '2018-10-28 02:00:00',
... '2018-10-28 02:30:00',
... '2018-10-28 02:00:00',
... '2018-10-28 02:30:00',
... '2018-10-28 03:00:00',
... '2018-10-28 03:30:00']))
>>> s.tz_localize('CET', ambiguous='infer')
2018-10-28 01:30:00+02:00 0
2018-10-28 02:00:00+02:00 1
2018-10-28 02:30:00+02:00 2
2018-10-28 02:00:00+01:00 3
2018-10-28 02:30:00+01:00 4
2018-10-28 03:00:00+01:00 5
2018-10-28 03:30:00+01:00 6
dtype: int64
In some cases, inferring the DST is impossible. In such cases, you can pass an ndarray to the ambiguous parameter to set the DST explicitly
>>> s = pd.Series(range(3),
... index=pd.DatetimeIndex(['2018-10-28 01:20:00',
... '2018-10-28 02:36:00',
... '2018-10-28 03:46:00']))
>>> s.tz_localize('CET', ambiguous=np.array([True, True, False]))
2018-10-28 01:20:00+02:00 0
2018-10-28 02:36:00+02:00 1
2018-10-28 03:46:00+01:00 2
dtype: int64
If the DST transition causes nonexistent times, you can shift these
dates forward or backward with a timedelta object or 'shift_forward'
or 'shift_backward'.
>>> s = pd.Series(range(2),
... index=pd.DatetimeIndex(['2015-03-29 02:30:00',
... '2015-03-29 03:30:00']))
>>> s.tz_localize('Europe/Warsaw', nonexistent='shift_forward')
2015-03-29 03:00:00+02:00 0
2015-03-29 03:30:00+02:00 1
dtype: int64
>>> s.tz_localize('Europe/Warsaw', nonexistent='shift_backward')
2015-03-29 01:59:59.999999999+01:00 0
2015-03-29 03:30:00+02:00 1
dtype: int64
>>> s.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1h'))
2015-03-29 03:30:00+02:00 0
2015-03-29 03:30:00+02:00 1
dtype: int64
describe(percentiles=None, include=None, exclude=None)
Generate descriptive statistics.
Descriptive statistics include those that summarize the central
tendency, dispersion and shape of a
dataset's distribution, excluding NaN values.
Analyzes both numeric and object series, as well
as DataFrame column sets of mixed data types. The output
will vary depending on what is provided. Refer to the notes
below for more detail.
percentiles(list-like of numbers, optional) — The percentiles to include in the output. All shouldfall between 0 and 1. The default is[.25, .5, .75], which returns the 25th, 50th, and 75th percentiles.include('all', list-like of dtypes or None (default), optional) — A white list of data types to include in the result. IgnoredforSeries. Here are the options:
- 'all' : All columns of the input will be included in the output.
- A list-like of dtypes : Limits the results to the
provided data types.
To limit the result to numeric types submit
numpy.number. To limit it instead to object columns submit thenumpy.objectdata type. Strings can also be used in the style ofselect_dtypes(e.g.df.describe(include=['O'])). To select pandas categorical columns, use'category' - None (default) : The result will include all numeric columns.
exclude(list-like of dtypes or None (default), optional,) — A black list of data types to omit from the result. IgnoredforSeries. Here are the options:
- A list-like of dtypes : Excludes the provided data types
from the result. To exclude numeric types submit
numpy.number. To exclude object columns submit the data typenumpy.object. Strings can also be used in the style ofselect_dtypes(e.g.df.describe(exclude=['O'])). To exclude pandas categorical columns, use'category' - None (default) : The result will exclude nothing.
- A list-like of dtypes : Excludes the provided data types
from the result. To exclude numeric types submit
Summary statistics of the Series or Dataframe provided.
DataFrame.count: Count number of non-NA/null observations.DataFrame.max: Maximum of the values in the object. DataFrame.min: Minimum of the values in the object. DataFrame.mean: Mean of the values. DataFrame.std: Standard deviation of the observations. DataFrame.select_dtypes: Subset of a DataFrame including/excluding columns based on their dtype.
Notes
For numeric data, the result's index will include count,
mean, std, min, max as well as lower, 50 and
upper percentiles. By default the lower percentile is 25 and the
upper percentile is 75. The 50 percentile is the
same as the median.
For object data (e.g. strings or timestamps), the result's index
will include count, unique, top, and freq. The top
is the most common value. The freq is the most common value's
frequency. Timestamps also include the first and last items.
If multiple object values have the highest count, then the
count and top results will be arbitrarily chosen from
among those with the highest count.
For mixed data types provided via a DataFrame, the default is to
return only an analysis of numeric columns. If the dataframe consists
only of object and categorical data without any numeric columns, the
default is to return an analysis of both the object and categorical
columns. If include='all' is provided as an option, the result
will include a union of attributes of each type.
The include and exclude parameters can be used to limit
which columns in a DataFrame are analyzed for the output.
The parameters are ignored when analyzing a Series.
Describing a numeric Series.
>>> s = pd.Series([1, 2, 3])
>>> s.describe()
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
dtype: float64
Describing a categorical Series.
>>> s = pd.Series(['a', 'a', 'b', 'c'])
>>> s.describe()
count 4
unique 3
top a
freq 2
dtype: object
Describing a timestamp Series.
>>> s = pd.Series([
... np.datetime64("2000-01-01"),
... np.datetime64("2010-01-01"),
... np.datetime64("2010-01-01")
... ])
>>> s.describe()
count 3
mean 2006-09-01 08:00:00
min 2000-01-01 00:00:00
25% 2004-12-31 12:00:00
50% 2010-01-01 00:00:00
75% 2010-01-01 00:00:00
max 2010-01-01 00:00:00
dtype: object
Describing a DataFrame. By default only numeric fields
are returned.
>>> df = pd.DataFrame({'categorical': pd.Categorical(['d', 'e', 'f']),
... 'numeric': [1, 2, 3],
... 'object': ['a', 'b', 'c']
... })
>>> df.describe()
numeric
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Describing all columns of a DataFrame regardless of data type.
>>> df.describe(include='all') # doctest: +SKIP
categorical numeric object
count 3 3.0 3
unique 3 NaN 3
top f NaN a
freq 1 NaN 1
mean NaN 2.0 NaN
std NaN 1.0 NaN
min NaN 1.0 NaN
25% NaN 1.5 NaN
50% NaN 2.0 NaN
75% NaN 2.5 NaN
max NaN 3.0 NaN
Describing a column from a DataFrame by accessing it as
an attribute.
>>> df.numeric.describe()
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Name: numeric, dtype: float64
Including only numeric columns in a DataFrame description.
>>> df.describe(include=[np.number])
numeric
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Including only string columns in a DataFrame description.
>>> df.describe(include=[object]) # doctest: +SKIP
object
count 3
unique 3
top a
freq 1
Including only categorical columns from a DataFrame description.
>>> df.describe(include=['category'])
categorical
count 3
unique 3
top d
freq 1
Excluding numeric columns from a DataFrame description.
>>> df.describe(exclude=[np.number]) # doctest: +SKIP
categorical object
count 3 3
unique 3 3
top f a
freq 1 1
Excluding object columns from a DataFrame description.
>>> df.describe(exclude=[object]) # doctest: +SKIP
categorical numeric
count 3 3.0
unique 3 NaN
top f NaN
freq 1 NaN
mean NaN 2.0
std NaN 1.0
min NaN 1.0
25% NaN 1.5
50% NaN 2.0
75% NaN 2.5
max NaN 3.0
pct_change(periods=1, fill_method=<no_default>, limit=<no_default>, freq=None, **kwargs)
Fractional change between the current and a prior element.
Computes the fractional change from the immediately previous row by default. This is useful in comparing the fraction of change in a time series of elements.
.. note::
Despite the name of this method, it calculates fractional change
(also known as per unit change or relative change) and not
percentage change. If you need the percentage change, multiply
these values by 100.
periods(int, default 1) — Periods to shift for forming percent change.fill_method({'backfill', 'bfill', 'pad', 'ffill', None}, default 'pad') — How to handle NAs before computing percent changes.
.. deprecated:: 2.1 All options offill_methodare deprecated exceptfill_method=None.limit(int, default None) — The number of consecutive NAs to fill before stopping.
.. deprecated:: 2.1freq(DateOffset, timedelta, or str, optional) — Increment to use from time series API (e.g. 'ME' or BDay()).**kwargs— Additional keyword arguments are passed intoDataFrame.shiftorSeries.shift.
The same type as the calling object.
Series.diff : Compute the difference of two elements in a Series.DataFrame.diff : Compute the difference of two elements in a DataFrame. Series.shift : Shift the index by some number of periods. DataFrame.shift : Shift the index by some number of periods.
Series
>>> s = pd.Series([90, 91, 85])
>>> s
0 90
1 91
2 85
dtype: int64
>>> s.pct_change()
0 NaN
1 0.011111
2 -0.065934
dtype: float64
>>> s.pct_change(periods=2)
0 NaN
1 NaN
2 -0.055556
dtype: float64
See the percentage change in a Series where filling NAs with last valid observation forward to next valid.
>>> s = pd.Series([90, 91, None, 85])
>>> s
0 90.0
1 91.0
2 NaN
3 85.0
dtype: float64
>>> s.ffill().pct_change()
0 NaN
1 0.011111
2 0.000000
3 -0.065934
dtype: float64
DataFrame
Percentage change in French franc, Deutsche Mark, and Italian lira from 1980-01-01 to 1980-03-01.
>>> df = pd.DataFrame({
... 'FR': [4.0405, 4.0963, 4.3149],
... 'GR': [1.7246, 1.7482, 1.8519],
... 'IT': [804.74, 810.01, 860.13]},
... index=['1980-01-01', '1980-02-01', '1980-03-01'])
>>> df
FR GR IT
1980-01-01 4.0405 1.7246 804.74
1980-02-01 4.0963 1.7482 810.01
1980-03-01 4.3149 1.8519 860.13
>>> df.pct_change()
FR GR IT
1980-01-01 NaN NaN NaN
1980-02-01 0.013810 0.013684 0.006549
1980-03-01 0.053365 0.059318 0.061876
Percentage of change in GOOG and APPL stock volume. Shows computing the percentage change between columns.
>>> df = pd.DataFrame({
... '2016': [1769950, 30586265],
... '2015': [1500923, 40912316],
... '2014': [1371819, 41403351]},
... index=['GOOG', 'APPL'])
>>> df
2016 2015 2014
GOOG 1769950 1500923 1371819
APPL 30586265 40912316 41403351
>>> df.pct_change(axis='columns', periods=-1)
2016 2015 2014
GOOG 0.179241 0.094112 NaN
APPL -0.252395 -0.011860 NaN
rolling(window, min_periods=None, center=False, win_type=None, on=None, axis=<no_default>, closed=None, step=None, method='single')
Provide rolling window calculations.
window(int, timedelta, str, offset, or BaseIndexer subclass) — Size of the moving window.
If an integer, the fixed number of observations used for each window.
If a timedelta, str, or offset, the time period of each window. Each window will be a variable sized based on the observations included in the time-period. This is only valid for datetimelike indexes. To learn more about the offsets & frequency strings, please seethis link <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>__.
If a BaseIndexer subclass, the window boundaries based on the definedget_window_boundsmethod. Additional rolling keyword arguments, namelymin_periods,center,closedandstepwill be passed toget_window_bounds.min_periods(int, default None) — Minimum number of observations in window required to have a value;otherwise, result isnp.nan.
For a window that is specified by an offset,min_periodswill default to 1.
For a window that is specified by an integer,min_periodswill default to the size of the window.center(bool, default False) — If False, set the window labels as the right edge of the window index.
If True, set the window labels as the center of the window index.win_type(str, default None) — IfNone, all points are evenly weighted.
If a string, it must be a validscipy.signal window function <https://docs.scipy.org/doc/scipy/reference/signal.windows.html#module-scipy.signal.windows>__.
Certain Scipy window types require additional parameters to be passed in the aggregation function. The additional parameters must match the keywords specified in the Scipy window type method signature.on(str, optional) — For a DataFrame, a column label or Index level on whichto calculate the rolling window, rather than the DataFrame's index.
Provided integer column is ignored and excluded from result since an integer index is not used to calculate the rolling window.axis(int or str, default 0) — If0or'index', roll across the rows.
If1or'columns', roll across the columns.
ForSeriesthis parameter is unused and defaults to 0.
.. deprecated:: 2.1.0
The axis keyword is deprecated. For ``axis=1``, transpose the DataFrame first instead.closed(str, default None) — If'right', the first point in the window is excluded from calculations.
If'left', the last point in the window is excluded from calculations.
If'both', the no points in the window are excluded from calculations.
If'neither', the first and last points in the window are excluded from calculations.
DefaultNone('right').step(int, default None) — 0
s r .method(str {'single', 'table'}, default 'single') — 0
) .
` .
An instance of Window is returned if win_type is passed. Otherwise,an instance of Rolling is returned.
expanding : Provides expanding transformations.ewm : Provides exponential weighted functions.
Notes
See :ref:Windowing Operations <window.generic> for further usage details
and examples.
>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})>>> df
B
0 0.0
1 1.0
2 2.0
3 NaN
4 4.0
window
Rolling sum with a window length of 2 observations.
>>> df.rolling(2).sum()
B
0 NaN
1 1.0
2 3.0
3 NaN
4 NaN
Rolling sum with a window span of 2 seconds.
>>> df_time = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
... index=[pd.Timestamp('20130101 09:00:00'),
... pd.Timestamp('20130101 09:00:02'),
... pd.Timestamp('20130101 09:00:03'),
... pd.Timestamp('20130101 09:00:05'),
... pd.Timestamp('20130101 09:00:06')])
>>> df_time
B
2013-01-01 09:00:00 0.0
2013-01-01 09:00:02 1.0
2013-01-01 09:00:03 2.0
2013-01-01 09:00:05 NaN
2013-01-01 09:00:06 4.0
>>> df_time.rolling('2s').sum()
B
2013-01-01 09:00:00 0.0
2013-01-01 09:00:02 1.0
2013-01-01 09:00:03 3.0
2013-01-01 09:00:05 NaN
2013-01-01 09:00:06 4.0
Rolling sum with forward looking windows with 2 observations.
>>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2)
>>> df.rolling(window=indexer, min_periods=1).sum()
B
0 1.0
1 3.0
2 2.0
3 4.0
4 4.0
min_periods
Rolling sum with a window length of 2 observations, but only needs a minimum of 1 observation to calculate a value.
>>> df.rolling(2, min_periods=1).sum()
B
0 0.0
1 1.0
2 3.0
3 2.0
4 4.0
center
Rolling sum with the result assigned to the center of the window index.
>>> df.rolling(3, min_periods=1, center=True).sum()
B
0 1.0
1 3.0
2 3.0
3 6.0
4 4.0
>>> df.rolling(3, min_periods=1, center=False).sum()
B
0 0.0
1 1.0
2 3.0
3 3.0
4 6.0
step
Rolling sum with a window length of 2 observations, minimum of 1 observation to calculate a value, and a step of 2.
>>> df.rolling(2, min_periods=1, step=2).sum()
B
0 0.0
2 3.0
4 4.0
win_type
Rolling sum with a window length of 2, using the Scipy 'gaussian'
window type. std is required in the aggregation function.
>>> df.rolling(2, win_type='gaussian').sum(std=3)
B
0 NaN
1 0.986207
2 2.958621
3 NaN
4 NaN
on
Rolling sum with a window length of 2 days.
>>> df = pd.DataFrame({
... 'A': [pd.to_datetime('2020-01-01'),
... pd.to_datetime('2020-01-01'),
... pd.to_datetime('2020-01-02'),],
... 'B': [1, 2, 3], },
... index=pd.date_range('2020', periods=3))
>>> df
A B
2020-01-01 2020-01-01 1
2020-01-02 2020-01-01 2
2020-01-03 2020-01-02 3
>>> df.rolling('2D', on='A').sum()
A B
2020-01-01 2020-01-01 1.0
2020-01-02 2020-01-01 3.0
2020-01-03 2020-01-02 6.0
expanding(min_periods=1, axis=<no_default>, method='single')
Provide expanding window calculations.
min_periods(int, default 1) — Minimum number of observations in window required to have a value;otherwise, result isnp.nan.axis(int or str, default 0) — If0or'index', roll across the rows.
If1or'columns', roll across the columns.
ForSeriesthis parameter is unused and defaults to 0.method(str {'single', 'table'}, default 'single') — Execute the rolling operation per single column or row ('single')or over the entire object ('table').
This argument is only implemented when specifyingengine='numba'in the method call.
.. versionadded:: 1.3.0
rolling : Provides rolling window calculations.ewm : Provides exponential weighted functions.
Notes
See :ref:Windowing Operations <window.expanding> for further usage details
and examples.
>>> df = pd.DataFrame({"B": [0, 1, 2, np.nan, 4]})>>> df
B
0 0.0
1 1.0
2 2.0
3 NaN
4 4.0
min_periods
Expanding sum with 1 vs 3 observations needed to calculate a value.
>>> df.expanding(1).sum()
B
0 0.0
1 1.0
2 3.0
3 3.0
4 7.0
>>> df.expanding(3).sum()
B
0 NaN
1 NaN
2 3.0
3 3.0
4 7.0
ewm(com=None, span=None, halflife=None, alpha=None, min_periods=0, adjust=True, ignore_na=False, axis=<no_default>, times=None, method='single')
Provide exponentially weighted (EW) calculations.
Exactly one of com, span, halflife, or alpha must be
provided if times is not provided. If times is provided,
halflife and one of com, span or alpha may be provided.
com(float, optional) — Specify decay in terms of center of mass
:math:\alpha = 1 / (1 + com), for :math:com \geq 0.span(float, optional) — Specify decay in terms of span
:math:\alpha = 2 / (span + 1), for :math:span \geq 1.halflife(float, str, timedelta, optional) — Specify decay in terms of half-life
:math:\alpha = 1 - \exp\left(-\ln(2) / halflife\right), for :math:halflife > 0.
Iftimesis specified, a timedelta convertible unit over which an observation decays to half its value. Only applicable tomean(), and halflife value will not apply to the other functions.alpha(float, optional) — Specify smoothing factor :math:\alphadirectly
:math:0 < \alpha \leq 1.min_periods(int, default 0) — Minimum number of observations in window required to have a value;otherwise, result isnp.nan.adjust(bool, default True) — Divide by decaying adjustment factor in beginning periods to accountfor imbalance in relative weightings (viewing EWMA as a moving average).
- When
adjust=True(default), the EW function is calculated using weights :math:w_i = (1 - \alpha)^i. For example, the EW moving average of the series [:math:x_0, x_1, ..., x_t] would be:
- When
adjust=False, the exponentially weighted function is calculated recursively:
- When
ignore_na(bool, default False) — Ignore missing values when calculating weights.-
When
ignore_na=False(default), weights are based on absolute positions. For example, the weights of :math:x_0and :math:x_2used in calculating the final weighted average of [:math:x_0, None, :math:x_2] are :math:(1-\alpha)^2and :math:1ifadjust=True, and :math:(1-\alpha)^2and :math:\alphaifadjust=False.
-
When
ignore_na=True, weights are based on relative positions. For example, the weights of :math:x_0and :math:x_2used in calculating the final weighted average of [:math:x_0, None, :math:x_2] are :math:1-\alphaand :math:1ifadjust=True, and :math:1-\alphaand :math:\alphaifadjust=False.
-
When
axis({0, 1}, default 0) — If0or'index', calculate across the rows.
If1or'columns', calculate across the columns.
ForSeriesthis parameter is unused and defaults to 0.times(np.ndarray, Series, default None) — .
d .
.method(str {'single', 'table'}, default 'single') — .. versionadded:: 1.4.0
Execute the rolling operation per single column or row ('single') or over the entire object ('table').
This argument is only implemented when specifyingengine='numba'in the method call.
Only applicable tomean()
rolling : Provides rolling window calculations.expanding : Provides expanding transformations.
Notes
See :ref:Windowing Operations <window.exponentially_weighted>
for further usage details and examples.
>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})>>> df
B
0 0.0
1 1.0
2 2.0
3 NaN
4 4.0
>>> df.ewm(com=0.5).mean()
B
0 0.000000
1 0.750000
2 1.615385
3 1.615385
4 3.670213
>>> df.ewm(alpha=2 / 3).mean()
B
0 0.000000
1 0.750000
2 1.615385
3 1.615385
4 3.670213
adjust
>>> df.ewm(com=0.5, adjust=True).mean()
B
0 0.000000
1 0.750000
2 1.615385
3 1.615385
4 3.670213
>>> df.ewm(com=0.5, adjust=False).mean()
B
0 0.000000
1 0.666667
2 1.555556
3 1.555556
4 3.650794
ignore_na
>>> df.ewm(com=0.5, ignore_na=True).mean()
B
0 0.000000
1 0.750000
2 1.615385
3 1.615385
4 3.225000
>>> df.ewm(com=0.5, ignore_na=False).mean()
B
0 0.000000
1 0.750000
2 1.615385
3 1.615385
4 3.670213
times
Exponentially weighted mean with weights calculated with a timedelta halflife
relative to times.
>>> times = ['2020-01-01', '2020-01-03', '2020-01-10', '2020-01-15', '2020-01-17']
>>> df.ewm(halflife='4 days', times=pd.DatetimeIndex(times)).mean()
B
0 0.000000
1 0.585786
2 1.523889
3 1.523889
4 3.233686
first_valid_index()
Return index for first non-NA value or None, if no non-NA value is found.
For Series:
>>> s = pd.Series([None, 3, 4])
>>> s.first_valid_index()
1
>>> s.last_valid_index()
2
>>> s = pd.Series([None, None])
>>> print(s.first_valid_index())
None
>>> print(s.last_valid_index())
None
If all elements in Series are NA/null, returns None.
>>> s = pd.Series()
>>> print(s.first_valid_index())
None
>>> print(s.last_valid_index())
None
If Series is empty, returns None.
For DataFrame:
>>> df = pd.DataFrame({'A': [None, None, 2], 'B': [None, 3, 4]})
>>> df
A B
0 NaN NaN
1 NaN 3.0
2 2.0 4.0
>>> df.first_valid_index()
1
>>> df.last_valid_index()
2
>>> df = pd.DataFrame({'A': [None, None, None], 'B': [None, None, None]})
>>> df
A B
0 None None
1 None None
2 None None
>>> print(df.first_valid_index())
None
>>> print(df.last_valid_index())
None
If all elements in DataFrame are NA/null, returns None.
>>> df = pd.DataFrame()
>>> df
Empty DataFrame
Columns: []
Index: []
>>> print(df.first_valid_index())
None
>>> print(df.last_valid_index())
None
If DataFrame is empty, returns None.
last_valid_index()
Return index for last non-NA value or None, if no non-NA value is found.
For Series:
>>> s = pd.Series([None, 3, 4])
>>> s.first_valid_index()
1
>>> s.last_valid_index()
2
>>> s = pd.Series([None, None])
>>> print(s.first_valid_index())
None
>>> print(s.last_valid_index())
None
If all elements in Series are NA/null, returns None.
>>> s = pd.Series()
>>> print(s.first_valid_index())
None
>>> print(s.last_valid_index())
None
If Series is empty, returns None.
For DataFrame:
>>> df = pd.DataFrame({'A': [None, None, 2], 'B': [None, 3, 4]})
>>> df
A B
0 NaN NaN
1 NaN 3.0
2 2.0 4.0
>>> df.first_valid_index()
1
>>> df.last_valid_index()
2
>>> df = pd.DataFrame({'A': [None, None, None], 'B': [None, None, None]})
>>> df
A B
0 None None
1 None None
2 None None
>>> print(df.first_valid_index())
None
>>> print(df.last_valid_index())
None
If all elements in DataFrame are NA/null, returns None.
>>> df = pd.DataFrame()
>>> df
Empty DataFrame
Columns: []
Index: []
>>> print(df.first_valid_index())
None
>>> print(df.last_valid_index())
None
If DataFrame is empty, returns None.
__dataframe__(nan_as_null=False, allow_copy=True)
Return the dataframe interchange object implementing the interchange protocol.
nan_as_null(bool, default False) —nan_as_nullis DEPRECATED and has no effect. Please avoid usingit; it will be removed in a future release.allow_copy(bool, default True) — Whether to allow memory copying when exporting. If set to Falseit would cause non-zero-copy exports to fail.
The object which consuming library can use to ingress the dataframe.
Notes
Details on the interchange protocol: https://data-apis.org/dataframe-protocol/latest/index.html
>>> df_not_necessarily_pandas = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})>>> interchange_object = df_not_necessarily_pandas.__dataframe__()
>>> interchange_object.column_names()
Index(['A', 'B'], dtype='object')
>>> df_pandas = (pd.api.interchange.from_dataframe
... (interchange_object.select_columns_by_name(['A'])))
>>> df_pandas
A
0 1
1 2
These methods (column_names, select_columns_by_name) should work
for any dataframe library which implements the interchange protocol.
__dataframe_consortium_standard__(api_version=None) → Any
Provide entry point to the Consortium DataFrame Standard API.
This is developed and maintained outside of pandas. Please report any issues to https://github.com/data-apis/dataframe-api-compat.
__arrow_c_stream__(requested_schema=None)
Export the pandas DataFrame as an Arrow C stream PyCapsule.
This relies on pyarrow to convert the pandas DataFrame to the Arrow
format (and follows the default behaviour of pyarrow.Table.from_pandas
in its handling of the index, i.e. store the index as a column except
for RangeIndex).
This conversion is not necessarily zero-copy.
requested_schema(PyCapsule, default None) — The schema to which the dataframe should be casted, passed as aPyCapsule containing a C ArrowSchema representation of the requested schema.
__repr__() → str
Return a string representation for a particular DataFrame.
to_string(buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None, max_rows=None, max_cols=None, show_dimensions=False, decimal='.', line_width=None, min_rows=None, max_colwidth=None, encoding=None)
Render a DataFrame to a console-friendly tabular output.
buf(str, Path or StringIO-like, optional, default None) — Buffer to write to. If None, the output is returned as a string.columns(array-like, optional, default None) — The subset of columns to write. Writes all columns by default.col_space(int, list or dict of int, optional) — The minimum width of each column. If a list of ints is given every integers corresponds with one column. If a dict is given, the key references the column, while the value defines the space to use..header(bool or list of str, optional) — Write out the column names. If a list of columns is given, it is assumed to be aliases for the column names.index(bool, optional, default True) — Whether to print index (row) labels.na_rep(str, optional, default 'NaN') — String representation ofNaNto use.formatters(list, tuple or dict of one-param. functions, optional) — Formatter functions to apply to columns' elements by position orname. The result of each function must be a unicode string. List/tuple must be of length equal to the number of columns.float_format(one-parameter function, optional, default None) — Formatter function to apply to columns' elements if they arefloats. This function must return a unicode string and will be applied only to the non-NaNelements, withNaNbeing handled byna_rep.sparsify(bool, optional, default True) — Set to False for a DataFrame with a hierarchical index to printevery multiindex key at each row.index_names(bool, optional, default True) — Prints the names of the indexes.justify(str, default None) — How to justify the column labels. If None uses the option fromthe print configuration (controlled by set_option), 'right' out of the box. Valid values are
- left
- right
- center
- justify
- justify-all
- start
- end
- inherit
- match-parent
- initial
- unset.
max_rows(int, optional) — Maximum number of rows to display in the console.max_cols(int, optional) — Maximum number of columns to display in the console.show_dimensions(bool, default False) — Display DataFrame dimensions (number of rows by number of columns).decimal(str, default '.') — Character recognized as decimal separator, e.g. ',' in Europe.line_width(int, optional) — Width to wrap a line in characters.min_rows(int, optional) — The number of rows to display in the console in a truncated repr(when number of rows is abovemax_rows).max_colwidth(int, optional) — Max width to truncate each column in characters. By default, no limit.encoding(str, default "utf-8") — Set character encoding.
If buf is None, returns the result as a string. Otherwise returnsNone.
to_html : Convert DataFrame to HTML.
>>> d = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}>>> df = pd.DataFrame(d)
>>> print(df.to_string())
col1 col2
0 1 4
1 2 5
2 3 6
items()
Iterate over (column name, Series) pairs.
Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series.
The column names for the DataFrame being iterated over.ent : Series The column entries belonging to each label, as a Series.
DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs. DataFrame.itertuples : Iterate over DataFrame rows as namedtuples of the values.
>>> df = pd.DataFrame({'species': ['bear', 'bear', 'marsupial'],... 'population': [1864, 22000, 80000]},
... index=['panda', 'polar', 'koala'])
>>> df
species population
panda bear 1864
polar bear 22000
koala marsupial 80000
>>> for label, content in df.items():
... print(f'label: {label}')
... print(f'content: {content}', sep='\n')
...
label: species
content:
panda bear
polar bear
koala marsupial
Name: species, dtype: object
label: population
content:
panda 1864
polar 22000
koala 80000
Name: population, dtype: int64
iterrows()
Iterate over DataFrame rows as (index, Series) pairs.
The index of the row. A tuple for a MultiIndex. : Series
The data of the row as a Series.
DataFrame.itertuples : Iterate over DataFrame rows as namedtuples of the values.DataFrame.items : Iterate over (column name, Series) pairs.
Notes
- Because
iterrowsreturns a Series for each row, it does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames).
To preserve dtypes while iterating over the rows, it is better
to use :meth:itertuples which returns namedtuples of the values
and which is generally faster than iterrows.
- You should never modify something you are iterating over. This is not guaranteed to work in all cases. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect.
)] w 0 5 4 ) 4 ) 4
itertuples(index=True, name='Pandas')
Iterate over DataFrame rows as namedtuples.
index(bool, default True) — If True, return the index as the first element of the tuple.name(str or None, default "Pandas") — The name of the returned namedtuples or None to return regulartuples.
An object to iterate over namedtuples for each row in theDataFrame with the first field possibly being the index and following fields being the column values.
DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs. DataFrame.items : Iterate over (column name, Series) pairs.
Notes
The column names will be renamed to positional names if they are invalid Python identifiers, repeated, or start with an underscore.
>>> df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]},... index=['dog', 'hawk'])
>>> df
num_legs num_wings
dog 4 0
hawk 2 2
>>> for row in df.itertuples():
... print(row)
...
Pandas(Index='dog', num_legs=4, num_wings=0)
Pandas(Index='hawk', num_legs=2, num_wings=2)
By setting the index parameter to False we can remove the index
as the first element of the tuple:
>>> for row in df.itertuples(index=False):
... print(row)
...
Pandas(num_legs=4, num_wings=0)
Pandas(num_legs=2, num_wings=2)
With the name parameter set we set a custom name for the yielded
namedtuples:
>>> for row in df.itertuples(name='Animal'):
... print(row)
...
Animal(Index='dog', num_legs=4, num_wings=0)
Animal(Index='hawk', num_legs=2, num_wings=2)
__len__() → int
Returns length of info axis, but here we use the index.
dot(other)
Compute the matrix multiplication between the DataFrame and other.
This method computes the matrix product between the DataFrame and the values of an other Series, DataFrame or a numpy array.
It can also be called using self @ other.
other(Series, DataFrame or array-like) — The other object to compute the matrix product with.
If other is a Series, return the matrix product between self andother as a Series. If other is a DataFrame or a numpy.array, return the matrix product of self and other in a DataFrame of a np.array.
Series.dot: Similar method for Series.
Notes
The dimensions of DataFrame and other must be compatible in order to compute the matrix multiplication. In addition, the column names of DataFrame and the index of other must contain the same values, as they will be aligned prior to the multiplication.
The dot method for Series computes the inner product, instead of the matrix product here.
Here we multiply a DataFrame with a Series.
>>> df = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
>>> s = pd.Series([1, 1, 2, 1])
>>> df.dot(s)
0 -4
1 5
dtype: int64
Here we multiply a DataFrame with another DataFrame.
>>> other = pd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(other)
0 1
0 1 4
1 2 2
Note that the dot method give the same result as @
>>> df @ other
0 1
0 1 4
1 2 2
The dot method works also if other is an np.array.
>>> arr = np.array([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(arr)
0 1
0 1 4
1 2 2
Note how shuffling of the objects does not change the result.
>>> s2 = s.reindex([1, 0, 2, 3])
>>> df.dot(s2)
0 -4
1 5
dtype: int64
__matmul__(other) → pandas.core.frame.dataframe | pandas.core.series.series
Matrix multiplication using binary @ operator.
__rmatmul__(other) → DataFrame
Matrix multiplication using binary @ operator.
from_dict(data, orient='columns', dtype=None, columns=None)
Construct DataFrame from dict of array-like or dicts.
Creates DataFrame object from dictionary by columns or by index allowing dtype specification.
data(dict) — Of the form {field : array-like} or {field : dict}.orient({'columns', 'index', 'tight'}, default 'columns') — The "orientation" of the data. If the keys of the passed dictshould be the columns of the resulting DataFrame, pass 'columns' (default). Otherwise if the keys should be rows, pass 'index'. If 'tight', assume a dict with keys ['index', 'columns', 'data', 'index_names', 'column_names'].
.. versionadded:: 1.4.0 'tight' as an allowed value for theorientargumentdtype(dtype, default None) — Data type to force after DataFrame construction, otherwise infer.columns(list, default None) — Column labels to use whenorient='index'. Raises a ValueErrorif used withorient='columns'ororient='tight'.
DataFrame.from_records : DataFrame from structured ndarray, sequence of tuples or dicts, or DataFrame. DataFrame : DataFrame object creation using constructor. DataFrame.to_dict : Convert the DataFrame to a dictionary.
By default the keys of the dict become the DataFrame columns:
>>> data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data)
col_1 col_2
0 3 a
1 2 b
2 1 c
3 0 d
Specify orient='index' to create the DataFrame using dictionary
keys as rows:
>>> data = {'row_1': [3, 2, 1, 0], 'row_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data, orient='index')
0 1 2 3
row_1 3 2 1 0
row_2 a b c d
When using the 'index' orientation, the column names can be specified manually:
>>> pd.DataFrame.from_dict(data, orient='index',
... columns=['A', 'B', 'C', 'D'])
A B C D
row_1 3 2 1 0
row_2 a b c d
Specify orient='tight' to create the DataFrame using a 'tight'
format:
>>> data = {'index': [('a', 'b'), ('a', 'c')],
... 'columns': [('x', 1), ('y', 2)],
... 'data': [[1, 3], [2, 4]],
... 'index_names': ['n1', 'n2'],
... 'column_names': ['z1', 'z2']}
>>> pd.DataFrame.from_dict(data, orient='tight')
z1 x y
z2 1 2
n1 n2
a b 1 3
c 2 4
to_numpy(dtype=None, copy=False, na_value=<no_default>)
Convert the DataFrame to a NumPy array.
By default, the dtype of the returned array will be the common NumPy
dtype of all types in the DataFrame. For example, if the dtypes are
float16 and float32, the results dtype will be float32.
This may require copying data and coercing values, which may be
expensive.
dtype(str or numpy.dtype, optional) — The dtype to pass to :meth:numpy.asarray.copy(bool, default False) — Whether to ensure that the returned value is not a view onanother array. Note thatcopy=Falsedoes not ensure thatto_numpy()is no-copy. Rather,copy=Trueensure that a copy is made, even if not strictly necessary.na_value(Any, optional) — The value to use for missing values. The default value dependsondtypeand the dtypes of the DataFrame columns.
Series.to_numpy : Similar method for Series.
>>> pd.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy()array([[1, 3],
[2, 4]])
With heterogeneous data, the lowest common type will have to be used.
>>> df = pd.DataFrame({"A": [1, 2], "B": [3.0, 4.5]})
>>> df.to_numpy()
array([[1. , 3. ],
[2. , 4.5]])
For a mix of numeric and non-numeric types, the output array will have object dtype.
>>> df['C'] = pd.date_range('2000', periods=2)
>>> df.to_numpy()
array([[1, 3.0, Timestamp('2000-01-01 00:00:00')],
[2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object)
to_dict(orient='dict', into=<class 'dict'>, index=True)
Convert the DataFrame to a dictionary.
The type of the key-value pairs can be customized with the parameters (see below).
orient(str {'dict', 'list', 'series', 'split', 'tight', 'records', 'index'}) — Determines the type of the values of the dictionary.- 'dict' (default) : dict like {column -> {index -> value}}
- 'list' : dict like {column -> [values]}
- 'series' : dict like {column -> Series(values)}
- 'split' : dict like {'index' -> [index], 'columns' -> [columns], 'data' -> [values]}
- 'tight' : dict like {'index' -> [index], 'columns' -> [columns], 'data' -> [values], 'index_names' -> [index.names], 'column_names' -> [column.names]}
- 'records' : list like [{column -> value}, ... , {column -> value}]
- 'index' : dict like {index -> {column -> value}}
orientargumentinto(class, default dict) — The collections.abc.MutableMapping subclass used for all Mappingsin the return value. Can be the actual class or an empty instance of the mapping type you want. If you want a collections.defaultdict, you must pass it initialized.index(bool, default True) — Whether to include the index item (and index_names item iforientis 'tight') in the returned dictionary. Can only beFalsewhenorientis 'split' or 'tight'.
.. versionadded:: 2.0.0
Return a collections.abc.MutableMapping object representing theDataFrame. The resulting transformation depends on the orient
parameter.
DataFrame.from_dict: Create a DataFrame from a dictionary.DataFrame.to_json: Convert a DataFrame to JSON format.
>>> df = pd.DataFrame({'col1': [1, 2],... 'col2': [0.5, 0.75]},
... index=['row1', 'row2'])
>>> df
col1 col2
row1 1 0.50
row2 2 0.75
>>> df.to_dict()
{'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}
You can specify the return orientation.
>>> df.to_dict('series')
{'col1': row1 1
row2 2
Name: col1, dtype: int64,
'col2': row1 0.50
row2 0.75
Name: col2, dtype: float64}
>>> df.to_dict('split')
{'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
'data': [[1, 0.5], [2, 0.75]]}
>>> df.to_dict('records')
[{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}]
>>> df.to_dict('index')
{'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}
>>> df.to_dict('tight')
{'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
'data': [[1, 0.5], [2, 0.75]], 'index_names': [None], 'column_names': [None]}
You can also specify the mapping type.
>>> from collections import OrderedDict, defaultdict
>>> df.to_dict(into=OrderedDict)
OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])),
('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])
If you want a defaultdict, you need to initialize it:
>>> dd = defaultdict(list)
>>> df.to_dict('records', into=dd)
[defaultdict(<class 'list'>, {'col1': 1, 'col2': 0.5}),
defaultdict(<class 'list'>, {'col1': 2, 'col2': 0.75})]
to_gbq(destination_table, project_id=None, chunksize=None, reauth=False, if_exists='fail', auth_local_webserver=True, table_schema=None, location=None, progress_bar=True, credentials=None)
Write a DataFrame to a Google BigQuery table.
.. deprecated:: 2.2.0
Please use pandas_gbq.to_gbq instead.
This function requires the pandas-gbq package
<https://pandas-gbq.readthedocs.io>__.
See the How to authenticate with Google BigQuery
<https://pandas-gbq.readthedocs.io/en/latest/howto/authentication.html>__
guide for authentication instructions.
destination_table(str) — Name of table to be written, in the formdataset.tablename.project_id(str, optional) — Google BigQuery Account project ID. Optional when available fromthe environment.chunksize(int, optional) — Number of rows to be inserted in each chunk from the dataframe.Set toNoneto load the whole dataframe at once.reauth(bool, default False) — Force Google BigQuery to re-authenticate the user. This is usefulif multiple accounts are used.if_exists(str, default 'fail') — Behavior when the destination table exists. Value can be one of:'fail'If table exists raise pandasgbq.gbq.TableCreationError.'replace'If table exists, drop it, recreate it, and insert data.'append'If table exists, insert data. Create if does not exist.auth_local_webserver(bool, default True) — Use thelocal webserver flowinstead of theconsole flowwhen getting user credentials.
.. _local webserver flow: https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server .. _console flow: https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console
New in version 0.2.0 of pandas-gbq.
.. versionchanged:: 1.5.0 Default value is changed toTrue. Google has deprecated theauth_local_webserver = False"out of band" (copy-paste) flow <https://developers.googleblog.com/2022/02/making-oauth-flows-safer.html?m=1#disallowed-oob>_.table_schema(list of dicts, optional) — List of BigQuery table fields to which according DataFramecolumns conform to, e.g.[{'name': 'col1', 'type': 'STRING'},...]. If schema is not provided, it will be generated according to dtypes of DataFrame columns. See BigQuery API documentation on available names of a field.
New in version 0.3.1 of pandas-gbq.location(str, optional) — Location where the load job should run. See theBigQuery locationsdocumentation <https://cloud.google.com/bigquery/docs/dataset-locations>__ for a list of available locations. The location must match that of the target dataset.
New in version 0.5.0 of pandas-gbq.progress_bar(bool, default True) — Use the librarytqdmto show the progress bar for the upload,chunk by chunk.
New in version 0.5.0 of pandas-gbq.credentials(google.auth.credentials.Credentials, optional) — Credentials for accessing Google APIs. Use this parameter tooverride default credentials, such as to use Compute Engine :class:google.auth.compute_engine.Credentialsor Service Account :class:google.oauth2.service_account.Credentialsdirectly.
New in version 0.8.0 of pandas-gbq.
pandas_gbq.to_gbq : This function in the pandas-gbq library.read_gbq : Read a DataFrame from Google BigQuery.
Example taken from Google BigQuery documentation<https://cloud.google.com/bigquery/docs/samples/bigquery-pandas-gbq-to-gbq-simple>_
>>> project_id = "my-project"
>>> table_id = 'my_dataset.my_table'
>>> df = pd.DataFrame({
... "my_string": ["a", "b", "c"],
... "my_int64": [1, 2, 3],
... "my_float64": [4.0, 5.0, 6.0],
... "my_bool1": [True, False, True],
... "my_bool2": [False, True, False],
... "my_dates": pd.date_range("now", periods=3),
... }
... )
>>> df.to_gbq(table_id, project_id=project_id) # doctest: +SKIP
from_records(data, index=None, exclude=None, columns=None, coerce_float=False, nrows=None)
Convert structured or record ndarray to DataFrame.
Creates a DataFrame object from a structured ndarray, sequence of tuples or dicts, or DataFrame.
data(structured ndarray, sequence of tuples or dicts, or DataFrame) — Structured input data.
.. deprecated:: 2.1.0 Passing a DataFrame is deprecated.index(str, list of fields, array-like) — Field of array to use as the index, alternately a specific set ofinput labels to use.exclude(sequence, default None) — Columns or fields to exclude.columns(sequence, default None) — Column names to use. If the passed data do not have namesassociated with them, this argument provides names for the columns. Otherwise this argument indicates the order of the columns in the result (any names not found in the data will become all-NA columns).coerce_float(bool, default False) — Attempt to convert values of non-string, non-numeric objects (likedecimal.Decimal) to floating point, useful for SQL result sets.nrows(int, default None) — Number of rows to read if data is an iterator.
DataFrame.from_dict : DataFrame from dict of array-like or dicts.DataFrame : DataFrame object creation using constructor.
Data can be provided as a structured ndarray:
>>> data = np.array([(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')],
... dtype=[('col_1', 'i4'), ('col_2', 'U1')])
>>> pd.DataFrame.from_records(data)
col_1 col_2
0 3 a
1 2 b
2 1 c
3 0 d
Data can be provided as a list of dicts:
>>> data = [{'col_1': 3, 'col_2': 'a'},
... {'col_1': 2, 'col_2': 'b'},
... {'col_1': 1, 'col_2': 'c'},
... {'col_1': 0, 'col_2': 'd'}]
>>> pd.DataFrame.from_records(data)
col_1 col_2
0 3 a
1 2 b
2 1 c
3 0 d
Data can be provided as a list of tuples with corresponding columns:
>>> data = [(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')]
>>> pd.DataFrame.from_records(data, columns=['col_1', 'col_2'])
col_1 col_2
0 3 a
1 2 b
2 1 c
3 0 d
to_records(index=True, column_dtypes=None, index_dtypes=None)
Convert DataFrame to a NumPy record array.
Index will be included as the first field of the record array if requested.
index(bool, default True) — Include index in resulting record array, stored in 'index'field or using the index label, if set.column_dtypes(str, type, dict, default None) — If a string or type, the data type to store all columns. Ifa dictionary, a mapping of column names and indices (zero-indexed) to specific data types.index_dtypes(str, type, dict, default None) — If a string or type, the data type to store all index levels. Ifa dictionary, a mapping of index level names and indices (zero-indexed) to specific data types.
This mapping is applied only ifindex=True.
NumPy ndarray with the DataFrame labels as fields and each rowof the DataFrame as entries.
DataFrame.from_records: Convert structured or record ndarray to DataFrame. numpy.rec.recarray: An ndarray that allows field access using attributes, analogous to typed columns in a spreadsheet.
>>> df = pd.DataFrame({'A': [1, 2], 'B': [0.5, 0.75]},... index=['a', 'b'])
>>> df
A B
a 1 0.50
b 2 0.75
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
dtype=[('index', 'O'), ('A', '<i8'), ('B', '<f8')])
If the DataFrame index has no label then the recarray field name is set to 'index'. If the index has a label then this is used as the field name:
>>> df.index = df.index.rename("I")
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
dtype=[('I', 'O'), ('A', '<i8'), ('B', '<f8')])
The index can be excluded from the record array:
>>> df.to_records(index=False)
rec.array([(1, 0.5 ), (2, 0.75)],
dtype=[('A', '<i8'), ('B', '<f8')])
Data types can be specified for the columns:
>>> df.to_records(column_dtypes={"A": "int32"})
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
dtype=[('I', 'O'), ('A', '<i4'), ('B', '<f8')])
As well as for the index:
>>> df.to_records(index_dtypes="<S2")
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
dtype=[('I', 'S2'), ('A', '<i8'), ('B', '<f8')])
>>> index_dtypes = f"<S{df.index.str.len().max()}"
>>> df.to_records(index_dtypes=index_dtypes)
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
dtype=[('I', 'S1'), ('A', '<i8'), ('B', '<f8')])
to_stata(path, convert_dates=None, write_index=True, byteorder=None, time_stamp=None, data_label=None, variable_labels=None, version=114, convert_strl=None, compression='infer', storage_options=None, value_labels=None)
Export DataFrame object to Stata dta format.
Writes the DataFrame to a Stata dataset file. "dta" files contain a Stata dataset.
path(str, path object, or buffer) — String, path object (implementingos.PathLike[str]), or file-likeobject implementing a binarywrite()function.convert_dates(dict) — Dictionary mapping columns containing datetime types to statainternal format to use when writing the dates. Options are 'tc', 'td', 'tm', 'tw', 'th', 'tq', 'ty'. Column can be either an integer or a name. Datetime columns that do not have a conversion type specified will be converted to 'tc'. Raises NotImplementedError if a datetime column has timezone information.write_index(bool) — Write the index to Stata dataset.byteorder(str) — Can be ">", "<", "little", or "big". default issys.byteorder.time_stamp(datetime) — A datetime to use as file creation date. Default is the currenttime.data_label(str, optional) — A label for the data set. Must be 80 characters or smaller.variable_labels(dict) — Dictionary containing columns as keys and variable labels asvalues. Each label must be 80 characters or smaller.version({114, 117, 118, 119, None}, default 114) — Version to use in the output dta file. Set to None to let pandasdecide between 118 or 119 formats depending on the number of columns in the frame. Version 114 can be read by Stata 10 and later. Version 117 can be read by Stata 13 or later. Version 118 is supported in Stata 14 and later. Version 119 is supported in Stata 15 and later. Version 114 limits string variables to 244 characters or fewer while versions 117 and later allow strings with lengths up to 2,000,000 characters. Versions 118 and 119 support Unicode characters, and version 119 supports more than 32,767 variables.
Version 119 should usually only be used when the number of variables exceeds the capacity of dta format 118. Exporting smaller datasets in format 119 may have unintended consequences, and, as of November 2020, Stata SE cannot read version 119 files.convert_strl(list, optional) — List of column names to convert to string columns to Stata StrLformat. Only available if version is 117. Storing strings in the StrL format can produce smaller dta files if strings have more than 8 characters and values are repeated.compression(str or dict, default 'infer') — For on-the-fly compression of the output data. If 'infer' and 'path' ispath-like, then detect compression from the following extensions: '.gz', '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2' (otherwise no compression). Set toNonefor no compression. Can also be a dict with key'method'set to one of {'zip','gzip','bz2','zstd','xz','tar'} and other key-value pairs are forwarded tozipfile.ZipFile,gzip.GzipFile,bz2.BZ2File,zstandard.ZstdCompressor,lzma.LZMAFileortarfile.TarFile, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive:compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}.
.. versionadded:: 1.5.0 Added support for.tarfiles.
.. versionchanged:: 1.4.0 Zstandard support.storage_options(dict, optional) — Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded tourllib.request.Requestas header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded tofsspec.open. Please seefsspecandurllibfor more details, and for more examples on storage options referhere <https://pandas.pydata.org/docs/user_guide/io.html? highlight=storage_options#reading-writing-remote-files>_.value_labels(dict of dicts) — Dictionary containing columns as keys and dictionaries of column valueto labels as values. Labels for a single variable must be 32,000 characters or smaller.
.. versionadded:: 1.4.0
NotImplementedError—- If datetimes contain timezone information
- Column dtype is not representable in Stata
ValueError—- Columns listed in convert_dates are neither datetime64[ns] or datetime.datetime
- Column listed in convert_dates is not in DataFrame
- Categorical label contains more than 32,000 characters
read_stata : Import Stata data files.io.stata.StataWriter : Low-level writer for Stata data files. io.stata.StataWriter117 : Low-level writer for version 117 files.
>>> df = pd.DataFrame({'animal': ['falcon', 'parrot', 'falcon',... 'parrot'],
... 'speed': [350, 18, 361, 15]})
>>> df.to_stata('animals.dta') # doctest: +SKIP
to_feather(path, **kwargs)
Write a DataFrame to the binary Feather format.
path(str, path object, file-like object) — String, path object (implementingos.PathLike[str]), or file-likeobject implementing a binarywrite()function. If a string or a path, it will be used as Root Directory path when writing a partitioned dataset.**kwargs— Additional keywords passed to :func:pyarrow.feather.write_feather.This includes thecompression,compression_level,chunksizeandversionkeywords.
Notes
This function writes the dataframe as a feather file
<https://arrow.apache.org/docs/python/feather.html>_. Requires a default
index. For saving the DataFrame with your custom index use a method that
supports custom indices e.g. to_parquet.
>>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]])>>> df.to_feather("file.feather") # doctest: +SKIP
to_markdown(buf=None, mode='wt', index=True, storage_options=None, **kwargs)
Print DataFrame in Markdown-friendly format.
buf(str, Path or StringIO-like, optional, default None) — Buffer to write to. If None, the output is returned as a string.mode(str, optional) — Mode in which file is opened, "wt" by default.index(bool, optional, default True) — Add index (row) labels.storage_options(dict, optional) — Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded tourllib.request.Requestas header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded tofsspec.open. Please seefsspecandurllibfor more details, and for more examples on storage options referhere <https://pandas.pydata.org/docs/user_guide/io.html? highlight=storage_options#reading-writing-remote-files>_.**kwargs— These parameters will be passed totabulate <https://pypi.org/project/tabulate>_.
DataFrame in Markdown-friendly format.
Notes
Requires the tabulate <https://pypi.org/project/tabulate>_ package.
>>> df = pd.DataFrame(... data={"animal_1": ["elk", "pig"], "animal_2": ["dog", "quetzal"]}
... )
>>> print(df.to_markdown())
| | animal_1 | animal_2 |
|---:|:-----------|:-----------|
| 0 | elk | dog |
| 1 | pig | quetzal |
Output markdown with a tabulate option.
>>> print(df.to_markdown(tablefmt="grid"))
+----+------------+------------+
| | animal_1 | animal_2 |
+====+============+============+
| 0 | elk | dog |
+----+------------+------------+
| 1 | pig | quetzal |
+----+------------+------------+
to_parquet(path=None, engine='auto', compression='snappy', index=None, partition_cols=None, storage_options=None, **kwargs)
Write a DataFrame to the binary parquet format.
This function writes the dataframe as a parquet file
<https://parquet.apache.org/>_. You can choose different parquet
backends, and have the option of compression. See
:ref:the user guide <io.parquet> for more details.
path(str, path object, file-like object, or None, default None) — String, path object (implementingos.PathLike[str]), or file-likeobject implementing a binarywrite()function. If None, the result is returned as bytes. If a string or path, it will be used as Root Directory path when writing a partitioned dataset.engine({'auto', 'pyarrow', 'fastparquet'}, default 'auto') — Parquet library to use. If 'auto', then the optionio.parquet.engineis used. The defaultio.parquet.enginebehavior is to try 'pyarrow', falling back to 'fastparquet' if 'pyarrow' is unavailable.compression(str or None, default 'snappy') — Name of the compression to use. UseNonefor no compression.Supported options: 'snappy', 'gzip', 'brotli', 'lz4', 'zstd'.index(bool, default None) — IfTrue, include the dataframe's index(es) in the file output.IfFalse, they will not be written to the file. IfNone, similar toTruethe dataframe's index(es) will be saved. However, instead of being saved as values, the RangeIndex will be stored as a range in the metadata so it doesn't require much space and is faster. Other indexes will be included as columns in the file output.partition_cols(list, optional, default None) — Column names by which to partition the dataset.Columns are partitioned in the order they are given. Must be None if path is not a string.storage_options(dict, optional) — Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded tourllib.request.Requestas header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded tofsspec.open. Please seefsspecandurllibfor more details, and for more examples on storage options referhere <https://pandas.pydata.org/docs/user_guide/io.html? highlight=storage_options#reading-writing-remote-files>_.**kwargs— Additional arguments passed to the parquet library. See:ref:pandas io <io.parquet>for more details.
read_parquet : Read a parquet file.DataFrame.to_orc : Write an orc file. DataFrame.to_csv : Write a csv file. DataFrame.to_sql : Write to a sql table. DataFrame.to_hdf : Write to hdf.
Notes
This function requires either the fastparquet
<https://pypi.org/project/fastparquet> or pyarrow
<https://arrow.apache.org/docs/python/> library.
>>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]})>>> df.to_parquet('df.parquet.gzip',
... compression='gzip') # doctest: +SKIP
>>> pd.read_parquet('df.parquet.gzip') # doctest: +SKIP
col1 col2
0 1 3
1 2 4
If you want to get a buffer to the parquet content you can use a io.BytesIO object, as long as you don't use partition_cols, which creates multiple files.
>>> import io
>>> f = io.BytesIO()
>>> df.to_parquet(f)
>>> f.seek(0)
0
>>> content = f.read()
to_orc(path=None, engine='pyarrow', index=None, engine_kwargs=None)
Write a DataFrame to the ORC format.
.. versionadded:: 1.5.0
path(str, file-like object or None, default None) — If a string, it will be used as Root Directory pathwhen writing a partitioned dataset. By file-like object, we refer to objects with a write() method, such as a file handle (e.g. via builtin open function). If path is None, a bytes object is returned.engine({'pyarrow'}, default 'pyarrow') — ORC library to use.index(bool, optional) — IfTrue, include the dataframe's index(es) in the file output.IfFalse, they will not be written to the file. IfNone, similar toinferthe dataframe's index(es) will be saved. However, instead of being saved as values, the RangeIndex will be stored as a range in the metadata so it doesn't require much space and is faster. Other indexes will be included as columns in the file output.engine_kwargs(dict[str, Any] or None, default None) — Additional keyword arguments passed to :func:pyarrow.orc.write_table.
NotImplementedError— Dtype of one or more columns is category, unsigned integers, interval,period or sparse.ValueError— engine is not pyarrow.
read_orc : Read a ORC file.DataFrame.to_parquet : Write a parquet file. DataFrame.to_csv : Write a csv file. DataFrame.to_sql : Write to a sql table. DataFrame.to_hdf : Write to hdf.
Notes
- Before using this function you should read the :ref:
user guide about ORC <io.orc>and :ref:install optional dependencies <install.warn_orc>. - This function requires
pyarrow <https://arrow.apache.org/docs/python/>_ library. - For supported dtypes please refer to
supported ORC features in Arrow <https://arrow.apache.org/docs/cpp/orc.html#data-types>__. - Currently timezones in datetime columns are not preserved when a dataframe is converted into ORC files.
>>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [4, 3]})>>> df.to_orc('df.orc') # doctest: +SKIP
>>> pd.read_orc('df.orc') # doctest: +SKIP
col1 col2
0 1 4
1 2 3
If you want to get a buffer to the orc content you can write it to io.BytesIO
>>> import io
>>> b = io.BytesIO(df.to_orc()) # doctest: +SKIP
>>> b.seek(0) # doctest: +SKIP
0
>>> content = b.read() # doctest: +SKIP
to_html(buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None, max_rows=None, max_cols=None, show_dimensions=False, decimal='.', bold_rows=True, classes=None, escape=True, notebook=False, border=None, table_id=None, render_links=False, encoding=None)
Render a DataFrame as an HTML table.
buf(str, Path or StringIO-like, optional, default None) — Buffer to write to. If None, the output is returned as a string.columns(array-like, optional, default None) — The subset of columns to write. Writes all columns by default.col_space(str or int, list or dict of int or str, optional) — The minimum width of each column in CSS length units. An int is assumed to be px units..header(bool, optional) — Whether to print column labels, default True.index(bool, optional, default True) — Whether to print index (row) labels.na_rep(str, optional, default 'NaN') — String representation ofNaNto use.formatters(list, tuple or dict of one-param. functions, optional) — Formatter functions to apply to columns' elements by position orname. The result of each function must be a unicode string. List/tuple must be of length equal to the number of columns.float_format(one-parameter function, optional, default None) — Formatter function to apply to columns' elements if they arefloats. This function must return a unicode string and will be applied only to the non-NaNelements, withNaNbeing handled byna_rep.sparsify(bool, optional, default True) — Set to False for a DataFrame with a hierarchical index to printevery multiindex key at each row.index_names(bool, optional, default True) — Prints the names of the indexes.justify(str, default None) — How to justify the column labels. If None uses the option fromthe print configuration (controlled by set_option), 'right' out of the box. Valid values are
- left
- right
- center
- justify
- justify-all
- start
- end
- inherit
- match-parent
- initial
- unset.
max_rows(int, optional) — Maximum number of rows to display in the console.max_cols(int, optional) — Maximum number of columns to display in the console.show_dimensions(bool, default False) — Display DataFrame dimensions (number of rows by number of columns).decimal(str, default '.') — Character recognized as decimal separator, e.g. ',' in Europe.bold_rows(bool, default True) — Make the row labels bold in the output.classes(str or list or tuple, default None) — CSS class(es) to apply to the resulting html table.escape(bool, default True) — Convert the characters <, >, and & to HTML-safe sequences.notebook({True, False}, default False) — Whether the generated HTML is for IPython Notebook.border(int) — Aborder=borderattribute is included in the opening<table>tag. Defaultpd.options.display.html.border.table_id(str, optional) — A css id is included in the opening<table>tag if specified.render_links(bool, default False) — Convert URLs to HTML links.encoding(str, default "utf-8") — Set character encoding.
If buf is None, returns the result as a string. Otherwise returnsNone.
to_string : Convert DataFrame to a string.
>>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [4, 3]})>>> html_string = '''<table border="1" class="dataframe">
... <thead>
... <tr style="text-align: right;">
... <th></th>
... <th>col1</th>
... <th>col2</th>
... </tr>
... </thead>
... <tbody>
... <tr>
... <th>0</th>
... <td>1</td>
... <td>4</td>
... </tr>
... <tr>
... <th>1</th>
... <td>2</td>
... <td>3</td>
... </tr>
... </tbody>
... </table>'''
>>> assert html_string == df.to_html()
to_xml(path_or_buffer=None, index=True, root_name='data', row_name='row', na_rep=None, attr_cols=None, elem_cols=None, namespaces=None, prefix=None, encoding='utf-8', xml_declaration=True, pretty_print=True, parser='lxml', stylesheet=None, compression='infer', storage_options=None)
Render a DataFrame to an XML document.
.. versionadded:: 1.3.0
path_or_buffer(str, path object, file-like object, or None, default None) — String, path object (implementingos.PathLike[str]), or file-likeobject implementing awrite()function. If None, the result is returned as a string.index(bool, default True) — Whether to include index in XML document.root_name(str, default 'data') — The name of root element in XML document.row_name(str, default 'row') — The name of row element in XML document.na_rep(str, optional) — Missing data representation.attr_cols(list-like, optional) — List of columns to write as attributes in row element.Hierarchical columns will be flattened with underscore delimiting the different levels.elem_cols(list-like, optional) — List of columns to write as children in row element. By default,all columns output as children of row element. Hierarchical columns will be flattened with underscore delimiting the different levels.namespaces(dict, optional) — All namespaces to be defined in root element. Keys of dictshould be prefix names and values of dict corresponding URIs. Default namespaces should be given empty string key. For example, ::
namespaces = {"": "https://example.com"}prefix(str, optional) — Namespace prefix to be used for every element and/or attributein document. This should be one of the keys innamespacesdict.encoding(str, default 'utf-8') — Encoding of the resulting document.xml_declaration(bool, default True) — Whether to include the XML declaration at start of document.pretty_print(bool, default True) — Whether output should be pretty printed with indentation andline breaks.parser({'lxml','etree'}, default 'lxml') — Parser module to use for building of tree. Only 'lxml' and'etree' are supported. With 'lxml', the ability to use XSLT stylesheet is supported.stylesheet(str, path object or file-like object, optional) — A URL, file-like object, or a raw string containing an XSLTscript used to transform the raw XML output. Script should use layout of elements and attributes from original output. This argument requireslxmlto be installed. Only XSLT 1.0 scripts and not later versions is currently supported.compression(str or dict, default 'infer') — For on-the-fly compression of the output data. If 'infer' and 'path_or_buffer' ispath-like, then detect compression from the following extensions: '.gz', '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2' (otherwise no compression). Set toNonefor no compression. Can also be a dict with key'method'set to one of {'zip','gzip','bz2','zstd','xz','tar'} and other key-value pairs are forwarded tozipfile.ZipFile,gzip.GzipFile,bz2.BZ2File,zstandard.ZstdCompressor,lzma.LZMAFileortarfile.TarFile, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive:compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}.
.. versionadded:: 1.5.0 Added support for.tarfiles.
.. versionchanged:: 1.4.0 Zstandard support.storage_options(dict, optional) — Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded tourllib.request.Requestas header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded tofsspec.open. Please seefsspecandurllibfor more details, and for more examples on storage options referhere <https://pandas.pydata.org/docs/user_guide/io.html? highlight=storage_options#reading-writing-remote-files>_.
If io is None, returns the resulting XML format as astring. Otherwise returns None.
to_json : Convert the pandas object to a JSON string.to_html : Convert DataFrame to a html.
>>> df = pd.DataFrame({'shape': ['square', 'circle', 'triangle'],... 'degrees': [360, 360, 180],
... 'sides': [4, np.nan, 3]})
>>> df.to_xml() # doctest: +SKIP
<?xml version='1.0' encoding='utf-8'?>
<data>
<row>
<index>0</index>
<shape>square</shape>
<degrees>360</degrees>
<sides>4.0</sides>
</row>
<row>
<index>1</index>
<shape>circle</shape>
<degrees>360</degrees>
<sides/>
</row>
<row>
<index>2</index>
<shape>triangle</shape>
<degrees>180</degrees>
<sides>3.0</sides>
</row>
</data>
>>> df.to_xml(attr_cols=[
... 'index', 'shape', 'degrees', 'sides'
... ]) # doctest: +SKIP
<?xml version='1.0' encoding='utf-8'?>
<data>
<row index="0" shape="square" degrees="360" sides="4.0"/>
<row index="1" shape="circle" degrees="360"/>
<row index="2" shape="triangle" degrees="180" sides="3.0"/>
</data>
>>> df.to_xml(namespaces={"doc": "https://example.com"},
... prefix="doc") # doctest: +SKIP
<?xml version='1.0' encoding='utf-8'?>
<doc:data xmlns:doc="https://example.com">
<doc:row>
<doc:index>0</doc:index>
<doc:shape>square</doc:shape>
<doc:degrees>360</doc:degrees>
<doc:sides>4.0</doc:sides>
</doc:row>
<doc:row>
<doc:index>1</doc:index>
<doc:shape>circle</doc:shape>
<doc:degrees>360</doc:degrees>
<doc:sides/>
</doc:row>
<doc:row>
<doc:index>2</doc:index>
<doc:shape>triangle</doc:shape>
<doc:degrees>180</doc:degrees>
<doc:sides>3.0</doc:sides>
</doc:row>
</doc:data>
info(verbose=None, buf=None, max_cols=None, memory_usage=None, show_counts=None)
Print a concise summary of a DataFrame.
This method prints information about a DataFrame including the index dtype and columns, non-null values and memory usage.
verbose(bool, optional) — Whether to print the full summary. By default, the setting inpandas.options.display.max_info_columnsis followed.buf(writable buffer, defaults to sys.stdout) — Where to send the output. By default, the output is printed tosys.stdout. Pass a writable buffer if you need to further process the output.max_cols(int, optional) — When to switch from the verbose to the truncated output. If theDataFrame has more thanmax_colscolumns, the truncated output is used. By default, the setting inpandas.options.display.max_info_columnsis used.memory_usage(bool, str, optional) — Specifies whether total memory usage of the DataFrameelements (including the index) should be displayed. By default, this follows thepandas.options.display.memory_usagesetting.
True always show memory usage. False never shows memory usage. A value of 'deep' is equivalent to "True with deep introspection". Memory usage is shown in human-readable units (base-2 representation). Without deep introspection a memory estimation is made based in column dtype and number of rows assuming values consume the same memory amount for corresponding dtypes. With deep memory introspection, a real memory usage calculation is performed at the cost of computational resources. See the :ref:Frequently Asked Questions <df-memory-usage>for more details.show_counts(bool, optional) — Whether to show the non-null counts. By default, this is shownonly if the DataFrame is smaller thanpandas.options.display.max_info_rowsandpandas.options.display.max_info_columns. A value of True always shows the counts, and False never shows the counts.
This method prints a summary of a DataFrame and returns None.
DataFrame.describe: Generate descriptive statistics of DataFrame columns. DataFrame.memory_usage: Memory usage of DataFrame columns.
>>> int_values = [1, 2, 3, 4, 5]>>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
>>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
>>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,
... "float_col": float_values})
>>> df
int_col text_col float_col
0 1 alpha 0.00
1 2 beta 0.25
2 3 gamma 0.50
3 4 delta 0.75
4 5 epsilon 1.00
Prints information of all columns:
>>> df.info(verbose=True)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 int_col 5 non-null int64
1 text_col 5 non-null object
2 float_col 5 non-null float64
dtypes: float64(1), int64(1), object(1)
memory usage: 248.0+ bytes
Prints a summary of columns count and its dtypes but not per column information:
>>> df.info(verbose=False)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Columns: 3 entries, int_col to float_col
dtypes: float64(1), int64(1), object(1)
memory usage: 248.0+ bytes
Pipe output of DataFrame.info to buffer instead of sys.stdout, get buffer content and writes to a text file:
>>> import io
>>> buffer = io.StringIO()
>>> df.info(buf=buffer)
>>> s = buffer.getvalue()
>>> with open("df_info.txt", "w",
... encoding="utf-8") as f: # doctest: +SKIP
... f.write(s)
260
The memory_usage parameter allows deep introspection mode, specially
useful for big DataFrames and fine-tune memory optimization:
>>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
>>> df = pd.DataFrame({
... 'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
... 'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
... 'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
... })
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 column_1 1000000 non-null object
1 column_2 1000000 non-null object
2 column_3 1000000 non-null object
dtypes: object(3)
memory usage: 22.9+ MB
>>> df.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 column_1 1000000 non-null object
1 column_2 1000000 non-null object
2 column_3 1000000 non-null object
dtypes: object(3)
memory usage: 165.9 MB
memory_usage(index=True, deep=False)
Return the memory usage of each column in bytes.
The memory usage can optionally include the contribution of
the index and elements of object dtype.
This value is displayed in DataFrame.info by default. This can be
suppressed by setting pandas.options.display.memory_usage to False.
index(bool, default True) — Specifies whether to include the memory usage of the DataFrame'sindex in returned Series. Ifindex=True, the memory usage of the index is the first item in the output.deep(bool, default False) — If True, introspect the data deeply by interrogatingobjectdtypes for system-level memory consumption, and include it in the returned values.
A Series whose index is the original column names and whose valuesis the memory usage of each column in bytes.
numpy.ndarray.nbytes : Total bytes consumed by the elements of an ndarray. Series.memory_usage : Bytes consumed by a Series. Categorical : Memory-efficient array for string values with many repeated values. DataFrame.info : Concise summary of a DataFrame.
Notes
See the :ref:Frequently Asked Questions <df-memory-usage> for more
details.
>>> dtypes = ['int64', 'float64', 'complex128', 'object', 'bool']>>> data = dict([(t, np.ones(shape=5000, dtype=int).astype(t))
... for t in dtypes])
>>> df = pd.DataFrame(data)
>>> df.head()
int64 float64 complex128 object bool
0 1 1.0 1.0+0.0j 1 True
1 1 1.0 1.0+0.0j 1 True
2 1 1.0 1.0+0.0j 1 True
3 1 1.0 1.0+0.0j 1 True
4 1 1.0 1.0+0.0j 1 True
>>> df.memory_usage()
Index 128
int64 40000
float64 40000
complex128 80000
object 40000
bool 5000
dtype: int64
>>> df.memory_usage(index=False)
int64 40000
float64 40000
complex128 80000
object 40000
bool 5000
dtype: int64
The memory footprint of object dtype columns is ignored by default:
>>> df.memory_usage(deep=True)
Index 128
int64 40000
float64 40000
complex128 80000
object 180000
bool 5000
dtype: int64
Use a Categorical for efficient storage of an object-dtype column with many repeated values.
>>> df['object'].astype('category').memory_usage(deep=True)
5244
transpose(*args, copy=False)
Transpose index and columns.
Reflect the DataFrame over its main diagonal by writing rows as columns
and vice-versa. The property :attr:.T is an accessor to the method
:meth:transpose.
*args(tuple, optional) — Accepted for compatibility with NumPy.copy(bool, default False) — Whether to copy the data after transposing, even for DataFrameswith a single dtype.
Note that a copy is always required for mixed dtype DataFrames, or for DataFrames with any extension types.
.. note:: Thecopykeyword will change behavior in pandas 3.0.Copy-on-Write <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>__ will be enabled by default, which means that all methods with acopykeyword will use a lazy copy mechanism to defer the copy and ignore thecopykeyword. Thecopykeyword will be removed in a future version of pandas.
You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True``
The transposed DataFrame.
numpy.transpose : Permute the dimensions of a given array.
Notes
Transposing a DataFrame with mixed dtypes will result in a homogeneous
DataFrame with the object dtype. In such a case, a copy of the data
is always made.
Square DataFrame with homogeneous dtype
>>> d1 = {'col1': [1, 2], 'col2': [3, 4]}
>>> df1 = pd.DataFrame(data=d1)
>>> df1
col1 col2
0 1 3
1 2 4
>>> df1_transposed = df1.T # or df1.transpose()
>>> df1_transposed
0 1
col1 1 2
col2 3 4
When the dtype is homogeneous in the original DataFrame, we get a transposed DataFrame with the same dtype:
>>> df1.dtypes
col1 int64
col2 int64
dtype: object
>>> df1_transposed.dtypes
0 int64
1 int64
dtype: object
Non-square DataFrame with mixed dtypes
>>> d2 = {'name': ['Alice', 'Bob'],
... 'score': [9.5, 8],
... 'employed': [False, True],
... 'kids': [0, 0]}
>>> df2 = pd.DataFrame(data=d2)
>>> df2
name score employed kids
0 Alice 9.5 False 0
1 Bob 8.0 True 0
>>> df2_transposed = df2.T # or df2.transpose()
>>> df2_transposed
0 1
name Alice Bob
score 9.5 8.0
employed False True
kids 0 0
When the DataFrame has mixed dtypes, we get a transposed DataFrame with
the object dtype:
>>> df2.dtypes
name object
score float64
employed bool
kids int64
dtype: object
>>> df2_transposed.dtypes
0 object
1 object
dtype: object
isetitem(loc, value)
Set the given value in the column with position loc.
This is a positional analogue to __setitem__.
loc(int or sequence of ints) — Index position for the column.value(scalar or arraylike) — Value(s) for the column.
Notes
frame.isetitem(loc, value) is an in-place method as it will
modify the DataFrame in place (not returning a new object). In contrast to
frame.iloc[:, i] = value which will try to update the existing values in
place, frame.isetitem(loc, value) will not update the values of the column
itself in place, it will instead insert a new array.
In cases where frame.columns is unique, this is equivalent to
frame[frame.columns[i]] = value.
query(expr, inplace=False, **kwargs)
Query the columns of a DataFrame with a boolean expression.
expr(str) — The query string to evaluate.
You can refer to variables in the environment by prefixing them with an '@' character like@a + b.
You can refer to column names that are not valid Python variable names by surrounding them in backticks. Thus, column names containing spaces or punctuations (besides underscores) or starting with digits must be surrounded by backticks. (For example, a column named "Area (cm^2)" would be referenced asArea (cm^2)). Column names which are Python keywords (like "list", "for", "import", etc) cannot be used.
For example, if one of your columns is calleda aand you want to sum it withb, your query should be`a a` + b.inplace(bool) — Whether to modify the DataFrame rather than creating a new one.**kwargs— See the documentation for :func:evalfor complete detailson the keyword arguments accepted by :meth:DataFrame.query.
DataFrame resulting from the provided query expression orNone if inplace=True.
eval : Evaluate a string describing operations on DataFrame columns. DataFrame.eval : Evaluate a string describing operations on DataFrame columns.
Notes
The result of the evaluation of this expression is first passed to
:attr:DataFrame.loc and if that fails because of a
multidimensional key (e.g., a DataFrame) then the result will be passed
to :meth:DataFrame.__getitem__.
This method uses the top-level :func:eval function to
evaluate the passed query.
The :meth:~pandas.DataFrame.query method uses a slightly
modified Python syntax by default. For example, the & and |
(bitwise) operators have the precedence of their boolean cousins,
:keyword:and and :keyword:or. This is syntactically valid Python,
however the semantics are different.
You can change the semantics of the expression by passing the keyword
argument parser='python'. This enforces the same semantics as
evaluation in Python space. Likewise, you can pass engine='python'
to evaluate an expression using Python itself as a backend. This is not
recommended as it is inefficient compared to using numexpr as the
engine.
The :attr:DataFrame.index and
:attr:DataFrame.columns attributes of the
:class:~pandas.DataFrame instance are placed in the query namespace
by default, which allows you to treat both the index and columns of the
frame as a column in the frame.
The identifier index is used for the frame index; you can also
use the name of the index to identify it in a query. Please note that
Python keywords may not be used as identifiers.
For further details and examples see the query documentation in
:ref:indexing <indexing.query>.
Backtick quoted variables
Backtick quoted variables are parsed as literal Python code and are converted internally to a Python valid identifier. This can lead to the following problems.
During parsing a number of disallowed characters inside the backtick quoted string are replaced by strings that are allowed as a Python identifier. These characters include all operators in Python, the space character, the question mark, the exclamation mark, the dollar sign, and the euro sign. For other characters that fall outside the ASCII range (U+0001..U+007F) and those that are not further specified in PEP 3131, the query parser will raise an error. This excludes whitespace different than the space character, but also the hashtag (as it is used for comments) and the backtick itself (backtick can also not be escaped).
In a special case, quotes that make a pair around a backtick can
confuse the parser.
For example, it's` > `that's will raise an error,
as it forms a quoted string ('s > `that') with a backtick inside.
See also the Python documentation about lexical analysis
(https://docs.python.org/3/reference/lexical_analysis.html)
in combination with the source code in :mod:pandas.core.computation.parsing.
>>> df = pd.DataFrame({'A': range(1, 6),... 'B': range(10, 0, -2),
... 'C C': range(10, 5, -1)})
>>> df
A B C C
0 1 10 10
1 2 8 9
2 3 6 8
3 4 4 7
4 5 2 6
>>> df.query('A > B')
A B C C
4 5 2 6
The previous expression is equivalent to
>>> df[df.A > df.B]
A B C C
4 5 2 6
For columns with spaces in their name, you can use backtick quoting.
>>> df.query('B == `C C`')
A B C C
0 1 10 10
The previous expression is equivalent to
>>> df[df.B == df['C C']]
A B C C
0 1 10 10
eval(expr, inplace=False, **kwargs)
Evaluate a string describing operations on DataFrame columns.
Operates on columns only, not specific rows or elements. This allows
eval to run arbitrary code, which can make you vulnerable to code
injection if you pass user input to this function.
expr(str) — The expression string to evaluate.inplace(bool, default False) — If the expression contains an assignment, whether to perform theoperation inplace and mutate the existing DataFrame. Otherwise, a new DataFrame is returned.**kwargs— See the documentation for :func:evalfor complete detailson the keyword arguments accepted by :meth:~pandas.DataFrame.query.
The result of the evaluation or None if inplace=True.
DataFrame.query : Evaluates a boolean expression to query the columns of a frame. DataFrame.assign : Can evaluate an expression or function to create new values for a column. eval : Evaluate a Python expression as a string using various backends.
Notes
For more details see the API documentation for :func:~eval.
For detailed examples see :ref:enhancing performance with eval
<enhancingperf.eval>.
>>> df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})>>> df
A B
0 1 10
1 2 8
2 3 6
3 4 4
4 5 2
>>> df.eval('A + B')
0 11
1 10
2 9
3 8
4 7
dtype: int64
Assignment is allowed though by default the original DataFrame is not modified.
>>> df.eval('C = A + B')
A B C
0 1 10 11
1 2 8 10
2 3 6 9
3 4 4 8
4 5 2 7
>>> df
A B
0 1 10
1 2 8
2 3 6
3 4 4
4 5 2
Multiple columns can be assigned to using multi-line expressions:
>>> df.eval(
... '''
... C = A + B
... D = A - B
... '''
... )
A B C D
0 1 10 11 -9
1 2 8 10 -6
2 3 6 9 -3
3 4 4 8 0
4 5 2 7 3
select_dtypes(include=None, exclude=None)
Return a subset of the DataFrame's columns based on the column dtypes.
The subset of the frame including the dtypes in include andexcluding the dtypes in exclude.
ValueError—- If both of
includeandexcludeare empty - If
includeandexcludehave overlapping elements - If any kind of string dtype is passed in.
- If both of
DataFrame.dtypes: Return Series with the data type of each column.
Notes
- To select all numeric types, use
np.numberor'number' - To select strings you must use the
objectdtype, but note that this will return all object dtype columns - See the
numpy dtype hierarchy <https://numpy.org/doc/stable/reference/arrays.scalars.html>__ - To select datetimes, use
np.datetime64,'datetime'or'datetime64' - To select timedeltas, use
np.timedelta64,'timedelta'or'timedelta64' - To select Pandas categorical dtypes, use
'category' - To select Pandas datetimetz dtypes, use
'datetimetz'or'datetime64[ns, tz]'
>>> df = pd.DataFrame({'a': [1, 2] * 3,... 'b': [True, False] * 3,
... 'c': [1.0, 2.0] * 3})
>>> df
a b c
0 1 True 1.0
1 2 False 2.0
2 1 True 1.0
3 2 False 2.0
4 1 True 1.0
5 2 False 2.0
>>> df.select_dtypes(include='bool')
b
0 True
1 False
2 True
3 False
4 True
5 False
>>> df.select_dtypes(include=['float64'])
c
0 1.0
1 2.0
2 1.0
3 2.0
4 1.0
5 2.0
>>> df.select_dtypes(exclude=['int64'])
b c
0 True 1.0
1 False 2.0
2 True 1.0
3 False 2.0
4 True 1.0
5 False 2.0
insert(loc, column, value, allow_duplicates=<no_default>)
Insert column into DataFrame at specified location.
Raises a ValueError if column is already contained in the DataFrame,
unless allow_duplicates is set to True.
loc(int) — Insertion index. Must verify 0 <= loc <= len(columns).column(str, number, or hashable object) — Label of the inserted column.value(Scalar, Series, or array-like) — Content of the inserted column.allow_duplicates(bool, optional, default lib.no_default) — Allow duplicate column labels to be created.
Index.insert : Insert new item by index.
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})>>> df
col1 col2
0 1 3
1 2 4
>>> df.insert(1, "newcol", [99, 99])
>>> df
col1 newcol col2
0 1 99 3
1 2 99 4
>>> df.insert(0, "col1", [100, 100], allow_duplicates=True)
>>> df
col1 col1 newcol col2
0 100 1 99 3
1 100 2 99 4
Notice that pandas uses index alignment in case of value from type Series:
>>> df.insert(0, "col0", pd.Series([5, 6], index=[1, 2]))
>>> df
col0 col1 col1 newcol col2
0 NaN 100 1 99 3
1 5.0 100 2 99 4
assign(**kwargs)
Assign new columns to a DataFrame.
Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten.
**kwargs(dict of {str: callable or Series}) — The column names are keywords. If the values arecallable, they are computed on the DataFrame and assigned to the new columns. The callable must not change input DataFrame (though pandas doesn't check it). If the values are not callable, (e.g. a Series, scalar, or array), they are simply assigned.
A new DataFrame with the new columns in addition toall the existing columns.
Notes
Assigning multiple columns within the same assign is possible.
Later items in '**kwargs' may refer to newly created or modified
columns in 'df'; items are computed and assigned into 'df' in order.
>>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},... index=['Portland', 'Berkeley'])
>>> df
temp_c
Portland 17.0
Berkeley 25.0
Where the value is a callable, evaluated on df:
>>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
temp_c temp_f
Portland 17.0 62.6
Berkeley 25.0 77.0
Alternatively, the same behavior can be achieved by directly referencing an existing Series or sequence:
>>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
temp_c temp_f
Portland 17.0 62.6
Berkeley 25.0 77.0
You can create multiple columns within the same assign where one of the columns depends on another one defined within the same assign:
>>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
... temp_k=lambda x: (x['temp_f'] + 459.67) * 5 / 9)
temp_c temp_f temp_k
Portland 17.0 62.6 290.15
Berkeley 25.0 77.0 298.15
set_axis(labels, axis=0, copy=None)
Assign desired index to given axis.
Indexes for column or row labels can be changed by assigning a list-like or Index.
labels(list-like, Index) — The values for the new index.axis({0 or 'index', 1 or 'columns'}, default 0) — The axis to update. The value 0 identifies the rows. ForSeriesthis parameter is unused and defaults to 0.copy(bool, default True) — Whether to make a copy of the underlying data.
.. note:: Thecopykeyword will change behavior in pandas 3.0.Copy-on-Write <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>__ will be enabled by default, which means that all methods with acopykeyword will use a lazy copy mechanism to defer the copy and ignore thecopykeyword. Thecopykeyword will be removed in a future version of pandas.
You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True``
An object of type DataFrame.
DataFrame.renameaxis : Alter the name of the index or columns.
Examples
--------
~~~python
df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) ~~~
Change the row labels.
>>> df.set_axis(['a', 'b', 'c'], axis='index')
A B
a 1 4
b 2 5
c 3 6
Change the column labels.
>>> df.set_axis(['I', 'II'], axis='columns')
I II
0 1 4
1 2 5
2 3 6
reindex(labels=None, index=None, columns=None, axis=None, method=None, copy=None, level=None, fill_value=nan, limit=None, tolerance=None)
Conform DataFrame to new index with optional filling logic.
Places NA/NaN in locations having no value in the previous index. A new object
is produced unless the new index is equivalent to the current one and
copy=False.
DataFrame.set_index : Set row labels.DataFrame.reset_index : Remove row labels or move them to new columns. DataFrame.reindexlike : Change to same indices as other DataFrame.
DataFrame.reindex supports two calling conventions
(index=index_labels, columns=column_labels, ...)(labels, axis={'index', 'columns'}, ...)
We highly recommend using keyword arguments to clarify your intent.
Create a dataframe with some fictional data.
>>> index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror']
>>> df = pd.DataFrame({'http_status': [200, 200, 404, 404, 301],
... 'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]},
... index=index)
>>> df
http_status response_time
Firefox 200 0.04
Chrome 200 0.02
Safari 404 0.07
IE10 404 0.08
Konqueror 301 1.00
Create a new index and reindex the dataframe. By default
values in the new index that do not have corresponding
records in the dataframe are assigned NaN.
>>> new_index = ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10',
... 'Chrome']
>>> df.reindex(new_index)
http_status response_time
Safari 404.0 0.07
Iceweasel NaN NaN
Comodo Dragon NaN NaN
IE10 404.0 0.08
Chrome 200.0 0.02
We can fill in the missing values by passing a value to
the keyword fill_value. Because the index is not monotonically
increasing or decreasing, we cannot use arguments to the keyword
method to fill the NaN values.
>>> df.reindex(new_index, fill_value=0)
http_status response_time
Safari 404 0.07
Iceweasel 0 0.00
Comodo Dragon 0 0.00
IE10 404 0.08
Chrome 200 0.02
>>> df.reindex(new_index, fill_value='missing')
http_status response_time
Safari 404 0.07
Iceweasel missing missing
Comodo Dragon missing missing
IE10 404 0.08
Chrome 200 0.02
We can also reindex the columns.
>>> df.reindex(columns=['http_status', 'user_agent'])
http_status user_agent
Firefox 200 NaN
Chrome 200 NaN
Safari 404 NaN
IE10 404 NaN
Konqueror 301 NaN
Or we can use "axis-style" keyword arguments
>>> df.reindex(['http_status', 'user_agent'], axis="columns")
http_status user_agent
Firefox 200 NaN
Chrome 200 NaN
Safari 404 NaN
IE10 404 NaN
Konqueror 301 NaN
To further illustrate the filling functionality in
reindex, we will create a dataframe with a
monotonically increasing index (for example, a sequence
of dates).
>>> date_index = pd.date_range('1/1/2010', periods=6, freq='D')
>>> df2 = pd.DataFrame({"prices": [100, 101, np.nan, 100, 89, 88]},
... index=date_index)
>>> df2
prices
2010-01-01 100.0
2010-01-02 101.0
2010-01-03 NaN
2010-01-04 100.0
2010-01-05 89.0
2010-01-06 88.0
Suppose we decide to expand the dataframe to cover a wider date range.
>>> date_index2 = pd.date_range('12/29/2009', periods=10, freq='D')
>>> df2.reindex(date_index2)
prices
2009-12-29 NaN
2009-12-30 NaN
2009-12-31 NaN
2010-01-01 100.0
2010-01-02 101.0
2010-01-03 NaN
2010-01-04 100.0
2010-01-05 89.0
2010-01-06 88.0
2010-01-07 NaN
The index entries that did not have a value in the original data frame
(for example, '2009-12-29') are by default filled with NaN.
If desired, we can fill in the missing values using one of several
options.
For example, to back-propagate the last valid value to fill the NaN
values, pass bfill as an argument to the method keyword.
>>> df2.reindex(date_index2, method='bfill')
prices
2009-12-29 100.0
2009-12-30 100.0
2009-12-31 100.0
2010-01-01 100.0
2010-01-02 101.0
2010-01-03 NaN
2010-01-04 100.0
2010-01-05 89.0
2010-01-06 88.0
2010-01-07 NaN
Please note that the NaN value present in the original dataframe
(at index value 2010-01-03) will not be filled by any of the
value propagation schemes. This is because filling while reindexing
does not look at dataframe values, but only compares the original and
desired indexes. If you do want to fill in the NaN values present
in the original dataframe, use the fillna() method.
See the :ref:user guide <basics.reindexing> for more.
drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')
Drop specified labels from rows or columns.
Remove rows or columns by specifying label names and corresponding
axis, or by directly specifying index or column names. When using a
multi-index, labels on different levels can be removed by specifying
the level. See the :ref:user guide <advanced.shown_levels>
for more information about the now unused levels.
labels(single label or list-like) — Index or column labels to drop. A tuple will be used as a singlelabel and not treated as a list-like.axis({0 or 'index', 1 or 'columns'}, default 0) — Whether to drop labels from the index (0 or 'index') orcolumns (1 or 'columns').index(single label or list-like) — Alternative to specifying axis (labels, axis=0is equivalent toindex=labels).columns(single label or list-like) — Alternative to specifying axis (labels, axis=1is equivalent tocolumns=labels).level(int or level name, optional) — For MultiIndex, level from which the labels will be removed.inplace(bool, default False) — If False, return a copy. Otherwise, do operationin place and return None.errors({'ignore', 'raise'}, default 'raise') — If 'ignore', suppress error and only existing labels aredropped.
Returns DataFrame or None DataFrame with the specifiedindex or column labels removed or None if inplace=True.
KeyError— If any of the labels is not found in the selected axis.
DataFrame.loc : Label-location based indexer for selection by label.DataFrame.dropna : Return DataFrame with labels on given axis omitted where (all or any) data are missing. DataFrame.dropduplicates : Return DataFrame with duplicate rows removed, optionally only considering certain columns. Series.drop : Return Series with specified index labels removed.
>>> df = pd.DataFrame(np.arange(12).reshape(3, 4),... columns=['A', 'B', 'C', 'D'])
>>> df
A B C D
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
Drop columns
>>> df.drop(['B', 'C'], axis=1)
A D
0 0 3
1 4 7
2 8 11
>>> df.drop(columns=['B', 'C'])
A D
0 0 3
1 4 7
2 8 11
Drop a row by index
>>> df.drop([0, 1])
A B C D
2 8 9 10 11
Drop columns and/or rows of MultiIndex DataFrame
>>> midx = pd.MultiIndex(levels=[['llama', 'cow', 'falcon'],
... ['speed', 'weight', 'length']],
... codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
... [0, 1, 2, 0, 1, 2, 0, 1, 2]])
>>> df = pd.DataFrame(index=midx, columns=['big', 'small'],
... data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
... [250, 150], [1.5, 0.8], [320, 250],
... [1, 0.8], [0.3, 0.2]])
>>> df
big small
llama speed 45.0 30.0
weight 200.0 100.0
length 1.5 1.0
cow speed 30.0 20.0
weight 250.0 150.0
length 1.5 0.8
falcon speed 320.0 250.0
weight 1.0 0.8
length 0.3 0.2
Drop a specific index combination from the MultiIndex
DataFrame, i.e., drop the combination 'falcon' and
'weight', which deletes only the corresponding row
>>> df.drop(index=('falcon', 'weight'))
big small
llama speed 45.0 30.0
weight 200.0 100.0
length 1.5 1.0
cow speed 30.0 20.0
weight 250.0 150.0
length 1.5 0.8
falcon speed 320.0 250.0
length 0.3 0.2
>>> df.drop(index='cow', columns='small')
big
llama speed 45.0
weight 200.0
length 1.5
falcon speed 320.0
weight 1.0
length 0.3
>>> df.drop(index='length', level=1)
big small
llama speed 45.0 30.0
weight 200.0 100.0
cow speed 30.0 20.0
weight 250.0 150.0
falcon speed 320.0 250.0
weight 1.0 0.8
rename(mapper=None, index=None, columns=None, axis=None, copy=None, inplace=False, level=None, errors='ignore')
Rename columns or index labels.
Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don't throw an error.
See the :ref:user guide <basics.rename> for more.
mapper(dict-like or function) — Dict-like or function transformations to apply tothat axis' values. Use eithermapperandaxisto specify the axis to target withmapper, orindexandcolumns.index(dict-like or function) — Alternative to specifying axis (mapper, axis=0is equivalent toindex=mapper).columns(dict-like or function) — Alternative to specifying axis (mapper, axis=1is equivalent tocolumns=mapper).axis({0 or 'index', 1 or 'columns'}, default 0) — Axis to target withmapper. Can be either the axis name('index', 'columns') or number (0, 1). The default is 'index'.copy(bool, default True) — Also copy underlying data.
.. note:: Thecopykeyword will change behavior in pandas 3.0.Copy-on-Write <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>__ will be enabled by default, which means that all methods with acopykeyword will use a lazy copy mechanism to defer the copy and ignore thecopykeyword. Thecopykeyword will be removed in a future version of pandas.
You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True``inplace(bool, default False) — Whether to modify the DataFrame rather than creating a new one.If True then value of copy is ignored.level(int or level name, default None) — In case of a MultiIndex, only rename labels in the specifiedlevel.errors({'ignore', 'raise'}, default 'ignore') — If 'raise', raise aKeyErrorwhen a dict-likemapper,index,orcolumnscontains labels that are not present in the Index being transformed. If 'ignore', existing keys will be renamed and extra keys will be ignored.
DataFrame with the renamed axis labels or None if inplace=True.
KeyError— If any of the labels is not found in the selected axis and"errors='raise'".
DataFrame.renameaxis : Set the name of the axis.
DataFrame.rename supports two calling conventions
(index=index_mapper, columns=columns_mapper, ...)(mapper, axis={'index', 'columns'}, ...)
We highly recommend using keyword arguments to clarify your intent.
Rename columns using a mapping:
>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename(columns={"A": "a", "B": "c"})
a c
0 1 4
1 2 5
2 3 6
Rename index using a mapping:
>>> df.rename(index={0: "x", 1: "y", 2: "z"})
A B
x 1 4
y 2 5
z 3 6
Cast index labels to a different type:
>>> df.index
RangeIndex(start=0, stop=3, step=1)
>>> df.rename(index=str).index
Index(['0', '1', '2'], dtype='object')
>>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise")
Traceback (most recent call last):
KeyError: ['C'] not found in axis
Using axis-style parameters:
>>> df.rename(str.lower, axis='columns')
a b
0 1 4
1 2 5
2 3 6
>>> df.rename({1: 2, 2: 4}, axis='index')
A B
0 1 4
2 2 5
4 3 6
pop(item)
Return item and drop from frame. Raise KeyError if not found.
item(label) — Label of column to be popped.
>>> df = pd.DataFrame([('falcon', 'bird', 389.0),... ('parrot', 'bird', 24.0),
... ('lion', 'mammal', 80.5),
... ('monkey', 'mammal', np.nan)],
... columns=('name', 'class', 'max_speed'))
>>> df
name class max_speed
0 falcon bird 389.0
1 parrot bird 24.0
2 lion mammal 80.5
3 monkey mammal NaN
>>> df.pop('class')
0 bird
1 bird
2 mammal
3 mammal
Name: class, dtype: object
>>> df
name max_speed
0 falcon 389.0
1 parrot 24.0
2 lion 80.5
3 monkey NaN
shift(periods=1, freq=None, axis=0, fill_value=<no_default>, suffix=None)
Shift index by desired number of periods with an optional time freq.
When freq is not passed, shift the index without realigning the data.
If freq is passed (in this case, the index must be date or datetime,
or it will raise a NotImplementedError), the index will be
increased using the periods and the freq. freq can be inferred
when specified as "infer" as long as either freq or inferred_freq
attribute is set in the index.
periods(int or Sequence) — Number of periods to shift. Can be positive or negative.If an iterable of ints, the data will be shifted once by each int. This is equivalent to shifting by one value at a time and concatenating all resulting frames. The resulting columns will have the shift suffixed to their column names. For multiple periods, axis must not be 1.freq(DateOffset, tseries.offsets, timedelta, or str, optional) — Offset to use from the tseries module or time rule (e.g. 'EOM').Iffreqis specified then the index values are shifted but the data is not realigned. That is, usefreqif you would like to extend the index when shifting and preserve the original data. Iffreqis specified as "infer" then it will be inferred from the freq or inferred_freq attributes of the index. If neither of those attributes exist, a ValueError is thrown.axis({0 or 'index', 1 or 'columns', None}, default None) — Shift direction. ForSeriesthis parameter is unused and defaults to 0.fill_value(object, optional) — The scalar value to use for newly introduced missing values.the default depends on the dtype ofself. For numeric data,np.nanis used. For datetime, timedelta, or period data, etc. :attr:NaTis used. For extension dtypes,self.dtype.na_valueis used.suffix(str, optional) — If str and periods is an iterable, this is added after the columnname and before the shift value for each shifted column name.
Copy of input object, shifted.
Index.shift : Shift values of Index.DatetimeIndex.shift : Shift values of DatetimeIndex. PeriodIndex.shift : Shift values of PeriodIndex.
>>> df = pd.DataFrame({"Col1": [10, 20, 15, 30, 45],... "Col2": [13, 23, 18, 33, 48],
... "Col3": [17, 27, 22, 37, 52]},
... index=pd.date_range("2020-01-01", "2020-01-05"))
>>> df
Col1 Col2 Col3
2020-01-01 10 13 17
2020-01-02 20 23 27
2020-01-03 15 18 22
2020-01-04 30 33 37
2020-01-05 45 48 52
>>> df.shift(periods=3)
Col1 Col2 Col3
2020-01-01 NaN NaN NaN
2020-01-02 NaN NaN NaN
2020-01-03 NaN NaN NaN
2020-01-04 10.0 13.0 17.0
2020-01-05 20.0 23.0 27.0
>>> df.shift(periods=1, axis="columns")
Col1 Col2 Col3
2020-01-01 NaN 10 13
2020-01-02 NaN 20 23
2020-01-03 NaN 15 18
2020-01-04 NaN 30 33
2020-01-05 NaN 45 48
>>> df.shift(periods=3, fill_value=0)
Col1 Col2 Col3
2020-01-01 0 0 0
2020-01-02 0 0 0
2020-01-03 0 0 0
2020-01-04 10 13 17
2020-01-05 20 23 27
>>> df.shift(periods=3, freq="D")
Col1 Col2 Col3
2020-01-04 10 13 17
2020-01-05 20 23 27
2020-01-06 15 18 22
2020-01-07 30 33 37
2020-01-08 45 48 52
>>> df.shift(periods=3, freq="infer")
Col1 Col2 Col3
2020-01-04 10 13 17
2020-01-05 20 23 27
2020-01-06 15 18 22
2020-01-07 30 33 37
2020-01-08 45 48 52
>>> df['Col1'].shift(periods=[0, 1, 2])
Col1_0 Col1_1 Col1_2
2020-01-01 10 NaN NaN
2020-01-02 20 10.0 NaN
2020-01-03 15 20.0 10.0
2020-01-04 30 15.0 20.0
2020-01-05 45 30.0 15.0
set_index(keys, drop=True, append=False, inplace=False, verify_integrity=False)
Set the DataFrame index using existing columns.
Set the DataFrame index (row labels) using one or more existing columns or arrays (of the correct length). The index can replace the existing index or expand on it.
keys(label or array-like or list of labels/arrays) — This parameter can be either a single column key, a single array ofthe same length as the calling DataFrame, or a list containing an arbitrary combination of column keys and arrays. Here, "array" encompasses :class:Series, :class:Index,np.ndarray, and instances of :class:~collections.abc.Iterator.drop(bool, default True) — Delete columns to be used as the new index.append(bool, default False) — Whether to append columns to existing index.inplace(bool, default False) — Whether to modify the DataFrame rather than creating a new one.verify_integrity(bool, default False) — Check the new index for duplicates. Otherwise defer the check untilnecessary. Setting to False will improve the performance of this method.
Changed row labels or None if inplace=True.
DataFrame.reset_index : Opposite of set_index.DataFrame.reindex : Change to new indices or expand indices. DataFrame.reindexlike : Change to same indices as other DataFrame.
>>> df = pd.DataFrame({'month': [1, 4, 7, 10],... 'year': [2012, 2014, 2013, 2014],
... 'sale': [55, 40, 84, 31]})
>>> df
month year sale
0 1 2012 55
1 4 2014 40
2 7 2013 84
3 10 2014 31
Set the index to become the 'month' column:
>>> df.set_index('month')
year sale
month
1 2012 55
4 2014 40
7 2013 84
10 2014 31
Create a MultiIndex using columns 'year' and 'month':
>>> df.set_index(['year', 'month'])
sale
year month
2012 1 55
2014 4 40
2013 7 84
2014 10 31
Create a MultiIndex using an Index and a column:
>>> df.set_index([pd.Index([1, 2, 3, 4]), 'year'])
month sale
year
1 2012 1 55
2 2014 4 40
3 2013 7 84
4 2014 10 31
Create a MultiIndex using two Series:
>>> s = pd.Series([1, 2, 3, 4])
>>> df.set_index([s, s**2])
month year sale
1 1 1 2012 55
2 4 4 2014 40
3 9 7 2013 84
4 16 10 2014 31
reset_index(level=None, drop=False, inplace=False, col_level=0, col_fill='', allow_duplicates=<no_default>, names=None)
Reset the index, or a level of it.
Reset the index of the DataFrame, and use the default one instead. If the DataFrame has a MultiIndex, this method can remove one or more levels.
level(int, str, tuple, or list, default None) — Only remove the given levels from the index. Removes all levels bydefault.drop(bool, default False) — Do not try to insert index into dataframe columns. This resetsthe index to the default integer index.inplace(bool, default False) — Whether to modify the DataFrame rather than creating a new one.col_level(int or str, default 0) — If the columns have multiple levels, determines which level thelabels are inserted into. By default it is inserted into the first level.col_fill(object, default '') — If the columns have multiple levels, determines how the otherlevels are named. If None then the index name is repeated.allow_duplicates(bool, optional, default lib.no_default) — Allow duplicate column labels to be created.
.. versionadded:: 1.5.0names(int, str or 1-dimensional list, default None) — Using the given string, rename the DataFrame column which contains theindex data. If the DataFrame has a MultiIndex, this has to be a list or tuple with length equal to the number of levels.
.. versionadded:: 1.5.0
DataFrame with the new index or None if inplace=True.
DataFrame.set_index : Opposite of reset_index.DataFrame.reindex : Change to new indices or expand indices. DataFrame.reindexlike : Change to same indices as other DataFrame.
>>> df = pd.DataFrame([('bird', 389.0),... ('bird', 24.0),
... ('mammal', 80.5),
... ('mammal', np.nan)],
... index=['falcon', 'parrot', 'lion', 'monkey'],
... columns=('class', 'max_speed'))
>>> df
class max_speed
falcon bird 389.0
parrot bird 24.0
lion mammal 80.5
monkey mammal NaN
When we reset the index, the old index is added as a column, and a new sequential index is used:
>>> df.reset_index()
index class max_speed
0 falcon bird 389.0
1 parrot bird 24.0
2 lion mammal 80.5
3 monkey mammal NaN
We can use the drop parameter to avoid the old index being added as
a column:
>>> df.reset_index(drop=True)
class max_speed
0 bird 389.0
1 bird 24.0
2 mammal 80.5
3 mammal NaN
You can also use reset_index with MultiIndex.
>>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'),
... ('bird', 'parrot'),
... ('mammal', 'lion'),
... ('mammal', 'monkey')],
... names=['class', 'name'])
>>> columns = pd.MultiIndex.from_tuples([('speed', 'max'),
... ('species', 'type')])
>>> df = pd.DataFrame([(389.0, 'fly'),
... (24.0, 'fly'),
... (80.5, 'run'),
... (np.nan, 'jump')],
... index=index,
... columns=columns)
>>> df
speed species
max type
class name
bird falcon 389.0 fly
parrot 24.0 fly
mammal lion 80.5 run
monkey NaN jump
Using the names parameter, choose a name for the index column:
>>> df.reset_index(names=['classes', 'names'])
classes names speed species
max type
0 bird falcon 389.0 fly
1 bird parrot 24.0 fly
2 mammal lion 80.5 run
3 mammal monkey NaN jump
If the index has multiple levels, we can reset a subset of them:
>>> df.reset_index(level='class')
class speed species
max type
name
falcon bird 389.0 fly
parrot bird 24.0 fly
lion mammal 80.5 run
monkey mammal NaN jump
If we are not dropping the index, by default, it is placed in the top level. We can place it in another level:
>>> df.reset_index(level='class', col_level=1)
speed species
class max type
name
falcon bird 389.0 fly
parrot bird 24.0 fly
lion mammal 80.5 run
monkey mammal NaN jump
When the index is inserted under another level, we can specify under
which one with the parameter col_fill:
>>> df.reset_index(level='class', col_level=1, col_fill='species')
species speed species
class max type
name
falcon bird 389.0 fly
parrot bird 24.0 fly
lion mammal 80.5 run
monkey mammal NaN jump
If we specify a nonexistent level for col_fill, it is created:
>>> df.reset_index(level='class', col_level=1, col_fill='genus')
genus speed species
class max type
name
falcon bird 389.0 fly
parrot bird 24.0 fly
lion mammal 80.5 run
monkey mammal NaN jump
isna()
Detect missing values.
Return a boolean same-sized object indicating if the values are NA.
NA values, such as None or :attr:numpy.NaN, gets mapped to True
values.
Everything else gets mapped to False values. Characters such as empty
strings '' or :attr:numpy.inf are not considered NA values
(unless you set pandas.options.mode.use_inf_as_na = True).
Mask of bool values for each element in DataFrame thatindicates whether an element is an NA value.
DataFrame.isnull : Alias of isna.DataFrame.notna : Boolean inverse of isna. DataFrame.dropna : Omit axes labels with missing values. isna : Top-level isna.
Show which entries in a DataFrame are NA.
>>> df = pd.DataFrame(dict(age=[5, 6, np.nan],
... born=[pd.NaT, pd.Timestamp('1939-05-27'),
... pd.Timestamp('1940-04-25')],
... name=['Alfred', 'Batman', ''],
... toy=[None, 'Batmobile', 'Joker']))
>>> df
age born name toy
0 5.0 NaT Alfred None
1 6.0 1939-05-27 Batman Batmobile
2 NaN 1940-04-25 Joker
>>> df.isna()
age born name toy
0 False True False True
1 False False False False
2 True False False False
Show which entries in a Series are NA.
>>> ser = pd.Series([5, 6, np.nan])
>>> ser
0 5.0
1 6.0
2 NaN
dtype: float64
>>> ser.isna()
0 False
1 False
2 True
dtype: bool
isnull()
DataFrame.isnull is an alias for DataFrame.isna.
Detect missing values.
Return a boolean same-sized object indicating if the values are NA.
NA values, such as None or :attr:numpy.NaN, gets mapped to True
values.
Everything else gets mapped to False values. Characters such as empty
strings '' or :attr:numpy.inf are not considered NA values
(unless you set pandas.options.mode.use_inf_as_na = True).
Mask of bool values for each element in DataFrame thatindicates whether an element is an NA value.
DataFrame.isnull : Alias of isna.DataFrame.notna : Boolean inverse of isna. DataFrame.dropna : Omit axes labels with missing values. isna : Top-level isna.
Show which entries in a DataFrame are NA.
>>> df = pd.DataFrame(dict(age=[5, 6, np.nan],
... born=[pd.NaT, pd.Timestamp('1939-05-27'),
... pd.Timestamp('1940-04-25')],
... name=['Alfred', 'Batman', ''],
... toy=[None, 'Batmobile', 'Joker']))
>>> df
age born name toy
0 5.0 NaT Alfred None
1 6.0 1939-05-27 Batman Batmobile
2 NaN 1940-04-25 Joker
>>> df.isna()
age born name toy
0 False True False True
1 False False False False
2 True False False False
Show which entries in a Series are NA.
>>> ser = pd.Series([5, 6, np.nan])
>>> ser
0 5.0
1 6.0
2 NaN
dtype: float64
>>> ser.isna()
0 False
1 False
2 True
dtype: bool
notna()
Detect existing (non-missing) values.
Return a boolean same-sized object indicating if the values are not NA.
Non-missing values get mapped to True. Characters such as empty
strings '' or :attr:numpy.inf are not considered NA values
(unless you set pandas.options.mode.use_inf_as_na = True).
NA values, such as None or :attr:numpy.NaN, get mapped to False
values.
Mask of bool values for each element in DataFrame thatindicates whether an element is not an NA value.
DataFrame.notnull : Alias of notna.DataFrame.isna : Boolean inverse of notna. DataFrame.dropna : Omit axes labels with missing values. notna : Top-level notna.
Show which entries in a DataFrame are not NA.
>>> df = pd.DataFrame(dict(age=[5, 6, np.nan],
... born=[pd.NaT, pd.Timestamp('1939-05-27'),
... pd.Timestamp('1940-04-25')],
... name=['Alfred', 'Batman', ''],
... toy=[None, 'Batmobile', 'Joker']))
>>> df
age born name toy
0 5.0 NaT Alfred None
1 6.0 1939-05-27 Batman Batmobile
2 NaN 1940-04-25 Joker
>>> df.notna()
age born name toy
0 True False True False
1 True True True True
2 False True True True
Show which entries in a Series are not NA.
>>> ser = pd.Series([5, 6, np.nan])
>>> ser
0 5.0
1 6.0
2 NaN
dtype: float64
>>> ser.notna()
0 True
1 True
2 False
dtype: bool
notnull()
DataFrame.notnull is an alias for DataFrame.notna.
Detect existing (non-missing) values.
Return a boolean same-sized object indicating if the values are not NA.
Non-missing values get mapped to True. Characters such as empty
strings '' or :attr:numpy.inf are not considered NA values
(unless you set pandas.options.mode.use_inf_as_na = True).
NA values, such as None or :attr:numpy.NaN, get mapped to False
values.
Mask of bool values for each element in DataFrame thatindicates whether an element is not an NA value.
DataFrame.notnull : Alias of notna.DataFrame.isna : Boolean inverse of notna. DataFrame.dropna : Omit axes labels with missing values. notna : Top-level notna.
Show which entries in a DataFrame are not NA.
>>> df = pd.DataFrame(dict(age=[5, 6, np.nan],
... born=[pd.NaT, pd.Timestamp('1939-05-27'),
... pd.Timestamp('1940-04-25')],
... name=['Alfred', 'Batman', ''],
... toy=[None, 'Batmobile', 'Joker']))
>>> df
age born name toy
0 5.0 NaT Alfred None
1 6.0 1939-05-27 Batman Batmobile
2 NaN 1940-04-25 Joker
>>> df.notna()
age born name toy
0 True False True False
1 True True True True
2 False True True True
Show which entries in a Series are not NA.
>>> ser = pd.Series([5, 6, np.nan])
>>> ser
0 5.0
1 6.0
2 NaN
dtype: float64
>>> ser.notna()
0 True
1 True
2 False
dtype: bool
dropna(axis=0, how=<no_default>, thresh=<no_default>, subset=None, inplace=False, ignore_index=False)
Remove missing values.
See the :ref:User Guide <missing_data> for more on which values are
considered missing, and how to work with missing data.
axis({0 or 'index', 1 or 'columns'}, default 0) — Determine if rows or columns which contain missing values areremoved.
- 0, or 'index' : Drop rows which contain missing values.
- 1, or 'columns' : Drop columns which contain missing value.
how({'any', 'all'}, default 'any') — Determine if row or column is removed from DataFrame, when we haveat least one NA or all NA.
- 'any' : If any NA values are present, drop that row or column.
- 'all' : If all values are NA, drop that row or column.
thresh(int, optional) — Require that many non-NA values. Cannot be combined with how.subset(column label or sequence of labels, optional) — Labels along other axis to consider, e.g. if you are dropping rowsthese would be a list of columns to include.inplace(bool, default False) — Whether to modify the DataFrame rather than creating a new one.ignore_index(bool, default ``False``) — IfTrue, the resulting axis will be labeled 0, 1, …, n - 1.
.. versionadded:: 2.0.0
DataFrame with NA entries dropped from it or None if inplace=True.
DataFrame.isna: Indicate missing values.DataFrame.notna : Indicate existing (non-missing) values. DataFrame.fillna : Replace missing values. Series.dropna : Drop missing values. Index.dropna : Drop missing indices.
>>> df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],... "toy": [np.nan, 'Batmobile', 'Bullwhip'],
... "born": [pd.NaT, pd.Timestamp("1940-04-25"),
... pd.NaT]})
>>> df
name toy born
0 Alfred NaN NaT
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip NaT
Drop the rows where at least one element is missing.
>>> df.dropna()
name toy born
1 Batman Batmobile 1940-04-25
Drop the columns where at least one element is missing.
>>> df.dropna(axis='columns')
name
0 Alfred
1 Batman
2 Catwoman
Drop the rows where all elements are missing.
>>> df.dropna(how='all')
name toy born
0 Alfred NaN NaT
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip NaT
Keep only the rows with at least 2 non-NA values.
>>> df.dropna(thresh=2)
name toy born
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip NaT
Define in which columns to look for missing values.
>>> df.dropna(subset=['name', 'toy'])
name toy born
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip NaT
drop_duplicates(subset=None, keep='first', inplace=False, ignore_index=False)
Return DataFrame with duplicate rows removed.
Considering certain columns is optional. Indexes, including time indexes are ignored.
subset(column label or sequence of labels, optional) — Only consider certain columns for identifying duplicates, bydefault use all of the columns.keep({'first', 'last', ``False``}, default 'first') — Determines which duplicates (if any) to keep.- 'first' : Drop duplicates except for the first occurrence.
- 'last' : Drop duplicates except for the last occurrence.
False: Drop all duplicates.
inplace(bool, default ``False``) — Whether to modify the DataFrame rather than creating a new one.ignore_index(bool, default ``False``) — IfTrue, the resulting axis will be labeled 0, 1, …, n - 1.
DataFrame with duplicates removed or None if inplace=True.
DataFrame.value_counts: Count unique combinations of columns.
Consider dataset containing ramen rating.
>>> df = pd.DataFrame({
... 'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
... 'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
... 'rating': [4, 4, 3.5, 15, 5]
... })
>>> df
brand style rating
0 Yum Yum cup 4.0
1 Yum Yum cup 4.0
2 Indomie cup 3.5
3 Indomie pack 15.0
4 Indomie pack 5.0
By default, it removes duplicate rows based on all columns.
>>> df.drop_duplicates()
brand style rating
0 Yum Yum cup 4.0
2 Indomie cup 3.5
3 Indomie pack 15.0
4 Indomie pack 5.0
To remove duplicates on specific column(s), use subset.
>>> df.drop_duplicates(subset=['brand'])
brand style rating
0 Yum Yum cup 4.0
2 Indomie cup 3.5
To remove duplicates and keep last occurrences, use keep.
>>> df.drop_duplicates(subset=['brand', 'style'], keep='last')
brand style rating
1 Yum Yum cup 4.0
2 Indomie cup 3.5
4 Indomie pack 5.0
duplicated(subset=None, keep='first')
Return boolean Series denoting duplicate rows.
Considering certain columns is optional.
subset(column label or sequence of labels, optional) — Only consider certain columns for identifying duplicates, bydefault use all of the columns.keep({'first', 'last', False}, default 'first') — Determines which duplicates (if any) to mark.first: Mark duplicates asTrueexcept for the first occurrence.last: Mark duplicates asTrueexcept for the last occurrence.- False : Mark all duplicates as
True.
Boolean series for each duplicated rows.
Index.duplicated : Equivalent method on index.Series.duplicated : Equivalent method on Series. Series.dropduplicates : Remove duplicate values from Series. DataFrame.dropduplicates : Remove duplicate values from DataFrame.
Consider dataset containing ramen rating.
>>> df = pd.DataFrame({
... 'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
... 'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
... 'rating': [4, 4, 3.5, 15, 5]
... })
>>> df
brand style rating
0 Yum Yum cup 4.0
1 Yum Yum cup 4.0
2 Indomie cup 3.5
3 Indomie pack 15.0
4 Indomie pack 5.0
By default, for each set of duplicated values, the first occurrence is set on False and all others on True.
>>> df.duplicated()
0 False
1 True
2 False
3 False
4 False
dtype: bool
By using 'last', the last occurrence of each set of duplicated values is set on False and all others on True.
>>> df.duplicated(keep='last')
0 True
1 False
2 False
3 False
4 False
dtype: bool
By setting keep on False, all duplicates are True.
>>> df.duplicated(keep=False)
0 True
1 True
2 False
3 False
4 False
dtype: bool
To find duplicates on specific column(s), use subset.
>>> df.duplicated(subset=['brand'])
0 False
1 True
2 False
3 True
4 True
dtype: bool
sort_values(by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last', ignore_index=False, key=None)
Sort by the values along either axis.
by(str or list of str) — Name or list of names to sort by.- if
axisis 0 or'index'thenbymay contain index levels and/or column labels. - if
axisis 1 or'columns'thenbymay contain column levels and/or index labels.
- if
axis("{0 or 'index', 1 or 'columns'}", default 0) — Axis to be sorted.ascending(bool or list of bool, default True) — Sort ascending vs. descending. Specify list for multiple sortorders. If this is a list of bools, must match the length of the by.inplace(bool, default False) — If True, perform operation in-place.kind({'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort') — Choice of sorting algorithm. See also :func:numpy.sortfor moreinformation.mergesortandstableare the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label.na_position({'first', 'last'}, default 'last') — Puts NaNs at the beginning iffirst;lastputs NaNs at theend.ignore_index(bool, default False) — If True, the resulting axis will be labeled 0, 1, …, n - 1.key(callable, optional) — Apply the key function to the valuesbefore sorting. This is similar to thekeyargument in the builtin :meth:sortedfunction, with the notable difference that thiskeyfunction should be vectorized. It should expect aSeriesand return a Series with the same shape as the input. It will be applied to each column inbyindependently.
DataFrame with sorted values or None if inplace=True.
DataFrame.sort_index : Sort a DataFrame by the index.Series.sort_values : Similar method for a Series.
>>> df = pd.DataFrame({... 'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
... 'col2': [2, 1, 9, 8, 7, 4],
... 'col3': [0, 1, 9, 4, 2, 3],
... 'col4': ['a', 'B', 'c', 'D', 'e', 'F']
... })
>>> df
col1 col2 col3 col4
0 A 2 0 a
1 A 1 1 B
2 B 9 9 c
3 NaN 8 4 D
4 D 7 2 e
5 C 4 3 F
Sort by col1
>>> df.sort_values(by=['col1'])
col1 col2 col3 col4
0 A 2 0 a
1 A 1 1 B
2 B 9 9 c
5 C 4 3 F
4 D 7 2 e
3 NaN 8 4 D
Sort by multiple columns
>>> df.sort_values(by=['col1', 'col2'])
col1 col2 col3 col4
1 A 1 1 B
0 A 2 0 a
2 B 9 9 c
5 C 4 3 F
4 D 7 2 e
3 NaN 8 4 D
Sort Descending
>>> df.sort_values(by='col1', ascending=False)
col1 col2 col3 col4
4 D 7 2 e
5 C 4 3 F
2 B 9 9 c
0 A 2 0 a
1 A 1 1 B
3 NaN 8 4 D
Putting NAs first
>>> df.sort_values(by='col1', ascending=False, na_position='first')
col1 col2 col3 col4
3 NaN 8 4 D
4 D 7 2 e
5 C 4 3 F
2 B 9 9 c
0 A 2 0 a
1 A 1 1 B
Sorting with a key function
>>> df.sort_values(by='col4', key=lambda col: col.str.lower())
col1 col2 col3 col4
0 A 2 0 a
1 A 1 1 B
2 B 9 9 c
3 NaN 8 4 D
4 D 7 2 e
5 C 4 3 F
Natural sort with the key argument,
using the natsort <https://github.com/SethMMorton/natsort> package.
>>> df = pd.DataFrame({
... "time": ['0hr', '128hr', '72hr', '48hr', '96hr'],
... "value": [10, 20, 30, 40, 50]
... })
>>> df
time value
0 0hr 10
1 128hr 20
2 72hr 30
3 48hr 40
4 96hr 50
>>> from natsort import index_natsorted
>>> df.sort_values(
... by="time",
... key=lambda x: np.argsort(index_natsorted(df["time"]))
... )
time value
0 0hr 10
3 48hr 40
2 72hr 30
4 96hr 50
1 128hr 20
sort_index(axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True, ignore_index=False, key=None)
Sort object by labels (along an axis).
Returns a new DataFrame sorted by label if inplace argument is
False, otherwise updates the original DataFrame and returns None.
axis({0 or 'index', 1 or 'columns'}, default 0) — The axis along which to sort. The value 0 identifies the rows,and 1 identifies the columns.level(int or level name or list of ints or list of level names) — If not None, sort on values in specified index level(s).ascending(bool or list-like of bools, default True) — Sort ascending vs. descending. When the index is a MultiIndex thesort direction can be controlled for each level individually.inplace(bool, default False) — Whether to modify the DataFrame rather than creating a new one.kind({'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort') — Choice of sorting algorithm. See also :func:numpy.sortfor moreinformation.mergesortandstableare the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label.na_position({'first', 'last'}, default 'last') — Puts NaNs at the beginning iffirst;lastputs NaNs at the end.Not implemented for MultiIndex.sort_remaining(bool, default True) — If True and sorting by level and index is multilevel, sort by otherlevels too (in order) after sorting by specified level.ignore_index(bool, default False) — If True, the resulting axis will be labeled 0, 1, …, n - 1.key(callable, optional) — If not None, apply the key function to the index valuesbefore sorting. This is similar to thekeyargument in the builtin :meth:sortedfunction, with the notable difference that thiskeyfunction should be vectorized. It should expect anIndexand return anIndexof the same shape. For MultiIndex inputs, the key is applied per level.
The original DataFrame sorted by the labels or None if inplace=True.
Series.sort_index : Sort Series by the index.DataFrame.sort_values : Sort DataFrame by the value. Series.sort_values : Sort Series by the value.
>>> df = pd.DataFrame([1, 2, 3, 4, 5], index=[100, 29, 234, 1, 150],... columns=['A'])
>>> df.sort_index()
A
1 4
29 2
100 1
150 5
234 3
By default, it sorts in ascending order, to sort in descending order,
use ascending=False
>>> df.sort_index(ascending=False)
A
234 3
150 5
100 1
29 2
1 4
A key function can be specified which is applied to the index before
sorting. For a MultiIndex this is applied to each level separately.
>>> df = pd.DataFrame({"a": [1, 2, 3, 4]}, index=['A', 'b', 'C', 'd'])
>>> df.sort_index(key=lambda x: x.str.lower())
a
A 1
b 2
C 3
d 4
value_counts(subset=None, normalize=False, sort=True, ascending=False, dropna=True)
Return a Series containing the frequency of each distinct row in the Dataframe.
subset(label or list of labels, optional) — Columns to use when counting unique combinations.normalize(bool, default False) — Return proportions rather than frequencies.sort(bool, default True) — Sort by frequencies when True. Sort by DataFrame column values when False.ascending(bool, default False) — Sort in ascending order.dropna(bool, default True) — Don't include counts of rows that contain NA values.
.. versionadded:: 1.3.0
Series.value_counts: Equivalent method on Series.
Notes
The returned Series will have a MultiIndex with one level per input column but an Index (non-multi) for a single label. By default, rows that contain any NA values are omitted from the result. By default, the resulting Series will be in descending order so that the first element is the most frequently-occurring row.
>>> df = pd.DataFrame({'num_legs': [2, 4, 4, 6],... 'num_wings': [2, 0, 0, 0]},
... index=['falcon', 'dog', 'cat', 'ant'])
>>> df
num_legs num_wings
falcon 2 2
dog 4 0
cat 4 0
ant 6 0
>>> df.value_counts()
num_legs num_wings
4 0 2
2 2 1
6 0 1
Name: count, dtype: int64
>>> df.value_counts(sort=False)
num_legs num_wings
2 2 1
4 0 2
6 0 1
Name: count, dtype: int64
>>> df.value_counts(ascending=True)
num_legs num_wings
2 2 1
6 0 1
4 0 2
Name: count, dtype: int64
>>> df.value_counts(normalize=True)
num_legs num_wings
4 0 0.50
2 2 0.25
6 0 0.25
Name: proportion, dtype: float64
With dropna set to False we can also count rows with NA values.
>>> df = pd.DataFrame({'first_name': ['John', 'Anne', 'John', 'Beth'],
... 'middle_name': ['Smith', pd.NA, pd.NA, 'Louise']})
>>> df
first_name middle_name
0 John Smith
1 Anne <NA>
2 John <NA>
3 Beth Louise
>>> df.value_counts()
first_name middle_name
Beth Louise 1
John Smith 1
Name: count, dtype: int64
>>> df.value_counts(dropna=False)
first_name middle_name
Anne NaN 1
Beth Louise 1
John Smith 1
NaN 1
Name: count, dtype: int64
>>> df.value_counts("first_name")
first_name
John 2
Anne 1
Beth 1
Name: count, dtype: int64
nlargest(n, columns, keep='first')
Return the first n rows ordered by columns in descending order.
Return the first n rows with the largest values in columns, in
descending order. The columns that are not specified are returned as
well, but not used for ordering.
This method is equivalent to
df.sort_values(columns, ascending=False).head(n), but more
performant.
n(int) — Number of rows to return.columns(label or list of labels) — Column label(s) to order by.keep({'first', 'last', 'all'}, default 'first') — Where there are duplicate values:first: prioritize the first occurrence(s)last: prioritize the last occurrence(s)all: keep all the ties of the smallest item even if it means selecting more thannitems.
The first n rows ordered by the given columns in descendingorder.
DataFrame.nsmallest : Return the first n rows ordered by columns in ascending order.
DataFrame.sort_values : Sort DataFrame by the values.
DataFrame.head : Return the first n rows without re-ordering.
Notes
This function cannot be used with all column types. For example, when
specifying columns with object or category dtypes, TypeError is
raised.
>>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,... 434000, 434000, 337000, 11300,
... 11300, 11300],
... 'GDP': [1937894, 2583560 , 12011, 4520, 12128,
... 17036, 182, 38, 311],
... 'alpha-2': ["IT", "FR", "MT", "MV", "BN",
... "IS", "NR", "TV", "AI"]},
... index=["Italy", "France", "Malta",
... "Maldives", "Brunei", "Iceland",
... "Nauru", "Tuvalu", "Anguilla"])
>>> df
population GDP alpha-2
Italy 59000000 1937894 IT
France 65000000 2583560 FR
Malta 434000 12011 MT
Maldives 434000 4520 MV
Brunei 434000 12128 BN
Iceland 337000 17036 IS
Nauru 11300 182 NR
Tuvalu 11300 38 TV
Anguilla 11300 311 AI
In the following example, we will use nlargest to select the three
rows having the largest values in column "population".
>>> df.nlargest(3, 'population')
population GDP alpha-2
France 65000000 2583560 FR
Italy 59000000 1937894 IT
Malta 434000 12011 MT
When using keep='last', ties are resolved in reverse order:
>>> df.nlargest(3, 'population', keep='last')
population GDP alpha-2
France 65000000 2583560 FR
Italy 59000000 1937894 IT
Brunei 434000 12128 BN
When using keep='all', the number of element kept can go beyond n
if there are duplicate values for the smallest element, all the
ties are kept:
>>> df.nlargest(3, 'population', keep='all')
population GDP alpha-2
France 65000000 2583560 FR
Italy 59000000 1937894 IT
Malta 434000 12011 MT
Maldives 434000 4520 MV
Brunei 434000 12128 BN
However, nlargest does not keep n distinct largest elements:
>>> df.nlargest(5, 'population', keep='all')
population GDP alpha-2
France 65000000 2583560 FR
Italy 59000000 1937894 IT
Malta 434000 12011 MT
Maldives 434000 4520 MV
Brunei 434000 12128 BN
To order by the largest values in column "population" and then "GDP", we can specify multiple columns like in the next example.
>>> df.nlargest(3, ['population', 'GDP'])
population GDP alpha-2
France 65000000 2583560 FR
Italy 59000000 1937894 IT
Brunei 434000 12128 BN
nsmallest(n, columns, keep='first')
Return the first n rows ordered by columns in ascending order.
Return the first n rows with the smallest values in columns, in
ascending order. The columns that are not specified are returned as
well, but not used for ordering.
This method is equivalent to
df.sort_values(columns, ascending=True).head(n), but more
performant.
n(int) — Number of items to retrieve.columns(list or str) — Column name or names to order by.keep({'first', 'last', 'all'}, default 'first') — Where there are duplicate values:first: take the first occurrence.last: take the last occurrence.all: keep all the ties of the largest item even if it means selecting more thannitems.
DataFrame.nlargest : Return the first n rows ordered by columns in descending order.
DataFrame.sort_values : Sort DataFrame by the values.
DataFrame.head : Return the first n rows without re-ordering.
>>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,... 434000, 434000, 337000, 337000,
... 11300, 11300],
... 'GDP': [1937894, 2583560 , 12011, 4520, 12128,
... 17036, 182, 38, 311],
... 'alpha-2': ["IT", "FR", "MT", "MV", "BN",
... "IS", "NR", "TV", "AI"]},
... index=["Italy", "France", "Malta",
... "Maldives", "Brunei", "Iceland",
... "Nauru", "Tuvalu", "Anguilla"])
>>> df
population GDP alpha-2
Italy 59000000 1937894 IT
France 65000000 2583560 FR
Malta 434000 12011 MT
Maldives 434000 4520 MV
Brunei 434000 12128 BN
Iceland 337000 17036 IS
Nauru 337000 182 NR
Tuvalu 11300 38 TV
Anguilla 11300 311 AI
In the following example, we will use nsmallest to select the
three rows having the smallest values in column "population".
>>> df.nsmallest(3, 'population')
population GDP alpha-2
Tuvalu 11300 38 TV
Anguilla 11300 311 AI
Iceland 337000 17036 IS
When using keep='last', ties are resolved in reverse order:
>>> df.nsmallest(3, 'population', keep='last')
population GDP alpha-2
Anguilla 11300 311 AI
Tuvalu 11300 38 TV
Nauru 337000 182 NR
When using keep='all', the number of element kept can go beyond n
if there are duplicate values for the largest element, all the
ties are kept.
>>> df.nsmallest(3, 'population', keep='all')
population GDP alpha-2
Tuvalu 11300 38 TV
Anguilla 11300 311 AI
Iceland 337000 17036 IS
Nauru 337000 182 NR
However, nsmallest does not keep n distinct
smallest elements:
>>> df.nsmallest(4, 'population', keep='all')
population GDP alpha-2
Tuvalu 11300 38 TV
Anguilla 11300 311 AI
Iceland 337000 17036 IS
Nauru 337000 182 NR
To order by the smallest values in column "population" and then "GDP", we can specify multiple columns like in the next example.
>>> df.nsmallest(3, ['population', 'GDP'])
population GDP alpha-2
Tuvalu 11300 38 TV
Anguilla 11300 311 AI
Nauru 337000 182 NR
swaplevel(i=-2, j=-1, axis=0)
Swap levels i and j in a :class:MultiIndex.
Default is to swap the two innermost levels of the index.
axis({0 or 'index', 1 or 'columns'}, default 0) — The axis to swap levels on. 0 or 'index' for row-wise, 1 or'columns' for column-wise.
DataFrame with levels swapped in MultiIndex.
>>> df = pd.DataFrame(... {"Grade": ["A", "B", "A", "C"]},
... index=[
... ["Final exam", "Final exam", "Coursework", "Coursework"],
... ["History", "Geography", "History", "Geography"],
... ["January", "February", "March", "April"],
... ],
... )
>>> df
Grade
Final exam History January A
Geography February B
Coursework History March A
Geography April C
In the following example, we will swap the levels of the indices. Here, we will swap the levels column-wise, but levels can be swapped row-wise in a similar manner. Note that column-wise is the default behaviour. By not supplying any arguments for i and j, we swap the last and second to last indices.
>>> df.swaplevel()
Grade
Final exam January History A
February Geography B
Coursework March History A
April Geography C
By supplying one argument, we can choose which index to swap the last index with. We can for example swap the first index with the last one as follows.
>>> df.swaplevel(0)
Grade
January History Final exam A
February Geography Final exam B
March History Coursework A
April Geography Coursework C
We can also define explicitly which indices we want to swap by supplying values for both i and j. Here, we for example swap the first and second indices.
>>> df.swaplevel(0, 1)
Grade
History Final exam January A
Geography Final exam February B
History Coursework March A
Geography Coursework April C
reorder_levels(order, axis=0)
Rearrange index levels using input order. May not drop or duplicate levels.
order(list of int or list of str) — List representing new level order. Reference level by number(position) or by key (label).axis({0 or 'index', 1 or 'columns'}, default 0) — Where to reorder levels.
>>> data = {... "class": ["Mammals", "Mammals", "Reptiles"],
... "diet": ["Omnivore", "Carnivore", "Carnivore"],
... "species": ["Humans", "Dogs", "Snakes"],
... }
>>> df = pd.DataFrame(data, columns=["class", "diet", "species"])
>>> df = df.set_index(["class", "diet"])
>>> df
species
class diet
Mammals Omnivore Humans
Carnivore Dogs
Reptiles Carnivore Snakes
Let's reorder the levels of the index:
>>> df.reorder_levels(["diet", "class"])
species
diet class
Omnivore Mammals Humans
Carnivore Mammals Dogs
Reptiles Snakes
eq(other, axis='columns', level=None)
Get Equal to of dataframe and other, element-wise (binary operator eq).
Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison
operators.
Equivalent to ==, !=, <=, <, >=, > with support to choose axis
(rows or columns) and level for comparison.
other(scalar, sequence, Series, or DataFrame) — Any single or multiple element data structure, or list-like object.axis({0 or 'index', 1 or 'columns'}, default 'columns') — Whether to compare by the index (0 or 'index') or columns(1 or 'columns').level(int or label) — Broadcast across a level, matching Index values on the passedMultiIndex level.
Result of the comparison.
DataFrame.eq : Compare DataFrames for equality elementwise.DataFrame.ne : Compare DataFrames for inequality elementwise. DataFrame.le : Compare DataFrames for less than inequality or equality elementwise. DataFrame.lt : Compare DataFrames for strictly less than inequality elementwise. DataFrame.ge : Compare DataFrames for greater than inequality or equality elementwise. DataFrame.gt : Compare DataFrames for strictly greater than inequality elementwise.
Notes
Mismatched indices will be unioned together.
NaN values are considered different (i.e. NaN != NaN).
>>> df = pd.DataFrame({'cost': [250, 150, 100],... 'revenue': [100, 250, 300]},
... index=['A', 'B', 'C'])
>>> df
cost revenue
A 250 100
B 150 250
C 100 300
Comparison with a scalar, using either the operator or method:
>>> df == 100
cost revenue
A False True
B False False
C True False
>>> df.eq(100)
cost revenue
A False True
B False False
C True False
When other is a :class:Series, the columns of a DataFrame are aligned
with the index of other and broadcast:
>>> df != pd.Series([100, 250], index=["cost", "revenue"])
cost revenue
A True True
B True False
C False True
Use the method to control the broadcast axis:
>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
cost revenue
A True False
B True True
C True True
D True True
When comparing to an arbitrary sequence, the number of columns must
match the number elements in other:
>>> df == [250, 100]
cost revenue
A True True
B False False
C False False
Use the method to control the axis:
>>> df.eq([250, 250, 100], axis='index')
cost revenue
A True False
B False True
C True False
Compare to a DataFrame of different shape.
>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
... index=['A', 'B', 'C', 'D'])
>>> other
revenue
A 300
B 250
C 100
D 150
>>> df.gt(other)
cost revenue
A False False
B False False
C False True
D False False
Compare to a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
... 'revenue': [100, 250, 300, 200, 175, 225]},
... index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
... ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
cost revenue
Q1 A 250 100
B 150 250
C 100 300
Q2 A 150 200
B 300 175
C 220 225
>>> df.le(df_multindex, level=1)
cost revenue
Q1 A True True
B True True
C True True
Q2 A False True
B True False
C True False
ne(other, axis='columns', level=None)
Get Not equal to of dataframe and other, element-wise (binary operator ne).
Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison
operators.
Equivalent to ==, !=, <=, <, >=, > with support to choose axis
(rows or columns) and level for comparison.
other(scalar, sequence, Series, or DataFrame) — Any single or multiple element data structure, or list-like object.axis({0 or 'index', 1 or 'columns'}, default 'columns') — Whether to compare by the index (0 or 'index') or columns(1 or 'columns').level(int or label) — Broadcast across a level, matching Index values on the passedMultiIndex level.
Result of the comparison.
DataFrame.eq : Compare DataFrames for equality elementwise.DataFrame.ne : Compare DataFrames for inequality elementwise. DataFrame.le : Compare DataFrames for less than inequality or equality elementwise. DataFrame.lt : Compare DataFrames for strictly less than inequality elementwise. DataFrame.ge : Compare DataFrames for greater than inequality or equality elementwise. DataFrame.gt : Compare DataFrames for strictly greater than inequality elementwise.
Notes
Mismatched indices will be unioned together.
NaN values are considered different (i.e. NaN != NaN).
>>> df = pd.DataFrame({'cost': [250, 150, 100],... 'revenue': [100, 250, 300]},
... index=['A', 'B', 'C'])
>>> df
cost revenue
A 250 100
B 150 250
C 100 300
Comparison with a scalar, using either the operator or method:
>>> df == 100
cost revenue
A False True
B False False
C True False
>>> df.eq(100)
cost revenue
A False True
B False False
C True False
When other is a :class:Series, the columns of a DataFrame are aligned
with the index of other and broadcast:
>>> df != pd.Series([100, 250], index=["cost", "revenue"])
cost revenue
A True True
B True False
C False True
Use the method to control the broadcast axis:
>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
cost revenue
A True False
B True True
C True True
D True True
When comparing to an arbitrary sequence, the number of columns must
match the number elements in other:
>>> df == [250, 100]
cost revenue
A True True
B False False
C False False
Use the method to control the axis:
>>> df.eq([250, 250, 100], axis='index')
cost revenue
A True False
B False True
C True False
Compare to a DataFrame of different shape.
>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
... index=['A', 'B', 'C', 'D'])
>>> other
revenue
A 300
B 250
C 100
D 150
>>> df.gt(other)
cost revenue
A False False
B False False
C False True
D False False
Compare to a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
... 'revenue': [100, 250, 300, 200, 175, 225]},
... index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
... ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
cost revenue
Q1 A 250 100
B 150 250
C 100 300
Q2 A 150 200
B 300 175
C 220 225
>>> df.le(df_multindex, level=1)
cost revenue
Q1 A True True
B True True
C True True
Q2 A False True
B True False
C True False
le(other, axis='columns', level=None)
Get Less than or equal to of dataframe and other, element-wise (binary operator le).
Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison
operators.
Equivalent to ==, !=, <=, <, >=, > with support to choose axis
(rows or columns) and level for comparison.
other(scalar, sequence, Series, or DataFrame) — Any single or multiple element data structure, or list-like object.axis({0 or 'index', 1 or 'columns'}, default 'columns') — Whether to compare by the index (0 or 'index') or columns(1 or 'columns').level(int or label) — Broadcast across a level, matching Index values on the passedMultiIndex level.
Result of the comparison.
DataFrame.eq : Compare DataFrames for equality elementwise.DataFrame.ne : Compare DataFrames for inequality elementwise. DataFrame.le : Compare DataFrames for less than inequality or equality elementwise. DataFrame.lt : Compare DataFrames for strictly less than inequality elementwise. DataFrame.ge : Compare DataFrames for greater than inequality or equality elementwise. DataFrame.gt : Compare DataFrames for strictly greater than inequality elementwise.
Notes
Mismatched indices will be unioned together.
NaN values are considered different (i.e. NaN != NaN).
>>> df = pd.DataFrame({'cost': [250, 150, 100],... 'revenue': [100, 250, 300]},
... index=['A', 'B', 'C'])
>>> df
cost revenue
A 250 100
B 150 250
C 100 300
Comparison with a scalar, using either the operator or method:
>>> df == 100
cost revenue
A False True
B False False
C True False
>>> df.eq(100)
cost revenue
A False True
B False False
C True False
When other is a :class:Series, the columns of a DataFrame are aligned
with the index of other and broadcast:
>>> df != pd.Series([100, 250], index=["cost", "revenue"])
cost revenue
A True True
B True False
C False True
Use the method to control the broadcast axis:
>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
cost revenue
A True False
B True True
C True True
D True True
When comparing to an arbitrary sequence, the number of columns must
match the number elements in other:
>>> df == [250, 100]
cost revenue
A True True
B False False
C False False
Use the method to control the axis:
>>> df.eq([250, 250, 100], axis='index')
cost revenue
A True False
B False True
C True False
Compare to a DataFrame of different shape.
>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
... index=['A', 'B', 'C', 'D'])
>>> other
revenue
A 300
B 250
C 100
D 150
>>> df.gt(other)
cost revenue
A False False
B False False
C False True
D False False
Compare to a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
... 'revenue': [100, 250, 300, 200, 175, 225]},
... index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
... ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
cost revenue
Q1 A 250 100
B 150 250
C 100 300
Q2 A 150 200
B 300 175
C 220 225
>>> df.le(df_multindex, level=1)
cost revenue
Q1 A True True
B True True
C True True
Q2 A False True
B True False
C True False
lt(other, axis='columns', level=None)
Get Less than of dataframe and other, element-wise (binary operator lt).
Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison
operators.
Equivalent to ==, !=, <=, <, >=, > with support to choose axis
(rows or columns) and level for comparison.
other(scalar, sequence, Series, or DataFrame) — Any single or multiple element data structure, or list-like object.axis({0 or 'index', 1 or 'columns'}, default 'columns') — Whether to compare by the index (0 or 'index') or columns(1 or 'columns').level(int or label) — Broadcast across a level, matching Index values on the passedMultiIndex level.
Result of the comparison.
DataFrame.eq : Compare DataFrames for equality elementwise.DataFrame.ne : Compare DataFrames for inequality elementwise. DataFrame.le : Compare DataFrames for less than inequality or equality elementwise. DataFrame.lt : Compare DataFrames for strictly less than inequality elementwise. DataFrame.ge : Compare DataFrames for greater than inequality or equality elementwise. DataFrame.gt : Compare DataFrames for strictly greater than inequality elementwise.
Notes
Mismatched indices will be unioned together.
NaN values are considered different (i.e. NaN != NaN).
>>> df = pd.DataFrame({'cost': [250, 150, 100],... 'revenue': [100, 250, 300]},
... index=['A', 'B', 'C'])
>>> df
cost revenue
A 250 100
B 150 250
C 100 300
Comparison with a scalar, using either the operator or method:
>>> df == 100
cost revenue
A False True
B False False
C True False
>>> df.eq(100)
cost revenue
A False True
B False False
C True False
When other is a :class:Series, the columns of a DataFrame are aligned
with the index of other and broadcast:
>>> df != pd.Series([100, 250], index=["cost", "revenue"])
cost revenue
A True True
B True False
C False True
Use the method to control the broadcast axis:
>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
cost revenue
A True False
B True True
C True True
D True True
When comparing to an arbitrary sequence, the number of columns must
match the number elements in other:
>>> df == [250, 100]
cost revenue
A True True
B False False
C False False
Use the method to control the axis:
>>> df.eq([250, 250, 100], axis='index')
cost revenue
A True False
B False True
C True False
Compare to a DataFrame of different shape.
>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
... index=['A', 'B', 'C', 'D'])
>>> other
revenue
A 300
B 250
C 100
D 150
>>> df.gt(other)
cost revenue
A False False
B False False
C False True
D False False
Compare to a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
... 'revenue': [100, 250, 300, 200, 175, 225]},
... index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
... ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
cost revenue
Q1 A 250 100
B 150 250
C 100 300
Q2 A 150 200
B 300 175
C 220 225
>>> df.le(df_multindex, level=1)
cost revenue
Q1 A True True
B True True
C True True
Q2 A False True
B True False
C True False
ge(other, axis='columns', level=None)
Get Greater than or equal to of dataframe and other, element-wise (binary operator ge).
Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison
operators.
Equivalent to ==, !=, <=, <, >=, > with support to choose axis
(rows or columns) and level for comparison.
other(scalar, sequence, Series, or DataFrame) — Any single or multiple element data structure, or list-like object.axis({0 or 'index', 1 or 'columns'}, default 'columns') — Whether to compare by the index (0 or 'index') or columns(1 or 'columns').level(int or label) — Broadcast across a level, matching Index values on the passedMultiIndex level.
Result of the comparison.
DataFrame.eq : Compare DataFrames for equality elementwise.DataFrame.ne : Compare DataFrames for inequality elementwise. DataFrame.le : Compare DataFrames for less than inequality or equality elementwise. DataFrame.lt : Compare DataFrames for strictly less than inequality elementwise. DataFrame.ge : Compare DataFrames for greater than inequality or equality elementwise. DataFrame.gt : Compare DataFrames for strictly greater than inequality elementwise.
Notes
Mismatched indices will be unioned together.
NaN values are considered different (i.e. NaN != NaN).
>>> df = pd.DataFrame({'cost': [250, 150, 100],... 'revenue': [100, 250, 300]},
... index=['A', 'B', 'C'])
>>> df
cost revenue
A 250 100
B 150 250
C 100 300
Comparison with a scalar, using either the operator or method:
>>> df == 100
cost revenue
A False True
B False False
C True False
>>> df.eq(100)
cost revenue
A False True
B False False
C True False
When other is a :class:Series, the columns of a DataFrame are aligned
with the index of other and broadcast:
>>> df != pd.Series([100, 250], index=["cost", "revenue"])
cost revenue
A True True
B True False
C False True
Use the method to control the broadcast axis:
>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
cost revenue
A True False
B True True
C True True
D True True
When comparing to an arbitrary sequence, the number of columns must
match the number elements in other:
>>> df == [250, 100]
cost revenue
A True True
B False False
C False False
Use the method to control the axis:
>>> df.eq([250, 250, 100], axis='index')
cost revenue
A True False
B False True
C True False
Compare to a DataFrame of different shape.
>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
... index=['A', 'B', 'C', 'D'])
>>> other
revenue
A 300
B 250
C 100
D 150
>>> df.gt(other)
cost revenue
A False False
B False False
C False True
D False False
Compare to a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
... 'revenue': [100, 250, 300, 200, 175, 225]},
... index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
... ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
cost revenue
Q1 A 250 100
B 150 250
C 100 300
Q2 A 150 200
B 300 175
C 220 225
>>> df.le(df_multindex, level=1)
cost revenue
Q1 A True True
B True True
C True True
Q2 A False True
B True False
C True False
gt(other, axis='columns', level=None)
Get Greater than of dataframe and other, element-wise (binary operator gt).
Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison
operators.
Equivalent to ==, !=, <=, <, >=, > with support to choose axis
(rows or columns) and level for comparison.
other(scalar, sequence, Series, or DataFrame) — Any single or multiple element data structure, or list-like object.axis({0 or 'index', 1 or 'columns'}, default 'columns') — Whether to compare by the index (0 or 'index') or columns(1 or 'columns').level(int or label) — Broadcast across a level, matching Index values on the passedMultiIndex level.
Result of the comparison.
DataFrame.eq : Compare DataFrames for equality elementwise.DataFrame.ne : Compare DataFrames for inequality elementwise. DataFrame.le : Compare DataFrames for less than inequality or equality elementwise. DataFrame.lt : Compare DataFrames for strictly less than inequality elementwise. DataFrame.ge : Compare DataFrames for greater than inequality or equality elementwise. DataFrame.gt : Compare DataFrames for strictly greater than inequality elementwise.
Notes
Mismatched indices will be unioned together.
NaN values are considered different (i.e. NaN != NaN).
>>> df = pd.DataFrame({'cost': [250, 150, 100],... 'revenue': [100, 250, 300]},
... index=['A', 'B', 'C'])
>>> df
cost revenue
A 250 100
B 150 250
C 100 300
Comparison with a scalar, using either the operator or method:
>>> df == 100
cost revenue
A False True
B False False
C True False
>>> df.eq(100)
cost revenue
A False True
B False False
C True False
When other is a :class:Series, the columns of a DataFrame are aligned
with the index of other and broadcast:
>>> df != pd.Series([100, 250], index=["cost", "revenue"])
cost revenue
A True True
B True False
C False True
Use the method to control the broadcast axis:
>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
cost revenue
A True False
B True True
C True True
D True True
When comparing to an arbitrary sequence, the number of columns must
match the number elements in other:
>>> df == [250, 100]
cost revenue
A True True
B False False
C False False
Use the method to control the axis:
>>> df.eq([250, 250, 100], axis='index')
cost revenue
A True False
B False True
C True False
Compare to a DataFrame of different shape.
>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
... index=['A', 'B', 'C', 'D'])
>>> other
revenue
A 300
B 250
C 100
D 150
>>> df.gt(other)
cost revenue
A False False
B False False
C False True
D False False
Compare to a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
... 'revenue': [100, 250, 300, 200, 175, 225]},
... index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
... ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
cost revenue
Q1 A 250 100
B 150 250
C 100 300
Q2 A 150 200
B 300 175
C 220 225
>>> df.le(df_multindex, level=1)
cost revenue
Q1 A True True
B True True
C True True
Q2 A False True
B True False
C True False
add(other, axis='columns', level=None, fill_value=None)
Get Addition of dataframe and other, element-wise (binary operator add).
Equivalent to dataframe + other, but with support to substitute a fill_value
for missing data in one of the inputs. With reverse version, radd.
Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to
arithmetic operators: +, -, *, /, //, %, **.
other(scalar, sequence, Series, dict or DataFrame) — Any single or multiple element data structure, or list-like object.axis({0 or 'index', 1 or 'columns'}) — Whether to compare by the index (0 or 'index') or columns.(1 or 'columns'). For Series input, axis to match Series index on.level(int or label) — Broadcast across a level, matching Index values on thepassed MultiIndex level.fill_value(float or None, default None) — Fill existing missing (NaN) values, and any new element needed forsuccessful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
Result of the arithmetic operation.
DataFrame.add : Add DataFrames.DataFrame.sub : Subtract DataFrames. DataFrame.mul : Multiply DataFrames. DataFrame.div : Divide DataFrames (float division). DataFrame.truediv : Divide DataFrames (float division). DataFrame.floordiv : Divide DataFrames (integer division). DataFrame.mod : Calculate modulo (remainder after division). DataFrame.pow : Calculate exponential power.
Notes
Mismatched indices will be unioned together.
>>> df = pd.DataFrame({'angles': [0, 3, 4],... 'degrees': [360, 180, 360]},
... index=['circle', 'triangle', 'rectangle'])
>>> df
angles degrees
circle 0 360
triangle 3 180
rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
>>> df.add(1)
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10)
angles degrees
circle 0.0 36.0
triangle 0.3 18.0
rectangle 0.4 36.0
>>> df.rdiv(10)
angles degrees
circle inf 0.027778
triangle 3.333333 0.055556
rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2]
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub([1, 2], axis='columns')
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
... axis='index')
angles degrees
circle -1 359
triangle 2 179
rectangle 3 359
Multiply a dictionary by axis.
>>> df.mul({'angles': 0, 'degrees': 2})
angles degrees
circle 0 720
triangle 0 360
rectangle 0 720
>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
angles degrees
circle 0 0
triangle 6 360
rectangle 12 1080
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]},
... index=['circle', 'triangle', 'rectangle'])
>>> other
angles
circle 0
triangle 3
rectangle 4
>>> df * other
angles degrees
circle 0 NaN
triangle 9 NaN
rectangle 16 NaN
>>> df.mul(other, fill_value=0)
angles degrees
circle 0 0.0
triangle 9 0.0
rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
... 'degrees': [360, 180, 360, 360, 540, 720]},
... index=[['A', 'A', 'A', 'B', 'B', 'B'],
... ['circle', 'triangle', 'rectangle',
... 'square', 'pentagon', 'hexagon']])
>>> df_multindex
angles degrees
A circle 0 360
triangle 3 180
rectangle 4 360
B square 4 360
pentagon 5 540
hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0)
angles degrees
A circle NaN 1.0
triangle 1.0 1.0
rectangle 1.0 1.0
B square 0.0 0.0
pentagon 0.0 0.0
hexagon 0.0 0.0
radd(other, axis='columns', level=None, fill_value=None)
Get Addition of dataframe and other, element-wise (binary operator radd).
Equivalent to other + dataframe, but with support to substitute a fill_value
for missing data in one of the inputs. With reverse version, add.
Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to
arithmetic operators: +, -, *, /, //, %, **.
other(scalar, sequence, Series, dict or DataFrame) — Any single or multiple element data structure, or list-like object.axis({0 or 'index', 1 or 'columns'}) — Whether to compare by the index (0 or 'index') or columns.(1 or 'columns'). For Series input, axis to match Series index on.level(int or label) — Broadcast across a level, matching Index values on thepassed MultiIndex level.fill_value(float or None, default None) — Fill existing missing (NaN) values, and any new element needed forsuccessful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
Result of the arithmetic operation.
DataFrame.add : Add DataFrames.DataFrame.sub : Subtract DataFrames. DataFrame.mul : Multiply DataFrames. DataFrame.div : Divide DataFrames (float division). DataFrame.truediv : Divide DataFrames (float division). DataFrame.floordiv : Divide DataFrames (integer division). DataFrame.mod : Calculate modulo (remainder after division). DataFrame.pow : Calculate exponential power.
Notes
Mismatched indices will be unioned together.
>>> df = pd.DataFrame({'angles': [0, 3, 4],... 'degrees': [360, 180, 360]},
... index=['circle', 'triangle', 'rectangle'])
>>> df
angles degrees
circle 0 360
triangle 3 180
rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
>>> df.add(1)
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10)
angles degrees
circle 0.0 36.0
triangle 0.3 18.0
rectangle 0.4 36.0
>>> df.rdiv(10)
angles degrees
circle inf 0.027778
triangle 3.333333 0.055556
rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2]
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub([1, 2], axis='columns')
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
... axis='index')
angles degrees
circle -1 359
triangle 2 179
rectangle 3 359
Multiply a dictionary by axis.
>>> df.mul({'angles': 0, 'degrees': 2})
angles degrees
circle 0 720
triangle 0 360
rectangle 0 720
>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
angles degrees
circle 0 0
triangle 6 360
rectangle 12 1080
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]},
... index=['circle', 'triangle', 'rectangle'])
>>> other
angles
circle 0
triangle 3
rectangle 4
>>> df * other
angles degrees
circle 0 NaN
triangle 9 NaN
rectangle 16 NaN
>>> df.mul(other, fill_value=0)
angles degrees
circle 0 0.0
triangle 9 0.0
rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
... 'degrees': [360, 180, 360, 360, 540, 720]},
... index=[['A', 'A', 'A', 'B', 'B', 'B'],
... ['circle', 'triangle', 'rectangle',
... 'square', 'pentagon', 'hexagon']])
>>> df_multindex
angles degrees
A circle 0 360
triangle 3 180
rectangle 4 360
B square 4 360
pentagon 5 540
hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0)
angles degrees
A circle NaN 1.0
triangle 1.0 1.0
rectangle 1.0 1.0
B square 0.0 0.0
pentagon 0.0 0.0
hexagon 0.0 0.0
sub(other, axis='columns', level=None, fill_value=None)
Get Subtraction of dataframe and other, element-wise (binary operator sub).
Equivalent to dataframe - other, but with support to substitute a fill_value
for missing data in one of the inputs. With reverse version, rsub.
Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to
arithmetic operators: +, -, *, /, //, %, **.
other(scalar, sequence, Series, dict or DataFrame) — Any single or multiple element data structure, or list-like object.axis({0 or 'index', 1 or 'columns'}) — Whether to compare by the index (0 or 'index') or columns.(1 or 'columns'). For Series input, axis to match Series index on.level(int or label) — Broadcast across a level, matching Index values on thepassed MultiIndex level.fill_value(float or None, default None) — Fill existing missing (NaN) values, and any new element needed forsuccessful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
Result of the arithmetic operation.
DataFrame.add : Add DataFrames.DataFrame.sub : Subtract DataFrames. DataFrame.mul : Multiply DataFrames. DataFrame.div : Divide DataFrames (float division). DataFrame.truediv : Divide DataFrames (float division). DataFrame.floordiv : Divide DataFrames (integer division). DataFrame.mod : Calculate modulo (remainder after division). DataFrame.pow : Calculate exponential power.
Notes
Mismatched indices will be unioned together.
>>> df = pd.DataFrame({'angles': [0, 3, 4],... 'degrees': [360, 180, 360]},
... index=['circle', 'triangle', 'rectangle'])
>>> df
angles degrees
circle 0 360
triangle 3 180
rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
>>> df.add(1)
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10)
angles degrees
circle 0.0 36.0
triangle 0.3 18.0
rectangle 0.4 36.0
>>> df.rdiv(10)
angles degrees
circle inf 0.027778
triangle 3.333333 0.055556
rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2]
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub([1, 2], axis='columns')
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
... axis='index')
angles degrees
circle -1 359
triangle 2 179
rectangle 3 359
Multiply a dictionary by axis.
>>> df.mul({'angles': 0, 'degrees': 2})
angles degrees
circle 0 720
triangle 0 360
rectangle 0 720
>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
angles degrees
circle 0 0
triangle 6 360
rectangle 12 1080
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]},
... index=['circle', 'triangle', 'rectangle'])
>>> other
angles
circle 0
triangle 3
rectangle 4
>>> df * other
angles degrees
circle 0 NaN
triangle 9 NaN
rectangle 16 NaN
>>> df.mul(other, fill_value=0)
angles degrees
circle 0 0.0
triangle 9 0.0
rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
... 'degrees': [360, 180, 360, 360, 540, 720]},
... index=[['A', 'A', 'A', 'B', 'B', 'B'],
... ['circle', 'triangle', 'rectangle',
... 'square', 'pentagon', 'hexagon']])
>>> df_multindex
angles degrees
A circle 0 360
triangle 3 180
rectangle 4 360
B square 4 360
pentagon 5 540
hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0)
angles degrees
A circle NaN 1.0
triangle 1.0 1.0
rectangle 1.0 1.0
B square 0.0 0.0
pentagon 0.0 0.0
hexagon 0.0 0.0
rsub(other, axis='columns', level=None, fill_value=None)
Get Subtraction of dataframe and other, element-wise (binary operator rsub).
Equivalent to other - dataframe, but with support to substitute a fill_value
for missing data in one of the inputs. With reverse version, sub.
Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to
arithmetic operators: +, -, *, /, //, %, **.
other(scalar, sequence, Series, dict or DataFrame) — Any single or multiple element data structure, or list-like object.axis({0 or 'index', 1 or 'columns'}) — Whether to compare by the index (0 or 'index') or columns.(1 or 'columns'). For Series input, axis to match Series index on.level(int or label) — Broadcast across a level, matching Index values on thepassed MultiIndex level.fill_value(float or None, default None) — Fill existing missing (NaN) values, and any new element needed forsuccessful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
Result of the arithmetic operation.
DataFrame.add : Add DataFrames.DataFrame.sub : Subtract DataFrames. DataFrame.mul : Multiply DataFrames. DataFrame.div : Divide DataFrames (float division). DataFrame.truediv : Divide DataFrames (float division). DataFrame.floordiv : Divide DataFrames (integer division). DataFrame.mod : Calculate modulo (remainder after division). DataFrame.pow : Calculate exponential power.
Notes
Mismatched indices will be unioned together.
>>> df = pd.DataFrame({'angles': [0, 3, 4],... 'degrees': [360, 180, 360]},
... index=['circle', 'triangle', 'rectangle'])
>>> df
angles degrees
circle 0 360
triangle 3 180
rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
>>> df.add(1)
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10)
angles degrees
circle 0.0 36.0
triangle 0.3 18.0
rectangle 0.4 36.0
>>> df.rdiv(10)
angles degrees
circle inf 0.027778
triangle 3.333333 0.055556
rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2]
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub([1, 2], axis='columns')
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
... axis='index')
angles degrees
circle -1 359
triangle 2 179
rectangle 3 359
Multiply a dictionary by axis.
>>> df.mul({'angles': 0, 'degrees': 2})
angles degrees
circle 0 720
triangle 0 360
rectangle 0 720
>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
angles degrees
circle 0 0
triangle 6 360
rectangle 12 1080
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]},
... index=['circle', 'triangle', 'rectangle'])
>>> other
angles
circle 0
triangle 3
rectangle 4
>>> df * other
angles degrees
circle 0 NaN
triangle 9 NaN
rectangle 16 NaN
>>> df.mul(other, fill_value=0)
angles degrees
circle 0 0.0
triangle 9 0.0
rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
... 'degrees': [360, 180, 360, 360, 540, 720]},
... index=[['A', 'A', 'A', 'B', 'B', 'B'],
... ['circle', 'triangle', 'rectangle',
... 'square', 'pentagon', 'hexagon']])
>>> df_multindex
angles degrees
A circle 0 360
triangle 3 180
rectangle 4 360
B square 4 360
pentagon 5 540
hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0)
angles degrees
A circle NaN 1.0
triangle 1.0 1.0
rectangle 1.0 1.0
B square 0.0 0.0
pentagon 0.0 0.0
hexagon 0.0 0.0
mul(other, axis='columns', level=None, fill_value=None)
Get Multiplication of dataframe and other, element-wise (binary operator mul).
Equivalent to dataframe * other, but with support to substitute a fill_value
for missing data in one of the inputs. With reverse version, rmul.
Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to
arithmetic operators: +, -, *, /, //, %, **.
other(scalar, sequence, Series, dict or DataFrame) — Any single or multiple element data structure, or list-like object.axis({0 or 'index', 1 or 'columns'}) — Whether to compare by the index (0 or 'index') or columns.(1 or 'columns'). For Series input, axis to match Series index on.level(int or label) — Broadcast across a level, matching Index values on thepassed MultiIndex level.fill_value(float or None, default None) — Fill existing missing (NaN) values, and any new element needed forsuccessful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
Result of the arithmetic operation.
DataFrame.add : Add DataFrames.DataFrame.sub : Subtract DataFrames. DataFrame.mul : Multiply DataFrames. DataFrame.div : Divide DataFrames (float division). DataFrame.truediv : Divide DataFrames (float division). DataFrame.floordiv : Divide DataFrames (integer division). DataFrame.mod : Calculate modulo (remainder after division). DataFrame.pow : Calculate exponential power.
Notes
Mismatched indices will be unioned together.
>>> df = pd.DataFrame({'angles': [0, 3, 4],... 'degrees': [360, 180, 360]},
... index=['circle', 'triangle', 'rectangle'])
>>> df
angles degrees
circle 0 360
triangle 3 180
rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
>>> df.add(1)
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10)
angles degrees
circle 0.0 36.0
triangle 0.3 18.0
rectangle 0.4 36.0
>>> df.rdiv(10)
angles degrees
circle inf 0.027778
triangle 3.333333 0.055556
rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2]
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub([1, 2], axis='columns')
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
... axis='index')
angles degrees
circle -1 359
triangle 2 179
rectangle 3 359
Multiply a dictionary by axis.
>>> df.mul({'angles': 0, 'degrees': 2})
angles degrees
circle 0 720
triangle 0 360
rectangle 0 720
>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
angles degrees
circle 0 0
triangle 6 360
rectangle 12 1080
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]},
... index=['circle', 'triangle', 'rectangle'])
>>> other
angles
circle 0
triangle 3
rectangle 4
>>> df * other
angles degrees
circle 0 NaN
triangle 9 NaN
rectangle 16 NaN
>>> df.mul(other, fill_value=0)
angles degrees
circle 0 0.0
triangle 9 0.0
rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
... 'degrees': [360, 180, 360, 360, 540, 720]},
... index=[['A', 'A', 'A', 'B', 'B', 'B'],
... ['circle', 'triangle', 'rectangle',
... 'square', 'pentagon', 'hexagon']])
>>> df_multindex
angles degrees
A circle 0 360
triangle 3 180
rectangle 4 360
B square 4 360
pentagon 5 540
hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0)
angles degrees
A circle NaN 1.0
triangle 1.0 1.0
rectangle 1.0 1.0
B square 0.0 0.0
pentagon 0.0 0.0
hexagon 0.0 0.0
rmul(other, axis='columns', level=None, fill_value=None)
Get Multiplication of dataframe and other, element-wise (binary operator rmul).
Equivalent to other * dataframe, but with support to substitute a fill_value
for missing data in one of the inputs. With reverse version, mul.
Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to
arithmetic operators: +, -, *, /, //, %, **.
other(scalar, sequence, Series, dict or DataFrame) — Any single or multiple element data structure, or list-like object.axis({0 or 'index', 1 or 'columns'}) — Whether to compare by the index (0 or 'index') or columns.(1 or 'columns'). For Series input, axis to match Series index on.level(int or label) — Broadcast across a level, matching Index values on thepassed MultiIndex level.fill_value(float or None, default None) — Fill existing missing (NaN) values, and any new element needed forsuccessful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
Result of the arithmetic operation.
DataFrame.add : Add DataFrames.DataFrame.sub : Subtract DataFrames. DataFrame.mul : Multiply DataFrames. DataFrame.div : Divide DataFrames (float division). DataFrame.truediv : Divide DataFrames (float division). DataFrame.floordiv : Divide DataFrames (integer division). DataFrame.mod : Calculate modulo (remainder after division). DataFrame.pow : Calculate exponential power.
Notes
Mismatched indices will be unioned together.
>>> df = pd.DataFrame({'angles': [0, 3, 4],... 'degrees': [360, 180, 360]},
... index=['circle', 'triangle', 'rectangle'])
>>> df
angles degrees
circle 0 360
triangle 3 180
rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
>>> df.add(1)
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10)
angles degrees
circle 0.0 36.0
triangle 0.3 18.0
rectangle 0.4 36.0
>>> df.rdiv(10)
angles degrees
circle inf 0.027778
triangle 3.333333 0.055556
rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2]
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub([1, 2], axis='columns')
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
... axis='index')
angles degrees
circle -1 359
triangle 2 179
rectangle 3 359
Multiply a dictionary by axis.
>>> df.mul({'angles': 0, 'degrees': 2})
angles degrees
circle 0 720
triangle 0 360
rectangle 0 720
>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
angles degrees
circle 0 0
triangle 6 360
rectangle 12 1080
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]},
... index=['circle', 'triangle', 'rectangle'])
>>> other
angles
circle 0
triangle 3
rectangle 4
>>> df * other
angles degrees
circle 0 NaN
triangle 9 NaN
rectangle 16 NaN
>>> df.mul(other, fill_value=0)
angles degrees
circle 0 0.0
triangle 9 0.0
rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
... 'degrees': [360, 180, 360, 360, 540, 720]},
... index=[['A', 'A', 'A', 'B', 'B', 'B'],
... ['circle', 'triangle', 'rectangle',
... 'square', 'pentagon', 'hexagon']])
>>> df_multindex
angles degrees
A circle 0 360
triangle 3 180
rectangle 4 360
B square 4 360
pentagon 5 540
hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0)
angles degrees
A circle NaN 1.0
triangle 1.0 1.0
rectangle 1.0 1.0
B square 0.0 0.0
pentagon 0.0 0.0
hexagon 0.0 0.0
truediv(other, axis='columns', level=None, fill_value=None)
Get Floating division of dataframe and other, element-wise (binary operator truediv).
Equivalent to dataframe / other, but with support to substitute a fill_value
for missing data in one of the inputs. With reverse version, rtruediv.
Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to
arithmetic operators: +, -, *, /, //, %, **.
other(scalar, sequence, Series, dict or DataFrame) — Any single or multiple element data structure, or list-like object.axis({0 or 'index', 1 or 'columns'}) — Whether to compare by the index (0 or 'index') or columns.(1 or 'columns'). For Series input, axis to match Series index on.level(int or label) — Broadcast across a level, matching Index values on thepassed MultiIndex level.fill_value(float or None, default None) — Fill existing missing (NaN) values, and any new element needed forsuccessful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
Result of the arithmetic operation.
DataFrame.add : Add DataFrames.DataFrame.sub : Subtract DataFrames. DataFrame.mul : Multiply DataFrames. DataFrame.div : Divide DataFrames (float division). DataFrame.truediv : Divide DataFrames (float division). DataFrame.floordiv : Divide DataFrames (integer division). DataFrame.mod : Calculate modulo (remainder after division). DataFrame.pow : Calculate exponential power.
Notes
Mismatched indices will be unioned together.
>>> df = pd.DataFrame({'angles': [0, 3, 4],... 'degrees': [360, 180, 360]},
... index=['circle', 'triangle', 'rectangle'])
>>> df
angles degrees
circle 0 360
triangle 3 180
rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
>>> df.add(1)
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10)
angles degrees
circle 0.0 36.0
triangle 0.3 18.0
rectangle 0.4 36.0
>>> df.rdiv(10)
angles degrees
circle inf 0.027778
triangle 3.333333 0.055556
rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2]
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub([1, 2], axis='columns')
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
... axis='index')
angles degrees
circle -1 359
triangle 2 179
rectangle 3 359
Multiply a dictionary by axis.
>>> df.mul({'angles': 0, 'degrees': 2})
angles degrees
circle 0 720
triangle 0 360
rectangle 0 720
>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
angles degrees
circle 0 0
triangle 6 360
rectangle 12 1080
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]},
... index=['circle', 'triangle', 'rectangle'])
>>> other
angles
circle 0
triangle 3
rectangle 4
>>> df * other
angles degrees
circle 0 NaN
triangle 9 NaN
rectangle 16 NaN
>>> df.mul(other, fill_value=0)
angles degrees
circle 0 0.0
triangle 9 0.0
rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
... 'degrees': [360, 180, 360, 360, 540, 720]},
... index=[['A', 'A', 'A', 'B', 'B', 'B'],
... ['circle', 'triangle', 'rectangle',
... 'square', 'pentagon', 'hexagon']])
>>> df_multindex
angles degrees
A circle 0 360
triangle 3 180
rectangle 4 360
B square 4 360
pentagon 5 540
hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0)
angles degrees
A circle NaN 1.0
triangle 1.0 1.0
rectangle 1.0 1.0
B square 0.0 0.0
pentagon 0.0 0.0
hexagon 0.0 0.0
rtruediv(other, axis='columns', level=None, fill_value=None)
Get Floating division of dataframe and other, element-wise (binary operator rtruediv).
Equivalent to other / dataframe, but with support to substitute a fill_value
for missing data in one of the inputs. With reverse version, truediv.
Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to
arithmetic operators: +, -, *, /, //, %, **.
other(scalar, sequence, Series, dict or DataFrame) — Any single or multiple element data structure, or list-like object.axis({0 or 'index', 1 or 'columns'}) — Whether to compare by the index (0 or 'index') or columns.(1 or 'columns'). For Series input, axis to match Series index on.level(int or label) — Broadcast across a level, matching Index values on thepassed MultiIndex level.fill_value(float or None, default None) — Fill existing missing (NaN) values, and any new element needed forsuccessful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
Result of the arithmetic operation.
DataFrame.add : Add DataFrames.DataFrame.sub : Subtract DataFrames. DataFrame.mul : Multiply DataFrames. DataFrame.div : Divide DataFrames (float division). DataFrame.truediv : Divide DataFrames (float division). DataFrame.floordiv : Divide DataFrames (integer division). DataFrame.mod : Calculate modulo (remainder after division). DataFrame.pow : Calculate exponential power.
Notes
Mismatched indices will be unioned together.
>>> df = pd.DataFrame({'angles': [0, 3, 4],... 'degrees': [360, 180, 360]},
... index=['circle', 'triangle', 'rectangle'])
>>> df
angles degrees
circle 0 360
triangle 3 180
rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
>>> df.add(1)
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10)
angles degrees
circle 0.0 36.0
triangle 0.3 18.0
rectangle 0.4 36.0
>>> df.rdiv(10)
angles degrees
circle inf 0.027778
triangle 3.333333 0.055556
rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2]
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub([1, 2], axis='columns')
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
... axis='index')
angles degrees
circle -1 359
triangle 2 179
rectangle 3 359
Multiply a dictionary by axis.
>>> df.mul({'angles': 0, 'degrees': 2})
angles degrees
circle 0 720
triangle 0 360
rectangle 0 720
>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
angles degrees
circle 0 0
triangle 6 360
rectangle 12 1080
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]},
... index=['circle', 'triangle', 'rectangle'])
>>> other
angles
circle 0
triangle 3
rectangle 4
>>> df * other
angles degrees
circle 0 NaN
triangle 9 NaN
rectangle 16 NaN
>>> df.mul(other, fill_value=0)
angles degrees
circle 0 0.0
triangle 9 0.0
rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
... 'degrees': [360, 180, 360, 360, 540, 720]},
... index=[['A', 'A', 'A', 'B', 'B', 'B'],
... ['circle', 'triangle', 'rectangle',
... 'square', 'pentagon', 'hexagon']])
>>> df_multindex
angles degrees
A circle 0 360
triangle 3 180
rectangle 4 360
B square 4 360
pentagon 5 540
hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0)
angles degrees
A circle NaN 1.0
triangle 1.0 1.0
rectangle 1.0 1.0
B square 0.0 0.0
pentagon 0.0 0.0
hexagon 0.0 0.0
floordiv(other, axis='columns', level=None, fill_value=None)
Get Integer division of dataframe and other, element-wise (binary operator floordiv).
Equivalent to dataframe // other, but with support to substitute a fill_value
for missing data in one of the inputs. With reverse version, rfloordiv.
Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to
arithmetic operators: +, -, *, /, //, %, **.
other(scalar, sequence, Series, dict or DataFrame) — Any single or multiple element data structure, or list-like object.axis({0 or 'index', 1 or 'columns'}) — Whether to compare by the index (0 or 'index') or columns.(1 or 'columns'). For Series input, axis to match Series index on.level(int or label) — Broadcast across a level, matching Index values on thepassed MultiIndex level.fill_value(float or None, default None) — Fill existing missing (NaN) values, and any new element needed forsuccessful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
Result of the arithmetic operation.
DataFrame.add : Add DataFrames.DataFrame.sub : Subtract DataFrames. DataFrame.mul : Multiply DataFrames. DataFrame.div : Divide DataFrames (float division). DataFrame.truediv : Divide DataFrames (float division). DataFrame.floordiv : Divide DataFrames (integer division). DataFrame.mod : Calculate modulo (remainder after division). DataFrame.pow : Calculate exponential power.
Notes
Mismatched indices will be unioned together.
>>> df = pd.DataFrame({'angles': [0, 3, 4],... 'degrees': [360, 180, 360]},
... index=['circle', 'triangle', 'rectangle'])
>>> df
angles degrees
circle 0 360
triangle 3 180
rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
>>> df.add(1)
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10)
angles degrees
circle 0.0 36.0
triangle 0.3 18.0
rectangle 0.4 36.0
>>> df.rdiv(10)
angles degrees
circle inf 0.027778
triangle 3.333333 0.055556
rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2]
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub([1, 2], axis='columns')
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
... axis='index')
angles degrees
circle -1 359
triangle 2 179
rectangle 3 359
Multiply a dictionary by axis.
>>> df.mul({'angles': 0, 'degrees': 2})
angles degrees
circle 0 720
triangle 0 360
rectangle 0 720
>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
angles degrees
circle 0 0
triangle 6 360
rectangle 12 1080
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]},
... index=['circle', 'triangle', 'rectangle'])
>>> other
angles
circle 0
triangle 3
rectangle 4
>>> df * other
angles degrees
circle 0 NaN
triangle 9 NaN
rectangle 16 NaN
>>> df.mul(other, fill_value=0)
angles degrees
circle 0 0.0
triangle 9 0.0
rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
... 'degrees': [360, 180, 360, 360, 540, 720]},
... index=[['A', 'A', 'A', 'B', 'B', 'B'],
... ['circle', 'triangle', 'rectangle',
... 'square', 'pentagon', 'hexagon']])
>>> df_multindex
angles degrees
A circle 0 360
triangle 3 180
rectangle 4 360
B square 4 360
pentagon 5 540
hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0)
angles degrees
A circle NaN 1.0
triangle 1.0 1.0
rectangle 1.0 1.0
B square 0.0 0.0
pentagon 0.0 0.0
hexagon 0.0 0.0
rfloordiv(other, axis='columns', level=None, fill_value=None)
Get Integer division of dataframe and other, element-wise (binary operator rfloordiv).
Equivalent to other // dataframe, but with support to substitute a fill_value
for missing data in one of the inputs. With reverse version, floordiv.
Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to
arithmetic operators: +, -, *, /, //, %, **.
other(scalar, sequence, Series, dict or DataFrame) — Any single or multiple element data structure, or list-like object.axis({0 or 'index', 1 or 'columns'}) — Whether to compare by the index (0 or 'index') or columns.(1 or 'columns'). For Series input, axis to match Series index on.level(int or label) — Broadcast across a level, matching Index values on thepassed MultiIndex level.fill_value(float or None, default None) — Fill existing missing (NaN) values, and any new element needed forsuccessful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
Result of the arithmetic operation.
DataFrame.add : Add DataFrames.DataFrame.sub : Subtract DataFrames. DataFrame.mul : Multiply DataFrames. DataFrame.div : Divide DataFrames (float division). DataFrame.truediv : Divide DataFrames (float division). DataFrame.floordiv : Divide DataFrames (integer division). DataFrame.mod : Calculate modulo (remainder after division). DataFrame.pow : Calculate exponential power.
Notes
Mismatched indices will be unioned together.
>>> df = pd.DataFrame({'angles': [0, 3, 4],... 'degrees': [360, 180, 360]},
... index=['circle', 'triangle', 'rectangle'])
>>> df
angles degrees
circle 0 360
triangle 3 180
rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
>>> df.add(1)
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10)
angles degrees
circle 0.0 36.0
triangle 0.3 18.0
rectangle 0.4 36.0
>>> df.rdiv(10)
angles degrees
circle inf 0.027778
triangle 3.333333 0.055556
rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2]
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub([1, 2], axis='columns')
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
... axis='index')
angles degrees
circle -1 359
triangle 2 179
rectangle 3 359
Multiply a dictionary by axis.
>>> df.mul({'angles': 0, 'degrees': 2})
angles degrees
circle 0 720
triangle 0 360
rectangle 0 720
>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
angles degrees
circle 0 0
triangle 6 360
rectangle 12 1080
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]},
... index=['circle', 'triangle', 'rectangle'])
>>> other
angles
circle 0
triangle 3
rectangle 4
>>> df * other
angles degrees
circle 0 NaN
triangle 9 NaN
rectangle 16 NaN
>>> df.mul(other, fill_value=0)
angles degrees
circle 0 0.0
triangle 9 0.0
rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
... 'degrees': [360, 180, 360, 360, 540, 720]},
... index=[['A', 'A', 'A', 'B', 'B', 'B'],
... ['circle', 'triangle', 'rectangle',
... 'square', 'pentagon', 'hexagon']])
>>> df_multindex
angles degrees
A circle 0 360
triangle 3 180
rectangle 4 360
B square 4 360
pentagon 5 540
hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0)
angles degrees
A circle NaN 1.0
triangle 1.0 1.0
rectangle 1.0 1.0
B square 0.0 0.0
pentagon 0.0 0.0
hexagon 0.0 0.0
mod(other, axis='columns', level=None, fill_value=None)
Get Modulo of dataframe and other, element-wise (binary operator mod).
Equivalent to dataframe % other, but with support to substitute a fill_value
for missing data in one of the inputs. With reverse version, rmod.
Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to
arithmetic operators: +, -, *, /, //, %, **.
other(scalar, sequence, Series, dict or DataFrame) — Any single or multiple element data structure, or list-like object.axis({0 or 'index', 1 or 'columns'}) — Whether to compare by the index (0 or 'index') or columns.(1 or 'columns'). For Series input, axis to match Series index on.level(int or label) — Broadcast across a level, matching Index values on thepassed MultiIndex level.fill_value(float or None, default None) — Fill existing missing (NaN) values, and any new element needed forsuccessful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
Result of the arithmetic operation.
DataFrame.add : Add DataFrames.DataFrame.sub : Subtract DataFrames. DataFrame.mul : Multiply DataFrames. DataFrame.div : Divide DataFrames (float division). DataFrame.truediv : Divide DataFrames (float division). DataFrame.floordiv : Divide DataFrames (integer division). DataFrame.mod : Calculate modulo (remainder after division). DataFrame.pow : Calculate exponential power.
Notes
Mismatched indices will be unioned together.
>>> df = pd.DataFrame({'angles': [0, 3, 4],... 'degrees': [360, 180, 360]},
... index=['circle', 'triangle', 'rectangle'])
>>> df
angles degrees
circle 0 360
triangle 3 180
rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
>>> df.add(1)
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10)
angles degrees
circle 0.0 36.0
triangle 0.3 18.0
rectangle 0.4 36.0
>>> df.rdiv(10)
angles degrees
circle inf 0.027778
triangle 3.333333 0.055556
rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2]
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub([1, 2], axis='columns')
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
... axis='index')
angles degrees
circle -1 359
triangle 2 179
rectangle 3 359
Multiply a dictionary by axis.
>>> df.mul({'angles': 0, 'degrees': 2})
angles degrees
circle 0 720
triangle 0 360
rectangle 0 720
>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
angles degrees
circle 0 0
triangle 6 360
rectangle 12 1080
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]},
... index=['circle', 'triangle', 'rectangle'])
>>> other
angles
circle 0
triangle 3
rectangle 4
>>> df * other
angles degrees
circle 0 NaN
triangle 9 NaN
rectangle 16 NaN
>>> df.mul(other, fill_value=0)
angles degrees
circle 0 0.0
triangle 9 0.0
rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
... 'degrees': [360, 180, 360, 360, 540, 720]},
... index=[['A', 'A', 'A', 'B', 'B', 'B'],
... ['circle', 'triangle', 'rectangle',
... 'square', 'pentagon', 'hexagon']])
>>> df_multindex
angles degrees
A circle 0 360
triangle 3 180
rectangle 4 360
B square 4 360
pentagon 5 540
hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0)
angles degrees
A circle NaN 1.0
triangle 1.0 1.0
rectangle 1.0 1.0
B square 0.0 0.0
pentagon 0.0 0.0
hexagon 0.0 0.0
rmod(other, axis='columns', level=None, fill_value=None)
Get Modulo of dataframe and other, element-wise (binary operator rmod).
Equivalent to other % dataframe, but with support to substitute a fill_value
for missing data in one of the inputs. With reverse version, mod.
Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to
arithmetic operators: +, -, *, /, //, %, **.
other(scalar, sequence, Series, dict or DataFrame) — Any single or multiple element data structure, or list-like object.axis({0 or 'index', 1 or 'columns'}) — Whether to compare by the index (0 or 'index') or columns.(1 or 'columns'). For Series input, axis to match Series index on.level(int or label) — Broadcast across a level, matching Index values on thepassed MultiIndex level.fill_value(float or None, default None) — Fill existing missing (NaN) values, and any new element needed forsuccessful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
Result of the arithmetic operation.
DataFrame.add : Add DataFrames.DataFrame.sub : Subtract DataFrames. DataFrame.mul : Multiply DataFrames. DataFrame.div : Divide DataFrames (float division). DataFrame.truediv : Divide DataFrames (float division). DataFrame.floordiv : Divide DataFrames (integer division). DataFrame.mod : Calculate modulo (remainder after division). DataFrame.pow : Calculate exponential power.
Notes
Mismatched indices will be unioned together.
>>> df = pd.DataFrame({'angles': [0, 3, 4],... 'degrees': [360, 180, 360]},
... index=['circle', 'triangle', 'rectangle'])
>>> df
angles degrees
circle 0 360
triangle 3 180
rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
>>> df.add(1)
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10)
angles degrees
circle 0.0 36.0
triangle 0.3 18.0
rectangle 0.4 36.0
>>> df.rdiv(10)
angles degrees
circle inf 0.027778
triangle 3.333333 0.055556
rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2]
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub([1, 2], axis='columns')
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
... axis='index')
angles degrees
circle -1 359
triangle 2 179
rectangle 3 359
Multiply a dictionary by axis.
>>> df.mul({'angles': 0, 'degrees': 2})
angles degrees
circle 0 720
triangle 0 360
rectangle 0 720
>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
angles degrees
circle 0 0
triangle 6 360
rectangle 12 1080
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]},
... index=['circle', 'triangle', 'rectangle'])
>>> other
angles
circle 0
triangle 3
rectangle 4
>>> df * other
angles degrees
circle 0 NaN
triangle 9 NaN
rectangle 16 NaN
>>> df.mul(other, fill_value=0)
angles degrees
circle 0 0.0
triangle 9 0.0
rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
... 'degrees': [360, 180, 360, 360, 540, 720]},
... index=[['A', 'A', 'A', 'B', 'B', 'B'],
... ['circle', 'triangle', 'rectangle',
... 'square', 'pentagon', 'hexagon']])
>>> df_multindex
angles degrees
A circle 0 360
triangle 3 180
rectangle 4 360
B square 4 360
pentagon 5 540
hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0)
angles degrees
A circle NaN 1.0
triangle 1.0 1.0
rectangle 1.0 1.0
B square 0.0 0.0
pentagon 0.0 0.0
hexagon 0.0 0.0
pow(other, axis='columns', level=None, fill_value=None)
Get Exponential power of dataframe and other, element-wise (binary operator pow).
Equivalent to dataframe ** other, but with support to substitute a fill_value
for missing data in one of the inputs. With reverse version, rpow.
Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to
arithmetic operators: +, -, *, /, //, %, **.
other(scalar, sequence, Series, dict or DataFrame) — Any single or multiple element data structure, or list-like object.axis({0 or 'index', 1 or 'columns'}) — Whether to compare by the index (0 or 'index') or columns.(1 or 'columns'). For Series input, axis to match Series index on.level(int or label) — Broadcast across a level, matching Index values on thepassed MultiIndex level.fill_value(float or None, default None) — Fill existing missing (NaN) values, and any new element needed forsuccessful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
Result of the arithmetic operation.
DataFrame.add : Add DataFrames.DataFrame.sub : Subtract DataFrames. DataFrame.mul : Multiply DataFrames. DataFrame.div : Divide DataFrames (float division). DataFrame.truediv : Divide DataFrames (float division). DataFrame.floordiv : Divide DataFrames (integer division). DataFrame.mod : Calculate modulo (remainder after division). DataFrame.pow : Calculate exponential power.
Notes
Mismatched indices will be unioned together.
>>> df = pd.DataFrame({'angles': [0, 3, 4],... 'degrees': [360, 180, 360]},
... index=['circle', 'triangle', 'rectangle'])
>>> df
angles degrees
circle 0 360
triangle 3 180
rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
>>> df.add(1)
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10)
angles degrees
circle 0.0 36.0
triangle 0.3 18.0
rectangle 0.4 36.0
>>> df.rdiv(10)
angles degrees
circle inf 0.027778
triangle 3.333333 0.055556
rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2]
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub([1, 2], axis='columns')
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
... axis='index')
angles degrees
circle -1 359
triangle 2 179
rectangle 3 359
Multiply a dictionary by axis.
>>> df.mul({'angles': 0, 'degrees': 2})
angles degrees
circle 0 720
triangle 0 360
rectangle 0 720
>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
angles degrees
circle 0 0
triangle 6 360
rectangle 12 1080
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]},
... index=['circle', 'triangle', 'rectangle'])
>>> other
angles
circle 0
triangle 3
rectangle 4
>>> df * other
angles degrees
circle 0 NaN
triangle 9 NaN
rectangle 16 NaN
>>> df.mul(other, fill_value=0)
angles degrees
circle 0 0.0
triangle 9 0.0
rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
... 'degrees': [360, 180, 360, 360, 540, 720]},
... index=[['A', 'A', 'A', 'B', 'B', 'B'],
... ['circle', 'triangle', 'rectangle',
... 'square', 'pentagon', 'hexagon']])
>>> df_multindex
angles degrees
A circle 0 360
triangle 3 180
rectangle 4 360
B square 4 360
pentagon 5 540
hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0)
angles degrees
A circle NaN 1.0
triangle 1.0 1.0
rectangle 1.0 1.0
B square 0.0 0.0
pentagon 0.0 0.0
hexagon 0.0 0.0
rpow(other, axis='columns', level=None, fill_value=None)
Get Exponential power of dataframe and other, element-wise (binary operator rpow).
Equivalent to other ** dataframe, but with support to substitute a fill_value
for missing data in one of the inputs. With reverse version, pow.
Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to
arithmetic operators: +, -, *, /, //, %, **.
other(scalar, sequence, Series, dict or DataFrame) — Any single or multiple element data structure, or list-like object.axis({0 or 'index', 1 or 'columns'}) — Whether to compare by the index (0 or 'index') or columns.(1 or 'columns'). For Series input, axis to match Series index on.level(int or label) — Broadcast across a level, matching Index values on thepassed MultiIndex level.fill_value(float or None, default None) — Fill existing missing (NaN) values, and any new element needed forsuccessful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
Result of the arithmetic operation.
DataFrame.add : Add DataFrames.DataFrame.sub : Subtract DataFrames. DataFrame.mul : Multiply DataFrames. DataFrame.div : Divide DataFrames (float division). DataFrame.truediv : Divide DataFrames (float division). DataFrame.floordiv : Divide DataFrames (integer division). DataFrame.mod : Calculate modulo (remainder after division). DataFrame.pow : Calculate exponential power.
Notes
Mismatched indices will be unioned together.
>>> df = pd.DataFrame({'angles': [0, 3, 4],... 'degrees': [360, 180, 360]},
... index=['circle', 'triangle', 'rectangle'])
>>> df
angles degrees
circle 0 360
triangle 3 180
rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
>>> df.add(1)
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10)
angles degrees
circle 0.0 36.0
triangle 0.3 18.0
rectangle 0.4 36.0
>>> df.rdiv(10)
angles degrees
circle inf 0.027778
triangle 3.333333 0.055556
rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2]
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub([1, 2], axis='columns')
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
... axis='index')
angles degrees
circle -1 359
triangle 2 179
rectangle 3 359
Multiply a dictionary by axis.
>>> df.mul({'angles': 0, 'degrees': 2})
angles degrees
circle 0 720
triangle 0 360
rectangle 0 720
>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
angles degrees
circle 0 0
triangle 6 360
rectangle 12 1080
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]},
... index=['circle', 'triangle', 'rectangle'])
>>> other
angles
circle 0
triangle 3
rectangle 4
>>> df * other
angles degrees
circle 0 NaN
triangle 9 NaN
rectangle 16 NaN
>>> df.mul(other, fill_value=0)
angles degrees
circle 0 0.0
triangle 9 0.0
rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
... 'degrees': [360, 180, 360, 360, 540, 720]},
... index=[['A', 'A', 'A', 'B', 'B', 'B'],
... ['circle', 'triangle', 'rectangle',
... 'square', 'pentagon', 'hexagon']])
>>> df_multindex
angles degrees
A circle 0 360
triangle 3 180
rectangle 4 360
B square 4 360
pentagon 5 540
hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0)
angles degrees
A circle NaN 1.0
triangle 1.0 1.0
rectangle 1.0 1.0
B square 0.0 0.0
pentagon 0.0 0.0
hexagon 0.0 0.0
compare(other, align_axis=1, keep_shape=False, keep_equal=False, result_names=('self', 'other'))
Compare to another DataFrame and show the differences.
other(DataFrame) — Object to compare with.align_axis({0 or 'index', 1 or 'columns'}, default 1) — Determine which axis to align the comparison on.- 0, or 'index' : Resulting differences are stacked vertically with rows drawn alternately from self and other.
- 1, or 'columns' : Resulting differences are aligned horizontally with columns drawn alternately from self and other.
keep_shape(bool, default False) — If true, all rows and columns are kept.Otherwise, only the ones with different values are kept.keep_equal(bool, default False) — If true, the result keeps values that are equal.Otherwise, equal values are shown as NaNs.result_names(tuple, default ('self', 'other')) — Set the dataframes names in the comparison.
.. versionadded:: 1.5.0
DataFrame that shows the differences stacked side by side.
The resulting index will be a MultiIndex with 'self' and 'other' stacked alternately at the inner level.
ValueError— When the two DataFrames don't have identical labels or shape.
Series.compare : Compare with another Series and show differences.DataFrame.equals : Test whether two objects contain the same elements.
Notes
Matching NaNs will not appear as a difference.
Can only compare identically-labeled (i.e. same shape, identical row and column labels) DataFrames
>>> df = pd.DataFrame(... {
... "col1": ["a", "a", "b", "b", "a"],
... "col2": [1.0, 2.0, 3.0, np.nan, 5.0],
... "col3": [1.0, 2.0, 3.0, 4.0, 5.0]
... },
... columns=["col1", "col2", "col3"],
... )
>>> df
col1 col2 col3
0 a 1.0 1.0
1 a 2.0 2.0
2 b 3.0 3.0
3 b NaN 4.0
4 a 5.0 5.0
>>> df2 = df.copy()
>>> df2.loc[0, 'col1'] = 'c'
>>> df2.loc[2, 'col3'] = 4.0
>>> df2
col1 col2 col3
0 c 1.0 1.0
1 a 2.0 2.0
2 b 3.0 4.0
3 b NaN 4.0
4 a 5.0 5.0
Align the differences on columns
>>> df.compare(df2)
col1 col3
self other self other
0 a c NaN NaN
2 NaN NaN 3.0 4.0
Assign result_names
>>> df.compare(df2, result_names=("left", "right"))
col1 col3
left right left right
0 a c NaN NaN
2 NaN NaN 3.0 4.0
Stack the differences on rows
>>> df.compare(df2, align_axis=0)
col1 col3
0 self a NaN
other c NaN
2 self NaN 3.0
other NaN 4.0
Keep the equal values
>>> df.compare(df2, keep_equal=True)
col1 col3
self other self other
0 a c 1.0 1.0
2 b b 3.0 4.0
Keep all original rows and columns
>>> df.compare(df2, keep_shape=True)
col1 col2 col3
self other self other self other
0 a c NaN NaN NaN NaN
1 NaN NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN 3.0 4.0
3 NaN NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN NaN
Keep all original rows and columns and also all original values
>>> df.compare(df2, keep_shape=True, keep_equal=True)
col1 col2 col3
self other self other self other
0 a c 1.0 1.0 1.0 1.0
1 a a 2.0 2.0 2.0 2.0
2 b b 3.0 3.0 3.0 4.0
3 b b NaN NaN 4.0 4.0
4 a a 5.0 5.0 5.0 5.0
combine(other, func, fill_value=None, overwrite=True)
Perform column-wise combine with another DataFrame.
Combines a DataFrame with other DataFrame using func
to element-wise combine columns. The row and column indexes of the
resulting DataFrame will be the union of the two.
other(DataFrame) — The DataFrame to merge column-wise.func(function) — Function that takes two series as inputs and return a Series or ascalar. Used to merge the two dataframes column by columns.fill_value(scalar value, default None) — The value to fill NaNs with prior to passing any column to themerge func.overwrite(bool, default True) — If True, columns inselfthat do not exist inotherwill beoverwritten with NaNs.
Combination of the provided DataFrames.
DataFrame.combinefirst : Combine two DataFrame objects and default to non-null values in frame calling the method.
Combine using a simple function that chooses the smaller column.
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2
>>> df1.combine(df2, take_smaller)
A B
0 0 3
1 0 3
Example using a true element-wise combine function.
>>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine(df2, np.minimum)
A B
0 1 2
1 0 3
Using fill_value fills Nones prior to passing the column to the
merge function.
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine(df2, take_smaller, fill_value=-5)
A B
0 0 -5.0
1 0 4.0
However, if the same element in both dataframes is None, that None is preserved
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [None, 3]})
>>> df1.combine(df2, take_smaller, fill_value=-5)
A B
0 0 -5.0
1 0 3.0
Example that demonstrates the use of overwrite and behavior when
the axis differ between the dataframes.
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1], }, index=[1, 2])
>>> df1.combine(df2, take_smaller)
A B C
0 NaN NaN NaN
1 NaN 3.0 -10.0
2 NaN 3.0 1.0
>>> df1.combine(df2, take_smaller, overwrite=False)
A B C
0 0.0 NaN NaN
1 0.0 3.0 -10.0
2 NaN 3.0 1.0
Demonstrating the preference of the passed in dataframe.
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1], }, index=[1, 2])
>>> df2.combine(df1, take_smaller)
A B C
0 0.0 NaN NaN
1 0.0 3.0 NaN
2 NaN 3.0 NaN
>>> df2.combine(df1, take_smaller, overwrite=False)
A B C
0 0.0 NaN NaN
1 0.0 3.0 1.0
2 NaN 3.0 1.0
combine_first(other)
Update null elements with value in the same location in other.
Combine two DataFrame objects by filling null values in one DataFrame with non-null values from other DataFrame. The row and column indexes of the resulting DataFrame will be the union of the two. The resulting dataframe contains the 'first' dataframe values and overrides the second one values where both first.loc[index, col] and second.loc[index, col] are not missing values, upon calling first.combine_first(second).
other(DataFrame) — Provided DataFrame to use to fill null values.
The result of combining the provided DataFrame with the other object.
DataFrame.combine : Perform series-wise operation on two DataFrames using a given function.
>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [None, 4]})>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine_first(df2)
A B
0 1.0 3.0
1 0.0 4.0
Null values still persist if the location of that null value
does not exist in other
>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [4, None]})
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1]}, index=[1, 2])
>>> df1.combine_first(df2)
A B C
0 NaN 4.0 NaN
1 0.0 3.0 1.0
2 NaN 3.0 1.0
update(other, join='left', overwrite=True, filter_func=None, errors='ignore')
Modify in place using non-NA values from another DataFrame.
Aligns on indices. There is no return value.
other(DataFrame, or object coercible into a DataFrame) — Should have at least one matching index/column labelwith the original DataFrame. If a Series is passed, its name attribute must be set, and that will be used as the column name to align with the original DataFrame.join({'left'}, default 'left') — Only left join is implemented, keeping the index and columns of theoriginal object.overwrite(bool, default True) — How to handle non-NA values for overlapping keys:- True: overwrite original DataFrame's values
with values from
other. - False: only update values that are NA in the original DataFrame.
- True: overwrite original DataFrame's values
with values from
filter_func(callable(1d-array) -> bool 1d-array, optional) — Can choose to replace values other than NA. Return True for valuesthat should be updated.errors({'raise', 'ignore'}, default 'ignore') — If 'raise', will raise a ValueError if the DataFrame andotherboth contain non-NA data in the same place.
This method directly changes calling object.
NotImplementedError—- If
join != 'left'
- If
ValueError—- When
errors='raise'and there's overlapping non-NA data. - When
errorsis not either'ignore'or'raise'
- When
dict.update : Similar method for dictionaries.DataFrame.merge : For column(s)-on-column(s) operations.
>>> df = pd.DataFrame({'A': [1, 2, 3],... 'B': [400, 500, 600]})
>>> new_df = pd.DataFrame({'B': [4, 5, 6],
... 'C': [7, 8, 9]})
>>> df.update(new_df)
>>> df
A B
0 1 4
1 2 5
2 3 6
The DataFrame's length does not increase as a result of the update, only values at matching index/column labels are updated.
>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
... 'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e', 'f', 'g', 'h', 'i']})
>>> df.update(new_df)
>>> df
A B
0 a d
1 b e
2 c f
>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
... 'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'f']}, index=[0, 2])
>>> df.update(new_df)
>>> df
A B
0 a d
1 b y
2 c f
For Series, its name attribute must be set.
>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
... 'B': ['x', 'y', 'z']})
>>> new_column = pd.Series(['d', 'e', 'f'], name='B')
>>> df.update(new_column)
>>> df
A B
0 a d
1 b e
2 c f
If other contains NaNs the corresponding values are not updated
in the original dataframe.
>>> df = pd.DataFrame({'A': [1, 2, 3],
... 'B': [400., 500., 600.]})
>>> new_df = pd.DataFrame({'B': [4, np.nan, 6]})
>>> df.update(new_df)
>>> df
A B
0 1 4.0
1 2 500.0
2 3 6.0
groupby(by=None, axis=<no_default>, level=None, as_index=True, sort=True, group_keys=True, observed=<no_default>, dropna=True)
Group DataFrame using a mapper or by a Series of columns.
A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.
by(mapping, function, label, pd.Grouper or list of such) — Used to determine the groups for the groupby.Ifbyis a function, it's called on each value of the object's index. If a dict or Series is passed, the Series or dict VALUES will be used to determine the groups (the Series' values are first aligned; see.align()method). If a list or ndarray of length equal to the selected axis is passed (see thegroupby user guide <https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#splitting-an-object-into-groups>_), the values are used as-is to determine the groups. A label or list of labels may be passed to group by the columns inself. Notice that a tuple is interpreted as a (single) key.axis({0 or 'index', 1 or 'columns'}, default 0) — Split along rows (0) or columns (1). ForSeriesthis parameteris unused and defaults to 0.
.. deprecated:: 2.1.0
Will be removed and behave like axis=0 in a future version. For ``axis=1``, do ``frame.T.groupby(...)`` instead.level(int, level name, or sequence of such, default None) — If the axis is a MultiIndex (hierarchical), group by a particularlevel or levels. Do not specify bothbyandlevel.as_index(bool, default True) — Return object with group labels as theindex. Only relevant for DataFrame input. as_index=False is effectively "SQL-style" grouped output. This argument has no effect on filtrations (see thefiltrations in the user guide <https://pandas.pydata.org/docs/dev/user_guide/groupby.html#filtration>), such ashead(),tail(),nth()and in transformations (see thetransformations in the user guide <https://pandas.pydata.org/docs/dev/user_guide/groupby.html#transformation>).sort(bool, default True) — Sort group keys. Get better performance by turning this off.Note this does not influence the order of observations within each group. Groupby preserves the order of rows within each group. If False, the groups will appear in the same order as they did in the original DataFrame. This argument has no effect on filtrations (see thefiltrations in the user guide <https://pandas.pydata.org/docs/dev/user_guide/groupby.html#filtration>), such ashead(),tail(),nth()and in transformations (see thetransformations in the user guide <https://pandas.pydata.org/docs/dev/user_guide/groupby.html#transformation>).
.. versionchanged:: 2.0.0
Specifying ``sort=False`` with an ordered categorical grouper will no longer sort the values.group_keys(bool, default True) — When calling apply and thebyargument produces a like-indexed(i.e. :ref:a transform <groupby.transform>) result, add group keys to index to identify pieces. By default group keys are not included when the result's index (and column) labels match the inputs, and are included otherwise.
.. versionchanged:: 1.5.0
Warns thatgroup_keyswill no longer be ignored when the result fromapplyis a like-indexed Series or DataFrame. Specifygroup_keysexplicitly to include the group keys or not.
.. versionchanged:: 2.0.0
group_keysnow defaults toTrue.observed(bool, default False) — This only applies if any of the groupers are Categoricals.If True: only show observed values for categorical groupers. If False: show all values for categorical groupers.
.. deprecated:: 2.1.0
The default value will change to True in a future version of pandas.dropna(bool, default True) — If True, and if group keys contain NA values, NA values togetherwith row/column will be dropped. If False, NA values will also be treated as the key in groups.
Returns a groupby object that contains information about the groups.
resample : Convenience method for frequency conversion and resampling of time series.
Notes
See the user guide
<https://pandas.pydata.org/pandas-docs/stable/groupby.html>__ for more
detailed usage and examples, including splitting an object into groups,
iterating through groups, selecting a group, aggregation, and more.
>>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',... 'Parrot', 'Parrot'],
... 'Max Speed': [380., 370., 24., 26.]})
>>> df
Animal Max Speed
0 Falcon 380.0
1 Falcon 370.0
2 Parrot 24.0
3 Parrot 26.0
>>> df.groupby(['Animal']).mean()
Max Speed
Animal
Falcon 375.0
Parrot 25.0
Hierarchical Indexes
We can groupby different levels of a hierarchical index
using the level parameter:
>>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
... ['Captive', 'Wild', 'Captive', 'Wild']]
>>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
>>> df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},
... index=index)
>>> df
Max Speed
Animal Type
Falcon Captive 390.0
Wild 350.0
Parrot Captive 30.0
Wild 20.0
>>> df.groupby(level=0).mean()
Max Speed
Animal
Falcon 370.0
Parrot 25.0
>>> df.groupby(level="Type").mean()
Max Speed
Type
Captive 210.0
Wild 185.0
We can also choose to include NA in group keys or not by setting
dropna parameter, the default setting is True.
>>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
>>> df = pd.DataFrame(l, columns=["a", "b", "c"])
>>> df.groupby(by=["b"]).sum()
a c
b
1.0 2 3
2.0 2 5
>>> df.groupby(by=["b"], dropna=False).sum()
a c
b
1.0 2 3
2.0 2 5
NaN 1 4
>>> l = [["a", 12, 12], [None, 12.3, 33.], ["b", 12.3, 123], ["a", 1, 1]]
>>> df = pd.DataFrame(l, columns=["a", "b", "c"])
>>> df.groupby(by="a").sum()
b c
a
a 13.0 13.0
b 12.3 123.0
>>> df.groupby(by="a", dropna=False).sum()
b c
a
a 13.0 13.0
b 12.3 123.0
NaN 12.3 33.0
When using .apply(), use group_keys to include or exclude the
group keys. The group_keys argument defaults to True (include).
>>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
... 'Parrot', 'Parrot'],
... 'Max Speed': [380., 370., 24., 26.]})
>>> df.groupby("Animal", group_keys=True)[['Max Speed']].apply(lambda x: x)
Max Speed
Animal
Falcon 0 380.0
1 370.0
Parrot 2 24.0
3 26.0
>>> df.groupby("Animal", group_keys=False)[['Max Speed']].apply(lambda x: x)
Max Speed
0 380.0
1 370.0
2 24.0
3 26.0
pivot(columns, index=<no_default>, values=<no_default>)
Return reshaped DataFrame organized by given index / column values.
Reshape data (produce a "pivot" table) based on column values. Uses
unique values from specified index / columns to form axes of the
resulting DataFrame. This function does not support data
aggregation, multiple values will result in a MultiIndex in the
columns. See the :ref:User Guide <reshaping> for more on reshaping.
columns(str or object or a list of str) — Column to use to make new frame's columns.index(str or object or a list of str, optional) — Column to use to make new frame's index. If not given, uses existing index.values(str, object or a list of the previous, optional) — Column(s) to use for populating new frame's values. If notspecified, all remaining columns will be used and the result will have hierarchically indexed columns.
Returns reshaped DataFrame.
ValueError— When there are anyindex,columnscombinations with multiplevalues.DataFrame.pivot_tablewhen you need to aggregate.
DataFrame.pivottable : Generalization of pivot that can handle duplicate values for one index/column pair. DataFrame.unstack : Pivot based on the index values instead of a column. wide_to_long : Wide panel to long format. Less flexible but more user-friendly than melt.
Notes
For finer-tuned control, see hierarchical indexing documentation along with the related stack/unstack methods.
Reference :ref:the user guide <reshaping.pivot> for more examples.
>>> df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two',... 'two'],
... 'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
... 'baz': [1, 2, 3, 4, 5, 6],
... 'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
>>> df
foo bar baz zoo
0 one A 1 x
1 one B 2 y
2 one C 3 z
3 two A 4 q
4 two B 5 w
5 two C 6 t
>>> df.pivot(index='foo', columns='bar', values='baz')
bar A B C
foo
one 1 2 3
two 4 5 6
>>> df.pivot(index='foo', columns='bar')['baz']
bar A B C
foo
one 1 2 3
two 4 5 6
>>> df.pivot(index='foo', columns='bar', values=['baz', 'zoo'])
baz zoo
bar A B C A B C
foo
one 1 2 3 x y z
two 4 5 6 q w t
You could also assign a list of column names or a list of index names.
>>> df = pd.DataFrame({
... "lev1": [1, 1, 1, 2, 2, 2],
... "lev2": [1, 1, 2, 1, 1, 2],
... "lev3": [1, 2, 1, 2, 1, 2],
... "lev4": [1, 2, 3, 4, 5, 6],
... "values": [0, 1, 2, 3, 4, 5]})
>>> df
lev1 lev2 lev3 lev4 values
0 1 1 1 1 0
1 1 1 2 2 1
2 1 2 1 3 2
3 2 1 2 4 3
4 2 1 1 5 4
5 2 2 2 6 5
>>> df.pivot(index="lev1", columns=["lev2", "lev3"], values="values")
lev2 1 2
lev3 1 2 1 2
lev1
1 0.0 1.0 2.0 NaN
2 4.0 3.0 NaN 5.0
>>> df.pivot(index=["lev1", "lev2"], columns=["lev3"], values="values")
lev3 1 2
lev1 lev2
1 1 0.0 1.0
2 2.0 NaN
2 1 4.0 3.0
2 NaN 5.0
A ValueError is raised if there are any duplicates.
>>> df = pd.DataFrame({"foo": ['one', 'one', 'two', 'two'],
... "bar": ['A', 'A', 'B', 'C'],
... "baz": [1, 2, 3, 4]})
>>> df
foo bar baz
0 one A 1
1 one A 2
2 two B 3
3 two C 4
Notice that the first two rows are the same for our index
and columns arguments.
>>> df.pivot(index='foo', columns='bar', values='baz')
Traceback (most recent call last):
...
ValueError: Index contains duplicate entries, cannot reshape
pivot_table(values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=<no_default>, sort=True)
Create a spreadsheet-style pivot table as a DataFrame.
The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame.
values(list-like or scalar, optional) — Column or columns to aggregate.index(column, Grouper, array, or list of the previous) — Keys to group by on the pivot table index. If a list is passed,it can contain any of the other types (except list). If an array is passed, it must be the same length as the data and will be used in the same manner as column values.columns(column, Grouper, array, or list of the previous) — Keys to group by on the pivot table column. If a list is passed,it can contain any of the other types (except list). If an array is passed, it must be the same length as the data and will be used in the same manner as column values.aggfunc(function, list of functions, dict, default "mean") — If a list of functions is passed, the resulting pivot table will havehierarchical columns whose top level are the function names (inferred from the function objects themselves). If a dict is passed, the key is column to aggregate and the value is function or list of functions. Ifmargin=True, aggfunc will be used to calculate the partial aggregates.fill_value(scalar, default None) — Value to replace missing values with (in the resulting pivot table,after aggregation).margins(bool, default False) — Ifmargins=True, specialAllcolumns and rowswill be added with partial group aggregates across the categories on the rows and columns.dropna(bool, default True) — Do not include columns whose entries are all NaN. If True,rows with a NaN value in any column will be omitted before computing margins.margins_name(str, default 'All') — Name of the row / column that will contain the totalswhen margins is True.observed(bool, default False) — This only applies if any of the groupers are Categoricals.If True: only show observed values for categorical groupers. If False: show all values for categorical groupers.
.. deprecated:: 2.2.0
The default value of ``False`` is deprecated and will change to ``True`` in a future version of pandas.sort(bool, default True) — Specifies if the result should be sorted.
.. versionadded:: 1.3.0
An Excel style pivot table.
DataFrame.pivot : Pivot without aggregation that can handle non-numeric data. DataFrame.melt: Unpivot a DataFrame from wide to long format, optionally leaving identifiers set. wide_to_long : Wide panel to long format. Less flexible but more user-friendly than melt.
Notes
Reference :ref:the user guide <reshaping.pivot> for more examples.
>>> df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",... "bar", "bar", "bar", "bar"],
... "B": ["one", "one", "one", "two", "two",
... "one", "one", "two", "two"],
... "C": ["small", "large", "large", "small",
... "small", "large", "small", "small",
... "large"],
... "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
... "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
>>> df
A B C D E
0 foo one small 1 2
1 foo one large 2 4
2 foo one large 2 5
3 foo two small 3 5
4 foo two small 3 6
5 bar one large 4 6
6 bar one small 5 8
7 bar two small 6 9
8 bar two large 7 9
This first example aggregates values by taking the sum.
>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
... columns=['C'], aggfunc="sum")
>>> table
C large small
A B
bar one 4.0 5.0
two 7.0 6.0
foo one 4.0 1.0
two NaN 6.0
We can also fill missing values using the fill_value parameter.
>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
... columns=['C'], aggfunc="sum", fill_value=0)
>>> table
C large small
A B
bar one 4 5
two 7 6
foo one 4 1
two 0 6
The next example aggregates by taking the mean across multiple columns.
>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
... aggfunc={'D': "mean", 'E': "mean"})
>>> table
D E
A C
bar large 5.500000 7.500000
small 5.500000 8.500000
foo large 2.000000 4.500000
small 2.333333 4.333333
We can also calculate multiple types of aggregations for any given value column.
>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
... aggfunc={'D': "mean",
... 'E': ["min", "max", "mean"]})
>>> table
D E
mean max mean min
A C
bar large 5.500000 9 7.500000 6
small 5.500000 9 8.500000 8
foo large 2.000000 5 4.500000 4
small 2.333333 6 4.333333 2
stack(level=-1, dropna=<no_default>, sort=<no_default>, future_stack=False)
Stack the prescribed level(s) from columns to index.
Return a reshaped DataFrame or Series having a multi-level index with one or more new inner-most levels compared to the current DataFrame. The new inner-most levels are created by pivoting the columns of the current dataframe:
- if the columns have a single level, the output is a Series;
- if the columns have multiple levels, the new index level(s) is (are) taken from the prescribed level(s) and the output is a DataFrame.
level(int, str, list, default -1) — Level(s) to stack from the column axis onto the indexaxis, defined as one index or label, or a list of indices or labels.dropna(bool, default True) — Whether to drop rows in the resulting Frame/Series withmissing values. Stacking a column level onto the index axis can create combinations of index and column values that are missing from the original dataframe. See Examples section.sort(bool, default True) — Whether to sort the levels of the resulting MultiIndex.future_stack(bool, default False) — Whether to use the new implementation that will replace the currentimplementation in pandas 3.0. When True, dropna and sort have no impact on the result and must remain unspecified. See :ref:pandas 2.1.0 Release notes <whatsnew_210.enhancements.new_stack>for more details.
Stacked dataframe or series.
DataFrame.unstack : Unstack prescribed level(s) from index axis onto column axis. DataFrame.pivot : Reshape dataframe from long format to wide format. DataFrame.pivottable : Create a spreadsheet-style pivot table as a DataFrame.
Notes
The function is named by analogy with a collection of books being reorganized from being side by side on a horizontal position (the columns of the dataframe) to being stacked vertically on top of each other (in the index of the dataframe).
Reference :ref:the user guide <reshaping.stacking> for more examples.
Single level columns
>>> df_single_level_cols = pd.DataFrame([[0, 1], [2, 3]],
... index=['cat', 'dog'],
... columns=['weight', 'height'])
Stacking a dataframe with a single level column axis returns a Series:
>>> df_single_level_cols
weight height
cat 0 1
dog 2 3
>>> df_single_level_cols.stack(future_stack=True)
cat weight 0
height 1
dog weight 2
height 3
dtype: int64
Multi level columns: simple case
>>> multicol1 = pd.MultiIndex.from_tuples([('weight', 'kg'),
... ('weight', 'pounds')])
>>> df_multi_level_cols1 = pd.DataFrame([[1, 2], [2, 4]],
... index=['cat', 'dog'],
... columns=multicol1)
Stacking a dataframe with a multi-level column axis:
>>> df_multi_level_cols1
weight
kg pounds
cat 1 2
dog 2 4
>>> df_multi_level_cols1.stack(future_stack=True)
weight
cat kg 1
pounds 2
dog kg 2
pounds 4
Missing values
>>> multicol2 = pd.MultiIndex.from_tuples([('weight', 'kg'),
... ('height', 'm')])
>>> df_multi_level_cols2 = pd.DataFrame([[1.0, 2.0], [3.0, 4.0]],
... index=['cat', 'dog'],
... columns=multicol2)
It is common to have missing values when stacking a dataframe with multi-level columns, as the stacked dataframe typically has more values than the original dataframe. Missing values are filled with NaNs:
>>> df_multi_level_cols2
weight height
kg m
cat 1.0 2.0
dog 3.0 4.0
>>> df_multi_level_cols2.stack(future_stack=True)
weight height
cat kg 1.0 NaN
m NaN 2.0
dog kg 3.0 NaN
m NaN 4.0
Prescribing the level(s) to be stacked
The first parameter controls which level or levels are stacked:
>>> df_multi_level_cols2.stack(0, future_stack=True)
kg m
cat weight 1.0 NaN
height NaN 2.0
dog weight 3.0 NaN
height NaN 4.0
>>> df_multi_level_cols2.stack([0, 1], future_stack=True)
cat weight kg 1.0
height m 2.0
dog weight kg 3.0
height m 4.0
dtype: float64
explode(column, ignore_index=False)
Transform each element of a list-like to a row, replicating index values.
column(IndexLabel) — Column(s) to explode.For multiple columns, specify a non-empty list with each element be str or tuple, and all specified columns their list-like data on same row of the frame must have matching length.
.. versionadded:: 1.3.0 Multi-column explodeignore_index(bool, default False) — If True, the resulting index will be labeled 0, 1, …, n - 1.
Exploded lists to rows of the subset columns;index will be duplicated for these rows.
ValueError—- If columns of the frame are not unique.
- If specified columns to explode is empty list.
- If specified columns to explode have not matching count of elements rowwise in the frame.
DataFrame.unstack : Pivot a level of the (necessarily hierarchical) index labels. DataFrame.melt : Unpivot a DataFrame from wide format to long format. Series.explode : Explode a DataFrame from list-like columns to long format.
Notes
This routine will explode list-likes including lists, tuples, sets, Series, and np.ndarray. The result dtype of the subset rows will be object. Scalars will be returned unchanged, and empty list-likes will result in a np.nan for that row. In addition, the ordering of rows in the output will be non-deterministic when exploding sets.
Reference :ref:the user guide <reshaping.explode> for more examples.
>>> df = pd.DataFrame({'A': [[0, 1, 2], 'foo', [], [3, 4]],... 'B': 1,
... 'C': [['a', 'b', 'c'], np.nan, [], ['d', 'e']]})
>>> df
A B C
0 [0, 1, 2] 1 [a, b, c]
1 foo 1 NaN
2 [] 1 []
3 [3, 4] 1 [d, e]
Single-column explode.
>>> df.explode('A')
A B C
0 0 1 [a, b, c]
0 1 1 [a, b, c]
0 2 1 [a, b, c]
1 foo 1 NaN
2 NaN 1 []
3 3 1 [d, e]
3 4 1 [d, e]
Multi-column explode.
>>> df.explode(list('AC'))
A B C
0 0 1 a
0 1 1 b
0 2 1 c
1 foo 1 NaN
2 NaN 1 NaN
3 3 1 d
3 4 1 e
unstack(level=-1, fill_value=None, sort=True)
Pivot a level of the (necessarily hierarchical) index labels.
Returns a DataFrame having a new level of column labels whose inner-most level consists of the pivoted index labels.
If the index is not a MultiIndex, the output will be a Series (the analogue of stack when the columns are not a MultiIndex).
level(int, str, or list of these, default -1 (last level)) — Level(s) of index to unstack, can pass level name.fill_value(int, str or dict) — Replace NaN with this value if the unstack produces missing values.sort(bool, default True) — Sort the level(s) in the resulting MultiIndex columns.
DataFrame.pivot : Pivot a table based on column values.DataFrame.stack : Pivot a level of the column labels (inverse operation
from unstack).
Notes
Reference :ref:the user guide <reshaping.stacking> for more examples.
>>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),... ('two', 'a'), ('two', 'b')])
>>> s = pd.Series(np.arange(1.0, 5.0), index=index)
>>> s
one a 1.0
b 2.0
two a 3.0
b 4.0
dtype: float64
>>> s.unstack(level=-1)
a b
one 1.0 2.0
two 3.0 4.0
>>> s.unstack(level=0)
one two
a 1.0 3.0
b 2.0 4.0
>>> df = s.unstack(level=0)
>>> df.unstack()
one a 1.0
b 2.0
two a 3.0
b 4.0
dtype: float64
melt(id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None, ignore_index=True)
Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
This function is useful to massage a DataFrame into a format where one
or more columns are identifier variables (id_vars), while all other
columns, considered measured variables (value_vars), are "unpivoted" to
the row axis, leaving just two non-identifier columns, 'variable' and
'value'.
id_vars(scalar, tuple, list, or ndarray, optional) — Column(s) to use as identifier variables.value_vars(scalar, tuple, list, or ndarray, optional) — Column(s) to unpivot. If not specified, uses all columns thatare not set asid_vars.var_name(scalar, default None) — Name to use for the 'variable' column. If None it usesframe.columns.nameor 'variable'.value_name(scalar, default 'value') — Name to use for the 'value' column, can't be an existing column label.col_level(scalar, optional) — If columns are a MultiIndex then use this level to melt.ignore_index(bool, default True) — If True, original index is ignored. If False, the original index is retained.Index labels will be repeated as necessary.
Unpivoted DataFrame.
melt : Identical method.pivot_table : Create a spreadsheet-style pivot table as a DataFrame. DataFrame.pivot : Return reshaped DataFrame organized by given index / column values. DataFrame.explode : Explode a DataFrame from list-like columns to long format.
Notes
Reference :ref:the user guide <reshaping.melt> for more examples.
>>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},... 'B': {0: 1, 1: 3, 2: 5},
... 'C': {0: 2, 1: 4, 2: 6}})
>>> df
A B C
0 a 1 2
1 b 3 4
2 c 5 6
>>> df.melt(id_vars=['A'], value_vars=['B'])
A variable value
0 a B 1
1 b B 3
2 c B 5
>>> df.melt(id_vars=['A'], value_vars=['B', 'C'])
A variable value
0 a B 1
1 b B 3
2 c B 5
3 a C 2
4 b C 4
5 c C 6
The names of 'variable' and 'value' columns can be customized:
>>> df.melt(id_vars=['A'], value_vars=['B'],
... var_name='myVarname', value_name='myValname')
A myVarname myValname
0 a B 1
1 b B 3
2 c B 5
Original index values can be kept around:
>>> df.melt(id_vars=['A'], value_vars=['B', 'C'], ignore_index=False)
A variable value
0 a B 1
1 b B 3
2 c B 5
0 a C 2
1 b C 4
2 c C 6
If you have multi-index columns:
>>> df.columns = [list('ABC'), list('DEF')]
>>> df
A B C
D E F
0 a 1 2
1 b 3 4
2 c 5 6
>>> df.melt(col_level=0, id_vars=['A'], value_vars=['B'])
A variable value
0 a B 1
1 b B 3
2 c B 5
>>> df.melt(id_vars=[('A', 'D')], value_vars=[('B', 'E')])
(A, D) variable_0 variable_1 value
0 a B E 1
1 b B E 3
2 c B E 5
diff(periods=1, axis=0)
First discrete difference of element.
Calculates the difference of a DataFrame element compared with another element in the DataFrame (default is element in previous row).
periods(int, default 1) — Periods to shift for calculating difference, accepts negativevalues.axis({0 or 'index', 1 or 'columns'}, default 0) — Take difference over rows (0) or columns (1).
First differences of the Series.
DataFrame.pct_change: Percent change over given number of periods.DataFrame.shift: Shift index by desired number of periods with an optional time freq. Series.diff: First discrete difference of object.
Notes
For boolean dtypes, this uses :meth:operator.xor rather than
:meth:operator.sub.
The result is calculated according to current dtype in DataFrame,
however dtype of the result is always float64.
w
, , ) f c 1 4 9 6 5 6
) c N 0 0 0 0 0
n
) c 0 3 7 3 0 8
w
) c N N N 0 0 0
w
) c 0 0 0 0 0 N
e
) ) a N 0
aggregate(func=None, axis=0, *args, **kwargs)
Aggregate using one or more operations over the specified axis.
func(function, str, list or dict) — Function to use for aggregating the data. If a function, must eitherwork when passed a DataFrame or when passed to DataFrame.apply.
Accepted combinations are:
- function
- string function name
- list of functions and/or function names, e.g.
[np.sum, 'mean'] - dict of axis labels -> functions, function names or list of such.
axis({0 or 'index', 1 or 'columns'}, default 0) — If 0 or 'index': apply function to each column.If 1 or 'columns': apply function to each row.*args— Positional arguments to pass tofunc.**kwargs— Keyword arguments to pass tofunc.
:
n n s
DataFrame.apply : Perform any type of operations.DataFrame.transform : Perform transformation type operations. pandas.DataFrame.groupby : Perform operations over groups. pandas.DataFrame.resample : Perform operations over resampled bins. pandas.DataFrame.rolling : Perform operations over rolling window. pandas.DataFrame.expanding : Perform operations over expanding window. pandas.core.window.ewm.ExponentialMovingWindow : Perform operation over exponential weighted window.
Notes
The aggregation operations are always performed over an axis, either the
index (default) or the column axis. This behavior is different from
numpy aggregation functions (mean, median, prod, sum, std,
var), where the default is to compute the aggregation of the flattened
array, e.g., numpy.mean(arr_2d) as opposed to
numpy.mean(arr_2d, axis=0).
agg is an alias for aggregate. Use the alias.
Functions that mutate the passed object can produce unexpected
behavior or errors and are not supported. See :ref:gotchas.udf-mutation
for more details.
A passed user-defined-function will be passed a Series for evaluation.
>>> df = pd.DataFrame([[1, 2, 3],... [4, 5, 6],
... [7, 8, 9],
... [np.nan, np.nan, np.nan]],
... columns=['A', 'B', 'C'])
Aggregate these functions over the rows.
>>> df.agg(['sum', 'min'])
A B C
sum 12.0 15.0 18.0
min 1.0 2.0 3.0
Different aggregations per column.
>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
A B
sum 12.0 NaN
min 1.0 2.0
max NaN 8.0
Aggregate different functions over the columns and rename the index of the resulting DataFrame.
>>> df.agg(x=('A', 'max'), y=('B', 'min'), z=('C', 'mean'))
A B C
x 7.0 NaN NaN
y NaN 2.0 NaN
z NaN NaN 6.0
Aggregate over the columns.
>>> df.agg("mean", axis="columns")
0 2.0
1 5.0
2 8.0
3 NaN
dtype: float64
transform(func, axis=0, *args, **kwargs)
Call func on self producing a DataFrame with the same axis shape as self.
func(function, str, list-like or dict-like) — Function to use for transforming the data. If a function, must eitherwork when passed a DataFrame or when passed to DataFrame.apply. If func is both list-like and dict-like, dict-like behavior takes precedence.
Accepted combinations are:
- function
- string function name
- list-like of functions and/or function names, e.g.
[np.exp, 'sqrt'] - dict-like of axis labels -> functions, function names or list-like of such.
axis({0 or 'index', 1 or 'columns'}, default 0) — If 0 or 'index': apply function to each column.If 1 or 'columns': apply function to each row.*args— Positional arguments to pass tofunc.**kwargs— Keyword arguments to pass tofunc.
A DataFrame that must have the same length as self.
DataFrame.agg : Only perform aggregating type operations.DataFrame.apply : Invoke function on a DataFrame.
Notes
Functions that mutate the passed object can produce unexpected
behavior or errors and are not supported. See :ref:gotchas.udf-mutation
for more details.
>>> df = pd.DataFrame({'A': range(3), 'B': range(1, 4)})>>> df
A B
0 0 1
1 1 2
2 2 3
>>> df.transform(lambda x: x + 1)
A B
0 1 2
1 2 3
2 3 4
Even though the resulting DataFrame must have the same length as the input DataFrame, it is possible to provide several input functions:
>>> s = pd.Series(range(3))
>>> s
0 0
1 1
2 2
dtype: int64
>>> s.transform([np.sqrt, np.exp])
sqrt exp
0 0.000000 1.000000
1 1.000000 2.718282
2 1.414214 7.389056
You can call transform on a GroupBy object:
>>> df = pd.DataFrame({
... "Date": [
... "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05",
... "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05"],
... "Data": [5, 8, 6, 1, 50, 100, 60, 120],
... })
>>> df
Date Data
0 2015-05-08 5
1 2015-05-07 8
2 2015-05-06 6
3 2015-05-05 1
4 2015-05-08 50
5 2015-05-07 100
6 2015-05-06 60
7 2015-05-05 120
>>> df.groupby('Date')['Data'].transform('sum')
0 55
1 108
2 66
3 121
4 55
5 108
6 66
7 121
Name: Data, dtype: int64
>>> df = pd.DataFrame({
... "c": [1, 1, 1, 2, 2, 2, 2],
... "type": ["m", "n", "o", "m", "m", "n", "n"]
... })
>>> df
c type
0 1 m
1 1 n
2 1 o
3 2 m
4 2 m
5 2 n
6 2 n
>>> df['size'] = df.groupby('c')['type'].transform(len)
>>> df
c type size
0 1 m 3
1 1 n 3
2 1 o 3
3 2 m 4
4 2 m 4
5 2 n 4
6 2 n 4
apply(func, axis=0, raw=False, result_type=None, args=(), by_row='compat', engine='python', engine_kwargs=None, **kwargs)
Apply a function along an axis of the DataFrame.
Objects passed to the function are Series objects whose index is
either the DataFrame's index (axis=0) or the DataFrame's columns
(axis=1). By default (result_type=None), the final return type
is inferred from the return type of the applied function. Otherwise,
it depends on the result_type argument.
func(function) — Function to apply to each column or row.axis({0 or 'index', 1 or 'columns'}, default 0) — Axis along which the function is applied:- 0 or 'index': apply function to each column.
- 1 or 'columns': apply function to each row.
raw(bool, default False) — Determines if row or column is passed as a Series or ndarray object:False: passes each row or column as a Series to the function.True: the passed function will receive ndarray objects instead. If you are just applying a NumPy reduction function this will achieve much better performance.
result_type({'expand', 'reduce', 'broadcast', None}, default None) — These only act whenaxis=1(columns):- 'expand' : list-like results will be turned into columns.
- 'reduce' : returns a Series if possible rather than expanding list-like results. This is the opposite of 'expand'.
- 'broadcast' : results will be broadcast to the original shape of the DataFrame, the original index and columns will be retained.
args(tuple) — Positional arguments to pass tofuncin addition to thearray/series.by_row(False or "compat", default "compat") — Only has an effect whenfuncis a listlike or dictlike of funcsand the func isn't a string. If "compat", will if possible first translate the func into pandas methods (e.g.Series().apply(np.sum)will be translated toSeries().sum()). If that doesn't work, will try call to apply again withby_row=Trueand if that fails, will call apply again withby_row=False(backward compatible). If False, the funcs will be passed the whole Series at once.
.. versionadded:: 2.1.0engine({'python', 'numba'}, default 'python') — Choose between the python (default) engine or the numba engine in apply.
The numba engine will attempt to JIT compile the passed function, which may result in speedups for large DataFrames. It also supports the following engine_kwargs :
- nopython (compile the function in nopython mode)
- nogil (release the GIL inside the JIT compiled function)
- parallel (try to apply the function in parallel over the DataFrame)
Note: The numba compiler only supports a subset of valid Python/numpy operations.
Please read more about thesupported python features <https://numba.pydata.org/numba-doc/dev/reference/pysupported.html>andsupported numpy features <https://numba.pydata.org/numba-doc/dev/reference/numpysupported.html>in numba to learn what you can or cannot use in the passed function.
.. versionadded:: 2.2.0engine_kwargs(dict) — Pass keyword arguments to the engine.This is currently only used by the numba engine, see the documentation for the engine argument for more information.**kwargs— Additional keyword arguments to pass as keywords arguments tofunc.
Result of applying func along the given axis of theDataFrame.
DataFrame.map: For elementwise operations.DataFrame.aggregate: Only perform aggregating type operations. DataFrame.transform: Only perform transforming type operations.
Notes
Functions that mutate the passed object can produce unexpected
behavior or errors and are not supported. See :ref:gotchas.udf-mutation
for more details.
>>> df = pd.DataFrame([[4, 9]] * 3, columns=['A', 'B'])>>> df
A B
0 4 9
1 4 9
2 4 9
Using a numpy universal function (in this case the same as
np.sqrt(df)):
>>> df.apply(np.sqrt)
A B
0 2.0 3.0
1 2.0 3.0
2 2.0 3.0
Using a reducing function on either axis
>>> df.apply(np.sum, axis=0)
A 12
B 27
dtype: int64
>>> df.apply(np.sum, axis=1)
0 13
1 13
2 13
dtype: int64
Returning a list-like will result in a Series
>>> df.apply(lambda x: [1, 2], axis=1)
0 [1, 2]
1 [1, 2]
2 [1, 2]
dtype: object
Passing result_type='expand' will expand list-like results
to columns of a Dataframe
>>> df.apply(lambda x: [1, 2], axis=1, result_type='expand')
0 1
0 1 2
1 1 2
2 1 2
Returning a Series inside the function is similar to passing
result_type='expand'. The resulting column names
will be the Series index.
>>> df.apply(lambda x: pd.Series([1, 2], index=['foo', 'bar']), axis=1)
foo bar
0 1 2
1 1 2
2 1 2
Passing result_type='broadcast' will ensure the same shape
result, whether list-like or scalar is returned by the function,
and broadcast it along the axis. The resulting column names will
be the originals.
>>> df.apply(lambda x: [1, 2], axis=1, result_type='broadcast')
A B
0 1 2
1 1 2
2 1 2
map(func, na_action=None, **kwargs)
Apply a function to a Dataframe elementwise.
.. versionadded:: 2.1.0
DataFrame.applymap was deprecated and renamed to DataFrame.map.
This method applies a function that accepts and returns a scalar to every element of a DataFrame.
func(callable) — Python function, returns a single value from a single value.na_action({None, 'ignore'}, default None) — If 'ignore', propagate NaN values, without passing them to func.**kwargs— Additional keyword arguments to pass as keywords arguments tofunc.
Transformed DataFrame.
DataFrame.apply : Apply a function along input axis of DataFrame.DataFrame.replace: Replace values given in to_replace with value.
Series.map : Apply a function elementwise on a Series.
>>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])>>> df
0 1
0 1.000 2.120
1 3.356 4.567
>>> df.map(lambda x: len(str(x)))
0 1
0 3 4
1 5 5
Like Series.map, NA values can be ignored:
>>> df_copy = df.copy()
>>> df_copy.iloc[0, 0] = pd.NA
>>> df_copy.map(lambda x: len(str(x)), na_action='ignore')
0 1
0 NaN 4
1 5.0 5
It is also possible to use map with functions that are not
lambda functions:
>>> df.map(round, ndigits=1)
0 1
0 1.0 2.1
1 3.4 4.6
Note that a vectorized version of func often exists, which will
be much faster. You could square each number elementwise.
>>> df.map(lambda x: x**2)
0 1
0 1.000000 4.494400
1 11.262736 20.857489
But it's better to avoid map in that case.
>>> df ** 2
0 1
0 1.000000 4.494400
1 11.262736 20.857489
applymap(func, na_action=None, **kwargs)
Apply a function to a Dataframe elementwise.
.. deprecated:: 2.1.0
DataFrame.applymap has been deprecated. Use DataFrame.map instead.
This method applies a function that accepts and returns a scalar to every element of a DataFrame.
func(callable) — Python function, returns a single value from a single value.na_action({None, 'ignore'}, default None) — If 'ignore', propagate NaN values, without passing them to func.**kwargs— Additional keyword arguments to pass as keywords arguments tofunc.
Transformed DataFrame.
DataFrame.apply : Apply a function along input axis of DataFrame.DataFrame.map : Apply a function along input axis of DataFrame.
DataFrame.replace: Replace values given in to_replace with value.
>>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])>>> df
0 1
0 1.000 2.120
1 3.356 4.567
>>> df.map(lambda x: len(str(x)))
0 1
0 3 4
1 5 5
join(other, on=None, how='left', lsuffix='', rsuffix='', sort=False, validate=None)
Join columns of another DataFrame.
Join columns with other DataFrame either on index or on a key
column. Efficiently join multiple DataFrame objects by index at once by
passing a list.
other(DataFrame, Series, or a list containing any combination of them) — Index should be similar to one of the columns in this one. If aSeries is passed, its name attribute must be set, and that will be used as the column name in the resulting joined DataFrame.on(str, list of str, or array-like, optional) — Column or index level name(s) in the caller to join on the indexinother, otherwise joins index-on-index. If multiple values given, theotherDataFrame must have a MultiIndex. Can pass an array as the join key if it is not already contained in the calling DataFrame. Like an Excel VLOOKUP operation.how({'left', 'right', 'outer', 'inner', 'cross'}, default 'left') — How to handle the operation of the two objects.- left: use calling frame's index (or column if on is specified)
- right: use
other's index. - outer: form union of calling frame's index (or column if on is
specified) with
other's index, and sort it lexicographically. - inner: form intersection of calling frame's index (or column if
on is specified) with
other's index, preserving the order of the calling's one. - cross: creates the cartesian product from both frames, preserves the order of the left keys.
lsuffix(str, default '') — Suffix to use from left frame's overlapping columns.rsuffix(str, default '') — Suffix to use from right frame's overlapping columns.sort(bool, default False) — Order result DataFrame lexicographically by the join key. If False,the order of the join key depends on the join type (how keyword).validate(str, optional) — If specified, checks if join is of specified type.- "one_to_one" or "1:1": check if join keys are unique in both left and right datasets.
- "one_to_many" or "1:m": check if join keys are unique in left dataset.
- "many_to_one" or "m:1": check if join keys are unique in right dataset.
- "many_to_many" or "m:m": allowed, but does not result in checks.
A dataframe containing columns from both the caller and other.
DataFrame.merge : For column(s)-on-column(s) operations.
Notes
Parameters on, lsuffix, and rsuffix are not supported when
passing a list of DataFrame objects.
>>> df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],... 'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})
>>> df
key A
0 K0 A0
1 K1 A1
2 K2 A2
3 K3 A3
4 K4 A4
5 K5 A5
>>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
... 'B': ['B0', 'B1', 'B2']})
>>> other
key B
0 K0 B0
1 K1 B1
2 K2 B2
Join DataFrames using their indexes.
>>> df.join(other, lsuffix='_caller', rsuffix='_other')
key_caller A key_other B
0 K0 A0 K0 B0
1 K1 A1 K1 B1
2 K2 A2 K2 B2
3 K3 A3 NaN NaN
4 K4 A4 NaN NaN
5 K5 A5 NaN NaN
If we want to join using the key columns, we need to set key to be
the index in both df and other. The joined DataFrame will have
key as its index.
>>> df.set_index('key').join(other.set_index('key'))
A B
key
K0 A0 B0
K1 A1 B1
K2 A2 B2
K3 A3 NaN
K4 A4 NaN
K5 A5 NaN
Another option to join using the key columns is to use the on
parameter. DataFrame.join always uses other's index but we can use
any column in df. This method preserves the original DataFrame's
index in the result.
>>> df.join(other.set_index('key'), on='key')
key A B
0 K0 A0 B0
1 K1 A1 B1
2 K2 A2 B2
3 K3 A3 NaN
4 K4 A4 NaN
5 K5 A5 NaN
Using non-unique key values shows how they are matched.
>>> df = pd.DataFrame({'key': ['K0', 'K1', 'K1', 'K3', 'K0', 'K1'],
... 'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})
>>> df
key A
0 K0 A0
1 K1 A1
2 K1 A2
3 K3 A3
4 K0 A4
5 K1 A5
>>> df.join(other.set_index('key'), on='key', validate='m:1')
key A B
0 K0 A0 B0
1 K1 A1 B1
2 K1 A2 B1
3 K3 A3 NaN
4 K0 A4 B0
5 K1 A5 B1
merge(right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=None, indicator=False, validate=None)
Merge DataFrame or named Series objects with a database-style join.
A named Series object is treated as a DataFrame with a single named column.
The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on. When performing a cross merge, no column specifications to merge on are allowed.
.. warning::
If both key columns contain rows where the key is a null value, those
rows will be matched against each other. This is different from usual SQL
join behaviour and can lead to unexpected results.
right(DataFrame or named Series) — Object to merge with.how({'left', 'right', 'outer', 'inner', 'cross'}, default 'inner') — Type of merge to be performed.- left: use only keys from left frame, similar to a SQL left outer join; preserve key order.
- right: use only keys from right frame, similar to a SQL right outer join; preserve key order.
- outer: use union of keys from both frames, similar to a SQL full outer join; sort keys lexicographically.
- inner: use intersection of keys from both frames, similar to a SQL inner join; preserve the order of the left keys.
- cross: creates the cartesian product from both frames, preserves the order of the left keys.
on(label or list) — Column or index level names to join on. These must be found in bothDataFrames. Ifonis None and not merging on indexes then this defaults to the intersection of the columns in both DataFrames.left_on(label or list, or array-like) — Column or index level names to join on in the left DataFrame. Can alsobe an array or list of arrays of the length of the left DataFrame. These arrays are treated as if they are columns.right_on(label or list, or array-like) — Column or index level names to join on in the right DataFrame. Can alsobe an array or list of arrays of the length of the right DataFrame. These arrays are treated as if they are columns.left_index(bool, default False) — Use the index from the left DataFrame as the join key(s). If it is aMultiIndex, the number of keys in the other DataFrame (either the index or a number of columns) must match the number of levels.right_index(bool, default False) — Use the index from the right DataFrame as the join key. Same caveats asleft_index.sort(bool, default False) — Sort the join keys lexicographically in the result DataFrame. If False,the order of the join keys depends on the join type (how keyword).suffixes(list-like, default is ("_x", "_y")) — A length-2 sequence where each element is optionally a stringindicating the suffix to add to overlapping column names inleftandrightrespectively. Pass a value ofNoneinstead of a string to indicate that the column name fromleftorrightshould be left as-is, with no suffix. At least one of the values must not be None.copy(bool, default True) — If False, avoid copy if possible.
.. note:: Thecopykeyword will change behavior in pandas 3.0.Copy-on-Write <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>__ will be enabled by default, which means that all methods with acopykeyword will use a lazy copy mechanism to defer the copy and ignore thecopykeyword. Thecopykeyword will be removed in a future version of pandas.
You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True``indicator(bool or str, default False) — If True, adds a column to the output DataFrame called "_merge" withinformation on the source of each row. The column can be given a different name by providing a string argument. The column will have a Categorical type with the value of "left_only" for observations whose merge key only appears in the left DataFrame, "right_only" for observations whose merge key only appears in the right DataFrame, and "both" if the observation's merge key is found in both DataFrames.validate(str, optional) — If specified, checks if merge is of specified type.- "one_to_one" or "1:1": check if merge keys are unique in both left and right datasets.
- "one_to_many" or "1:m": check if merge keys are unique in left dataset.
- "many_to_one" or "m:1": check if merge keys are unique in right dataset.
- "many_to_many" or "m:m": allowed, but does not result in checks.
A DataFrame of the two merged objects.
merge_ordered : Merge with optional filling/interpolation.merge_asof : Merge on nearest keys. DataFrame.join : Similar method using indices.
>>> df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],... 'value': [1, 2, 3, 5]})
>>> df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
... 'value': [5, 6, 7, 8]})
>>> df1
lkey value
0 foo 1
1 bar 2
2 baz 3
3 foo 5
>>> df2
rkey value
0 foo 5
1 bar 6
2 baz 7
3 foo 8
Merge df1 and df2 on the lkey and rkey columns. The value columns have the default suffixes, _x and _y, appended.
>>> df1.merge(df2, left_on='lkey', right_on='rkey')
lkey value_x rkey value_y
0 foo 1 foo 5
1 foo 1 foo 8
2 bar 2 bar 6
3 baz 3 baz 7
4 foo 5 foo 5
5 foo 5 foo 8
Merge DataFrames df1 and df2 with specified left and right suffixes appended to any overlapping columns.
>>> df1.merge(df2, left_on='lkey', right_on='rkey',
... suffixes=('_left', '_right'))
lkey value_left rkey value_right
0 foo 1 foo 5
1 foo 1 foo 8
2 bar 2 bar 6
3 baz 3 baz 7
4 foo 5 foo 5
5 foo 5 foo 8
Merge DataFrames df1 and df2, but raise an exception if the DataFrames have any overlapping columns.
>>> df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))
Traceback (most recent call last):
...
ValueError: columns overlap but no suffix specified:
Index(['value'], dtype='object')
>>> df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})
>>> df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]})
>>> df1
a b
0 foo 1
1 bar 2
>>> df2
a c
0 foo 3
1 baz 4
>>> df1.merge(df2, how='inner', on='a')
a b c
0 foo 1 3
>>> df1.merge(df2, how='left', on='a')
a b c
0 foo 1 3.0
1 bar 2 NaN
>>> df1 = pd.DataFrame({'left': ['foo', 'bar']})
>>> df2 = pd.DataFrame({'right': [7, 8]})
>>> df1
left
0 foo
1 bar
>>> df2
right
0 7
1 8
>>> df1.merge(df2, how='cross')
left right
0 foo 7
1 foo 8
2 bar 7
3 bar 8
round(decimals=0, *args, **kwargs)
Round a DataFrame to a variable number of decimal places.
decimals(int, dict, Series) — Number of decimal places to round each column to. If an int isgiven, round each column to the same number of places. Otherwise dict and Series round to variable numbers of places. Column names should be in the keys ifdecimalsis a dict-like, or in the index ifdecimalsis a Series. Any columns not included indecimalswill be left as is. Elements ofdecimalswhich are not columns of the input will be ignored.*args— Additional keywords have no effect but might be accepted forcompatibility with numpy.**kwargs— Additional keywords have no effect but might be accepted forcompatibility with numpy.
A DataFrame with the affected columns rounded to the specifiednumber of decimal places.
numpy.around : Round a numpy array to the given number of decimals.Series.round : Round a Series to the given number of decimals.
>>> df = pd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)],... columns=['dogs', 'cats'])
>>> df
dogs cats
0 0.21 0.32
1 0.01 0.67
2 0.66 0.03
3 0.21 0.18
By providing an integer each column is rounded to the same number of decimal places
>>> df.round(1)
dogs cats
0 0.2 0.3
1 0.0 0.7
2 0.7 0.0
3 0.2 0.2
With a dict, the number of places for specific columns can be specified with the column names as key and the number of decimal places as value
>>> df.round({'dogs': 1, 'cats': 0})
dogs cats
0 0.2 0.0
1 0.0 1.0
2 0.7 0.0
3 0.2 0.0
Using a Series, the number of places for specific columns can be specified with the column names as index and the number of decimal places as value
>>> decimals = pd.Series([0, 1], index=['cats', 'dogs'])
>>> df.round(decimals)
dogs cats
0 0.2 0.0
1 0.0 1.0
2 0.7 0.0
3 0.2 0.0
corr(method='pearson', min_periods=1, numeric_only=False)
Compute pairwise correlation of columns, excluding NA/null values.
method({'pearson', 'kendall', 'spearman'} or callable) — Method of correlation:- pearson : standard correlation coefficient
- kendall : Kendall Tau correlation coefficient
- spearman : Spearman rank correlation
- callable: callable with input two 1d ndarrays and returning a float. Note that the returned matrix from corr will have 1 along the diagonals and will be symmetric regardless of the callable's behavior.
min_periods(int, optional) — Minimum number of observations required per pair of columnsto have a valid result. Currently only available for Pearson and Spearman correlation.numeric_only(bool, default False) — Include onlyfloat,intorbooleandata.
.. versionadded:: 1.5.0
.. versionchanged:: 2.0.0 The default value ofnumeric_onlyis nowFalse.
Correlation matrix.
DataFrame.corrwith : Compute pairwise correlation with another DataFrame or Series. Series.corr : Compute the correlation between two Series.
Notes
Pearson, Kendall and Spearman correlation are currently computed using pairwise complete observations.
Pearson correlation coefficient <https://en.wikipedia.org/wiki/Pearson_correlation_coefficient>_Kendall rank correlation coefficient <https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient>_Spearman's rank correlation coefficient <https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient>_
>>> def histogram_intersection(a, b):... v = np.minimum(a, b).sum().round(decimals=1)
... return v
>>> df = pd.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
... columns=['dogs', 'cats'])
>>> df.corr(method=histogram_intersection)
dogs cats
dogs 1.0 0.3
cats 0.3 1.0
>>> df = pd.DataFrame([(1, 1), (2, np.nan), (np.nan, 3), (4, 4)],
... columns=['dogs', 'cats'])
>>> df.corr(min_periods=3)
dogs cats
dogs 1.0 NaN
cats NaN 1.0
cov(min_periods=None, ddof=1, numeric_only=False)
Compute pairwise covariance of columns, excluding NA/null values.
Compute the pairwise covariance among the series of a DataFrame.
The returned data frame is the covariance matrix
<https://en.wikipedia.org/wiki/Covariance_matrix>__ of the columns
of the DataFrame.
Both NA and null values are automatically excluded from the
calculation. (See the note below about bias from missing values.)
A threshold can be set for the minimum number of
observations for each value created. Comparisons with observations
below this threshold will be returned as NaN.
This method is generally used for the analysis of time series data to understand the relationship between different measures across time.
min_periods(int, optional) — Minimum number of observations required per pair of columnsto have a valid result.ddof(int, default 1) — Delta degrees of freedom. The divisor used in calculationsisN - ddof, whereNrepresents the number of elements. This argument is applicable only when nonanis in the dataframe.numeric_only(bool, default False) — Include onlyfloat,intorbooleandata.
.. versionadded:: 1.5.0
.. versionchanged:: 2.0.0 The default value ofnumeric_onlyis nowFalse.
The covariance matrix of the series of the DataFrame.
Series.cov : Compute covariance with another Series.core.window.ewm.ExponentialMovingWindow.cov : Exponential weighted sample covariance. core.window.expanding.Expanding.cov : Expanding sample covariance. core.window.rolling.Rolling.cov : Rolling sample covariance.
Notes
Returns the covariance matrix of the DataFrame's time series. The covariance is normalized by N-ddof.
For DataFrames that have Series that are missing data (assuming that
data is missing at random
<https://en.wikipedia.org/wiki/Missing_data#Missing_at_random>__)
the returned covariance matrix will be an unbiased estimate
of the variance and covariance between the member Series.
However, for many applications this estimate may not be acceptable
because the estimate covariance matrix is not guaranteed to be positive
semi-definite. This could lead to estimate correlations having
absolute values which are greater than one, and/or a non-invertible
covariance matrix. See Estimation of covariance matrices
<https://en.wikipedia.org/w/index.php?title=Estimation_of_covariance_
matrices>__ for more details.
>>> df = pd.DataFrame([(1, 2), (0, 3), (2, 0), (1, 1)],... columns=['dogs', 'cats'])
>>> df.cov()
dogs cats
dogs 0.666667 -1.000000
cats -1.000000 1.666667
>>> np.random.seed(42)
>>> df = pd.DataFrame(np.random.randn(1000, 5),
... columns=['a', 'b', 'c', 'd', 'e'])
>>> df.cov()
a b c d e
a 0.998438 -0.020161 0.059277 -0.008943 0.014144
b -0.020161 1.059352 -0.008543 -0.024738 0.009826
c 0.059277 -0.008543 1.010670 -0.001486 -0.000271
d -0.008943 -0.024738 -0.001486 0.921297 -0.013692
e 0.014144 0.009826 -0.000271 -0.013692 0.977795
Minimum number of periods
This method also supports an optional min_periods keyword
that specifies the required minimum number of non-NA observations for
each column pair in order to have a valid result:
>>> np.random.seed(42)
>>> df = pd.DataFrame(np.random.randn(20, 3),
... columns=['a', 'b', 'c'])
>>> df.loc[df.index[:5], 'a'] = np.nan
>>> df.loc[df.index[5:10], 'b'] = np.nan
>>> df.cov(min_periods=12)
a b c
a 0.316741 NaN -0.150812
b NaN 1.248003 0.191417
c -0.150812 0.191417 0.895202
corrwith(other, axis=0, drop=False, method='pearson', numeric_only=False)
Compute pairwise correlation.
Pairwise correlation is computed between rows or columns of DataFrame with rows or columns of Series or DataFrame. DataFrames are first aligned along both axes before computing the correlations.
other(DataFrame, Series) — Object with which to compute correlations.axis({0 or 'index', 1 or 'columns'}, default 0) — The axis to use. 0 or 'index' to compute row-wise, 1 or 'columns' forcolumn-wise.drop(bool, default False) — Drop missing indices from result.method({'pearson', 'kendall', 'spearman'} or callable) — Method of correlation:- pearson : standard correlation coefficient
- kendall : Kendall Tau correlation coefficient
- spearman : Spearman rank correlation
- callable: callable with input two 1d ndarrays and returning a float.
numeric_only(bool, default False) — Include onlyfloat,intorbooleandata.
.. versionadded:: 1.5.0
.. versionchanged:: 2.0.0 The default value ofnumeric_onlyis nowFalse.
Pairwise correlations.
DataFrame.corr : Compute pairwise correlation of columns.
>>> index = ["a", "b", "c", "d", "e"]>>> columns = ["one", "two", "three", "four"]
>>> df1 = pd.DataFrame(np.arange(20).reshape(5, 4), index=index, columns=columns)
>>> df2 = pd.DataFrame(np.arange(16).reshape(4, 4), index=index[:4], columns=columns)
>>> df1.corrwith(df2)
one 1.0
two 1.0
three 1.0
four 1.0
dtype: float64
>>> df2.corrwith(df1, axis=1)
a 1.0
b 1.0
c 1.0
d 1.0
e NaN
dtype: float64
count(axis=0, numeric_only=False)
Count non-NA cells for each column or row.
The values None, NaN, NaT, pandas.NA are considered NA.
axis({0 or 'index', 1 or 'columns'}, default 0) — If 0 or 'index' counts are generated for each column.If 1 or 'columns' counts are generated for each row.numeric_only(bool, default False) — Include onlyfloat,intorbooleandata.
For each column/row the number of non-NA/null entries.
Series.count: Number of non-NA elements in a Series.DataFrame.value_counts: Count unique combinations of columns. DataFrame.shape: Number of DataFrame rows and columns (including NA elements). DataFrame.isna: Boolean same-sized DataFrame showing places of NA elements.
Constructing DataFrame from a dictionary:
>>> df = pd.DataFrame({"Person":
... ["John", "Myla", "Lewis", "John", "Myla"],
... "Age": [24., np.nan, 21., 33, 26],
... "Single": [False, True, True, True, False]})
>>> df
Person Age Single
0 John 24.0 False
1 Myla NaN True
2 Lewis 21.0 True
3 John 33.0 True
4 Myla 26.0 False
Notice the uncounted NA values:
>>> df.count()
Person 5
Age 4
Single 5
dtype: int64
Counts for each row:
>>> df.count(axis='columns')
0 3
1 2
2 3
3 3
4 3
dtype: int64
any(axis=0, bool_only=False, skipna=True, **kwargs)
Return whether any element is True, potentially over an axis.
Returns False unless there is at least one element within a series or along a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty).
axis({0 or 'index', 1 or 'columns', None}, default 0) — Indicate which axis or axes should be reduced. ForSeriesthis parameteris unused and defaults to 0.
- 0 / 'index' : reduce the index, return a Series whose index is the original column labels.
- 1 / 'columns' : reduce the columns, return a Series whose index is the original index.
- None : reduce all axes, return a scalar.
bool_only(bool, default False) — Include only boolean columns. Not implemented for Series.skipna(bool, default True) — Exclude NA/null values. If the entire row/column is NA and skipna isTrue, then the result will be False, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.**kwargs(any, default None) — Additional keywords have no effect but might be accepted forcompatibility with NumPy.
If level is specified, then, DataFrame is returned; otherwise, Seriesis returned.
numpy.any : Numpy version of this method.Series.any : Return whether any element is True. Series.all : Return whether all elements are True. DataFrame.any : Return whether any element is True over requested axis. DataFrame.all : Return whether all elements are True over requested axis.
Series
For Series input, the output is a scalar indicating whether any element is True.
>>> pd.Series([False, False]).any()
False
>>> pd.Series([True, False]).any()
True
>>> pd.Series([], dtype="float64").any()
False
>>> pd.Series([np.nan]).any()
False
>>> pd.Series([np.nan]).any(skipna=False)
True
DataFrame
Whether each column contains at least one True element (the default).
>>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]})
>>> df
A B C
0 1 0 0
1 2 2 0
>>> df.any()
A True
B True
C False
dtype: bool
Aggregating over the columns.
>>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]})
>>> df
A B
0 True 1
1 False 2
>>> df.any(axis='columns')
0 True
1 True
dtype: bool
>>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]})
>>> df
A B
0 True 1
1 False 0
>>> df.any(axis='columns')
0 True
1 False
dtype: bool
Aggregating over the entire DataFrame with axis=None.
>>> df.any(axis=None)
True
any for an empty DataFrame is an empty Series.
>>> pd.DataFrame([]).any()
Series([], dtype: bool)
all(axis=0, bool_only=False, skipna=True, **kwargs)
Return whether all elements are True, potentially over an axis.
Returns True unless there at least one element within a series or along a Dataframe axis that is False or equivalent (e.g. zero or empty).
axis({0 or 'index', 1 or 'columns', None}, default 0) — Indicate which axis or axes should be reduced. ForSeriesthis parameteris unused and defaults to 0.
- 0 / 'index' : reduce the index, return a Series whose index is the original column labels.
- 1 / 'columns' : reduce the columns, return a Series whose index is the original index.
- None : reduce all axes, return a scalar.
bool_only(bool, default False) — Include only boolean columns. Not implemented for Series.skipna(bool, default True) — Exclude NA/null values. If the entire row/column is NA and skipna isTrue, then the result will be True, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.**kwargs(any, default None) — Additional keywords have no effect but might be accepted forcompatibility with NumPy.
If level is specified, then, DataFrame is returned; otherwise, Seriesis returned.
Series.all : Return True if all elements are True.DataFrame.any : Return True if one (or more) elements are True.
Series
>>> pd.Series([True, True]).all()
True
>>> pd.Series([True, False]).all()
False
>>> pd.Series([], dtype="float64").all()
True
>>> pd.Series([np.nan]).all()
True
>>> pd.Series([np.nan]).all(skipna=False)
True
DataFrames
Create a dataframe from a dictionary.
>>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]})
>>> df
col1 col2
0 True True
1 True False
Default behaviour checks if values in each column all return True.
>>> df.all()
col1 True
col2 False
dtype: bool
Specify axis='columns' to check if values in each row all return True.
>>> df.all(axis='columns')
0 True
1 False
dtype: bool
Or axis=None for whether every value is True.
>>> df.all(axis=None)
False
min(axis=0, skipna=True, numeric_only=False, **kwargs)
Return the minimum of the values over the requested axis.
If you want the index of the minimum, use idxmin. This is the equivalent of the numpy.ndarray method argmin.
axis({index (0), columns (1)}) — Axis for the function to be applied on.ForSeriesthis parameter is unused and defaults to 0.
For DataFrames, specifyingaxis=Nonewill apply the aggregation across both axes.
.. versionadded:: 2.0.0skipna(bool, default True) — Exclude NA/null values when computing the result.numeric_only(bool, default False) — Include only float, int, boolean columns. Not implemented for Series.**kwargs— Additional keyword arguments to be passed to the function.
Series.sum : Return the sum.Series.min : Return the minimum. Series.max : Return the maximum. Series.idxmin : Return the index of the minimum. Series.idxmax : Return the index of the maximum. DataFrame.sum : Return the sum over the requested axis. DataFrame.min : Return the minimum over the requested axis. DataFrame.max : Return the maximum over the requested axis. DataFrame.idxmin : Return the index of the minimum over the requested axis. DataFrame.idxmax : Return the index of the maximum over the requested axis.
>>> idx = pd.MultiIndex.from_arrays([... ['warm', 'warm', 'cold', 'cold'],
... ['dog', 'falcon', 'fish', 'spider']],
... names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded animal
warm dog 4
falcon 2
cold fish 0
spider 8
Name: legs, dtype: int64
>>> s.min()
0
max(axis=0, skipna=True, numeric_only=False, **kwargs)
Return the maximum of the values over the requested axis.
If you want the index of the maximum, use idxmax. This is the equivalent of the numpy.ndarray method argmax.
axis({index (0), columns (1)}) — Axis for the function to be applied on.ForSeriesthis parameter is unused and defaults to 0.
For DataFrames, specifyingaxis=Nonewill apply the aggregation across both axes.
.. versionadded:: 2.0.0skipna(bool, default True) — Exclude NA/null values when computing the result.numeric_only(bool, default False) — Include only float, int, boolean columns. Not implemented for Series.**kwargs— Additional keyword arguments to be passed to the function.
Series.sum : Return the sum.Series.min : Return the minimum. Series.max : Return the maximum. Series.idxmin : Return the index of the minimum. Series.idxmax : Return the index of the maximum. DataFrame.sum : Return the sum over the requested axis. DataFrame.min : Return the minimum over the requested axis. DataFrame.max : Return the maximum over the requested axis. DataFrame.idxmin : Return the index of the minimum over the requested axis. DataFrame.idxmax : Return the index of the maximum over the requested axis.
>>> idx = pd.MultiIndex.from_arrays([... ['warm', 'warm', 'cold', 'cold'],
... ['dog', 'falcon', 'fish', 'spider']],
... names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded animal
warm dog 4
falcon 2
cold fish 0
spider 8
Name: legs, dtype: int64
>>> s.max()
8
sum(axis=0, skipna=True, numeric_only=False, min_count=0, **kwargs)
Return the sum of the values over the requested axis.
This is equivalent to the method numpy.sum.
axis({index (0), columns (1)}) — Axis for the function to be applied on.ForSeriesthis parameter is unused and defaults to 0.
.. warning::
.. versionadded:: 2.0.0The behavior of DataFrame.sum with ``axis=None`` is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).skipna(bool, default True) — Exclude NA/null values when computing the result.numeric_only(bool, default False) — Include only float, int, boolean columns. Not implemented for Series.min_count(int, default 0) — The required number of valid values to perform the operation. If fewer thanmin_countnon-NA values are present the result will be NA.**kwargs— Additional keyword arguments to be passed to the function.
Series.sum : Return the sum.Series.min : Return the minimum. Series.max : Return the maximum. Series.idxmin : Return the index of the minimum. Series.idxmax : Return the index of the maximum. DataFrame.sum : Return the sum over the requested axis. DataFrame.min : Return the minimum over the requested axis. DataFrame.max : Return the maximum over the requested axis. DataFrame.idxmin : Return the index of the minimum over the requested axis. DataFrame.idxmax : Return the index of the maximum over the requested axis.
>>> idx = pd.MultiIndex.from_arrays([... ['warm', 'warm', 'cold', 'cold'],
... ['dog', 'falcon', 'fish', 'spider']],
... names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded animal
warm dog 4
falcon 2
cold fish 0
spider 8
Name: legs, dtype: int64
>>> s.sum()
14
By default, the sum of an empty or all-NA Series is 0.
>>> pd.Series([], dtype="float64").sum() # min_count=0 is the default
0.0
This can be controlled with the min_count parameter. For example, if
you'd like the sum of an empty series to be NaN, pass min_count=1.
>>> pd.Series([], dtype="float64").sum(min_count=1)
nan
Thanks to the skipna parameter, min_count handles all-NA and
empty series identically.
>>> pd.Series([np.nan]).sum()
0.0
>>> pd.Series([np.nan]).sum(min_count=1)
nan
prod(axis=0, skipna=True, numeric_only=False, min_count=0, **kwargs)
Return the product of the values over the requested axis.
axis({index (0), columns (1)}) — Axis for the function to be applied on.ForSeriesthis parameter is unused and defaults to 0.
.. warning::
.. versionadded:: 2.0.0The behavior of DataFrame.prod with ``axis=None`` is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).skipna(bool, default True) — Exclude NA/null values when computing the result.numeric_only(bool, default False) — Include only float, int, boolean columns. Not implemented for Series.min_count(int, default 0) — The required number of valid values to perform the operation. If fewer thanmin_countnon-NA values are present the result will be NA.**kwargs— Additional keyword arguments to be passed to the function.
Series.sum : Return the sum.Series.min : Return the minimum. Series.max : Return the maximum. Series.idxmin : Return the index of the minimum. Series.idxmax : Return the index of the maximum. DataFrame.sum : Return the sum over the requested axis. DataFrame.min : Return the minimum over the requested axis. DataFrame.max : Return the maximum over the requested axis. DataFrame.idxmin : Return the index of the minimum over the requested axis. DataFrame.idxmax : Return the index of the maximum over the requested axis.
By default, the product of an empty or all-NA Series is 1
>>> pd.Series([], dtype="float64").prod()
1.0
This can be controlled with the min_count parameter
>>> pd.Series([], dtype="float64").prod(min_count=1)
nan
Thanks to the skipna parameter, min_count handles all-NA and
empty series identically.
>>> pd.Series([np.nan]).prod()
1.0
>>> pd.Series([np.nan]).prod(min_count=1)
nan
mean(axis=0, skipna=True, numeric_only=False, **kwargs)
Return the mean of the values over the requested axis.
axis({index (0), columns (1)}) — Axis for the function to be applied on.ForSeriesthis parameter is unused and defaults to 0.
For DataFrames, specifyingaxis=Nonewill apply the aggregation across both axes.
.. versionadded:: 2.0.0skipna(bool, default True) — Exclude NA/null values when computing the result.numeric_only(bool, default False) — Include only float, int, boolean columns. Not implemented for Series.**kwargs— Additional keyword arguments to be passed to the function.
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median(axis=0, skipna=True, numeric_only=False, **kwargs)
Return the median of the values over the requested axis.
axis({index (0), columns (1)}) — Axis for the function to be applied on.ForSeriesthis parameter is unused and defaults to 0.
For DataFrames, specifyingaxis=Nonewill apply the aggregation across both axes.
.. versionadded:: 2.0.0skipna(bool, default True) — Exclude NA/null values when computing the result.numeric_only(bool, default False) — Include only float, int, boolean columns. Not implemented for Series.**kwargs— Additional keyword arguments to be passed to the function.
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sem(axis=0, skipna=True, ddof=1, numeric_only=False, **kwargs)
Return unbiased standard error of the mean over requested axis.
Normalized by N-1 by default. This can be changed using the ddof argument
axis({index (0), columns (1)}) — ForSeriesthis parameter is unused and defaults to 0.
.. warning::
The behavior of DataFrame.sem with ``axis=None`` is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).skipna(bool, default True) — Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.ddof(int, default 1) — Delta Degrees of Freedom. The divisor used in calculations is N - ddof,where N represents the number of elements.numeric_only(bool, default False) — Include only float, int, boolean columns. Not implemented for Series.
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var(axis=0, skipna=True, ddof=1, numeric_only=False, **kwargs)
Return unbiased variance over requested axis.
Normalized by N-1 by default. This can be changed using the ddof argument.
axis({index (0), columns (1)}) — ForSeriesthis parameter is unused and defaults to 0.
.. warning::
The behavior of DataFrame.var with ``axis=None`` is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).skipna(bool, default True) — Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.ddof(int, default 1) — Delta Degrees of Freedom. The divisor used in calculations is N - ddof,where N represents the number of elements.numeric_only(bool, default False) — Include only float, int, boolean columns. Not implemented for Series.
>>> df = pd.DataFrame({'person_id': [0, 1, 2, 3],... 'age': [21, 25, 62, 43],
... 'height': [1.61, 1.87, 1.49, 2.01]}
... ).set_index('person_id')
>>> df
age height
person_id
0 21 1.61
1 25 1.87
2 62 1.49
3 43 2.01
>>> df.var()
age 352.916667
height 0.056367
dtype: float64
Alternatively, ddof=0 can be set to normalize by N instead of N-1:
>>> df.var(ddof=0)
age 264.687500
height 0.042275
dtype: float64
std(axis=0, skipna=True, ddof=1, numeric_only=False, **kwargs)
Return sample standard deviation over requested axis.
Normalized by N-1 by default. This can be changed using the ddof argument.
axis({index (0), columns (1)}) — ForSeriesthis parameter is unused and defaults to 0.
.. warning::
The behavior of DataFrame.std with ``axis=None`` is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).skipna(bool, default True) — Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.ddof(int, default 1) — Delta Degrees of Freedom. The divisor used in calculations is N - ddof,where N represents the number of elements.numeric_only(bool, default False) — Include only float, int, boolean columns. Not implemented for Series.
Notes
To have the same behaviour as numpy.std, use ddof=0 (instead of the
default ddof=1)
>>> df = pd.DataFrame({'person_id': [0, 1, 2, 3],... 'age': [21, 25, 62, 43],
... 'height': [1.61, 1.87, 1.49, 2.01]}
... ).set_index('person_id')
>>> df
age height
person_id
0 21 1.61
1 25 1.87
2 62 1.49
3 43 2.01
The standard deviation of the columns can be found as follows:
>>> df.std()
age 18.786076
height 0.237417
dtype: float64
Alternatively, ddof=0 can be set to normalize by N instead of N-1:
>>> df.std(ddof=0)
age 16.269219
height 0.205609
dtype: float64
skew(axis=0, skipna=True, numeric_only=False, **kwargs)
Return unbiased skew over requested axis.
Normalized by N-1.
axis({index (0), columns (1)}) — Axis for the function to be applied on.ForSeriesthis parameter is unused and defaults to 0.
For DataFrames, specifyingaxis=Nonewill apply the aggregation across both axes.
.. versionadded:: 2.0.0skipna(bool, default True) — Exclude NA/null values when computing the result.numeric_only(bool, default False) — Include only float, int, boolean columns. Not implemented for Series.**kwargs— Additional keyword arguments to be passed to the function.
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kurt(axis=0, skipna=True, numeric_only=False, **kwargs)
Return unbiased kurtosis over requested axis.
Kurtosis obtained using Fisher's definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.
axis({index (0), columns (1)}) — Axis for the function to be applied on.ForSeriesthis parameter is unused and defaults to 0.
For DataFrames, specifyingaxis=Nonewill apply the aggregation across both axes.
.. versionadded:: 2.0.0skipna(bool, default True) — Exclude NA/null values when computing the result.numeric_only(bool, default False) — Include only float, int, boolean columns. Not implemented for Series.**kwargs— Additional keyword arguments to be passed to the function.
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cummin(axis=None, skipna=True, *args, **kwargs)
Return cumulative minimum over a DataFrame or Series axis.
Returns a DataFrame or Series of the same size containing the cumulative minimum.
axis({0 or 'index', 1 or 'columns'}, default 0) — The index or the name of the axis. 0 is equivalent to None or 'index'.ForSeriesthis parameter is unused and defaults to 0.skipna(bool, default True) — Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
Return cumulative minimum of Series or DataFrame.
core.window.expanding.Expanding.min : Similar functionality but ignores NaN values.
DataFrame.min : Return the minimum over
DataFrame axis.
DataFrame.cummax : Return cumulative maximum over DataFrame axis.
DataFrame.cummin : Return cumulative minimum over DataFrame axis.
DataFrame.cumsum : Return cumulative sum over DataFrame axis.
DataFrame.cumprod : Return cumulative product over DataFrame axis.
Series
>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0 2.0
1 NaN
2 5.0
3 -1.0
4 0.0
dtype: float64
By default, NA values are ignored.
>>> s.cummin()
0 2.0
1 NaN
2 2.0
3 -1.0
4 -1.0
dtype: float64
To include NA values in the operation, use skipna=False
>>> s.cummin(skipna=False)
0 2.0
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
DataFrame
>>> df = pd.DataFrame([[2.0, 1.0],
... [3.0, np.nan],
... [1.0, 0.0]],
... columns=list('AB'))
>>> df
A B
0 2.0 1.0
1 3.0 NaN
2 1.0 0.0
By default, iterates over rows and finds the minimum
in each column. This is equivalent to axis=None or axis='index'.
>>> df.cummin()
A B
0 2.0 1.0
1 2.0 NaN
2 1.0 0.0
To iterate over columns and find the minimum in each row,
use axis=1
>>> df.cummin(axis=1)
A B
0 2.0 1.0
1 3.0 NaN
2 1.0 0.0
cummax(axis=None, skipna=True, *args, **kwargs)
Return cumulative maximum over a DataFrame or Series axis.
Returns a DataFrame or Series of the same size containing the cumulative maximum.
axis({0 or 'index', 1 or 'columns'}, default 0) — The index or the name of the axis. 0 is equivalent to None or 'index'.ForSeriesthis parameter is unused and defaults to 0.skipna(bool, default True) — Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
Return cumulative maximum of Series or DataFrame.
core.window.expanding.Expanding.max : Similar functionality but ignores NaN values.
DataFrame.max : Return the maximum over
DataFrame axis.
DataFrame.cummax : Return cumulative maximum over DataFrame axis.
DataFrame.cummin : Return cumulative minimum over DataFrame axis.
DataFrame.cumsum : Return cumulative sum over DataFrame axis.
DataFrame.cumprod : Return cumulative product over DataFrame axis.
Series
>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0 2.0
1 NaN
2 5.0
3 -1.0
4 0.0
dtype: float64
By default, NA values are ignored.
>>> s.cummax()
0 2.0
1 NaN
2 5.0
3 5.0
4 5.0
dtype: float64
To include NA values in the operation, use skipna=False
>>> s.cummax(skipna=False)
0 2.0
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
DataFrame
>>> df = pd.DataFrame([[2.0, 1.0],
... [3.0, np.nan],
... [1.0, 0.0]],
... columns=list('AB'))
>>> df
A B
0 2.0 1.0
1 3.0 NaN
2 1.0 0.0
By default, iterates over rows and finds the maximum
in each column. This is equivalent to axis=None or axis='index'.
>>> df.cummax()
A B
0 2.0 1.0
1 3.0 NaN
2 3.0 1.0
To iterate over columns and find the maximum in each row,
use axis=1
>>> df.cummax(axis=1)
A B
0 2.0 2.0
1 3.0 NaN
2 1.0 1.0
cumsum(axis=None, skipna=True, *args, **kwargs)
Return cumulative sum over a DataFrame or Series axis.
Returns a DataFrame or Series of the same size containing the cumulative sum.
axis({0 or 'index', 1 or 'columns'}, default 0) — The index or the name of the axis. 0 is equivalent to None or 'index'.ForSeriesthis parameter is unused and defaults to 0.skipna(bool, default True) — Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
Return cumulative sum of Series or DataFrame.
core.window.expanding.Expanding.sum : Similar functionality but ignores NaN values.
DataFrame.sum : Return the sum over
DataFrame axis.
DataFrame.cummax : Return cumulative maximum over DataFrame axis.
DataFrame.cummin : Return cumulative minimum over DataFrame axis.
DataFrame.cumsum : Return cumulative sum over DataFrame axis.
DataFrame.cumprod : Return cumulative product over DataFrame axis.
Series
>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0 2.0
1 NaN
2 5.0
3 -1.0
4 0.0
dtype: float64
By default, NA values are ignored.
>>> s.cumsum()
0 2.0
1 NaN
2 7.0
3 6.0
4 6.0
dtype: float64
To include NA values in the operation, use skipna=False
>>> s.cumsum(skipna=False)
0 2.0
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
DataFrame
>>> df = pd.DataFrame([[2.0, 1.0],
... [3.0, np.nan],
... [1.0, 0.0]],
... columns=list('AB'))
>>> df
A B
0 2.0 1.0
1 3.0 NaN
2 1.0 0.0
By default, iterates over rows and finds the sum
in each column. This is equivalent to axis=None or axis='index'.
>>> df.cumsum()
A B
0 2.0 1.0
1 5.0 NaN
2 6.0 1.0
To iterate over columns and find the sum in each row,
use axis=1
>>> df.cumsum(axis=1)
A B
0 2.0 3.0
1 3.0 NaN
2 1.0 1.0
cumprod(axis=None, skipna=True, *args, **kwargs)
Return cumulative product over a DataFrame or Series axis.
Returns a DataFrame or Series of the same size containing the cumulative product.
axis({0 or 'index', 1 or 'columns'}, default 0) — The index or the name of the axis. 0 is equivalent to None or 'index'.ForSeriesthis parameter is unused and defaults to 0.skipna(bool, default True) — Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
Return cumulative product of Series or DataFrame.
core.window.expanding.Expanding.prod : Similar functionality but ignores NaN values.
DataFrame.prod : Return the product over
DataFrame axis.
DataFrame.cummax : Return cumulative maximum over DataFrame axis.
DataFrame.cummin : Return cumulative minimum over DataFrame axis.
DataFrame.cumsum : Return cumulative sum over DataFrame axis.
DataFrame.cumprod : Return cumulative product over DataFrame axis.
Series
>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0 2.0
1 NaN
2 5.0
3 -1.0
4 0.0
dtype: float64
By default, NA values are ignored.
>>> s.cumprod()
0 2.0
1 NaN
2 10.0
3 -10.0
4 -0.0
dtype: float64
To include NA values in the operation, use skipna=False
>>> s.cumprod(skipna=False)
0 2.0
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
DataFrame
>>> df = pd.DataFrame([[2.0, 1.0],
... [3.0, np.nan],
... [1.0, 0.0]],
... columns=list('AB'))
>>> df
A B
0 2.0 1.0
1 3.0 NaN
2 1.0 0.0
By default, iterates over rows and finds the product
in each column. This is equivalent to axis=None or axis='index'.
>>> df.cumprod()
A B
0 2.0 1.0
1 6.0 NaN
2 6.0 0.0
To iterate over columns and find the product in each row,
use axis=1
>>> df.cumprod(axis=1)
A B
0 2.0 2.0
1 3.0 NaN
2 1.0 0.0
nunique(axis=0, dropna=True)
Count number of distinct elements in specified axis.
Return Series with number of distinct elements. Can ignore NaN values.
axis({0 or 'index', 1 or 'columns'}, default 0) — The axis to use. 0 or 'index' for row-wise, 1 or 'columns' forcolumn-wise.dropna(bool, default True) — Don't include NaN in the counts.
Series.nunique: Method nunique for Series.DataFrame.count: Count non-NA cells for each column or row.
>>> df = pd.DataFrame({'A': [4, 5, 6], 'B': [4, 1, 1]})>>> df.nunique()
A 3
B 2
dtype: int64
>>> df.nunique(axis=1)
0 1
1 2
2 2
dtype: int64
idxmin(axis=0, skipna=True, numeric_only=False)
Return index of first occurrence of minimum over requested axis.
NA/null values are excluded.
axis({0 or 'index', 1 or 'columns'}, default 0) — The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.skipna(bool, default True) — Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.numeric_only(bool, default False) — Include onlyfloat,intorbooleandata.
.. versionadded:: 1.5.0
Indexes of minima along the specified axis.
ValueError—- If the row/column is empty
Series.idxmin : Return index of the minimum element.
Notes
This method is the DataFrame version of ndarray.argmin.
Consider a dataset containing food consumption in Argentina.
>>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
... 'co2_emissions': [37.2, 19.66, 1712]},
... index=['Pork', 'Wheat Products', 'Beef'])
>>> df
consumption co2_emissions
Pork 10.51 37.20
Wheat Products 103.11 19.66
Beef 55.48 1712.00
By default, it returns the index for the minimum value in each column.
>>> df.idxmin()
consumption Pork
co2_emissions Wheat Products
dtype: object
To return the index for the minimum value in each row, use axis="columns".
>>> df.idxmin(axis="columns")
Pork consumption
Wheat Products co2_emissions
Beef consumption
dtype: object
idxmax(axis=0, skipna=True, numeric_only=False)
Return index of first occurrence of maximum over requested axis.
NA/null values are excluded.
axis({0 or 'index', 1 or 'columns'}, default 0) — The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.skipna(bool, default True) — Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.numeric_only(bool, default False) — Include onlyfloat,intorbooleandata.
.. versionadded:: 1.5.0
Indexes of maxima along the specified axis.
ValueError—- If the row/column is empty
Series.idxmax : Return index of the maximum element.
Notes
This method is the DataFrame version of ndarray.argmax.
Consider a dataset containing food consumption in Argentina.
>>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
... 'co2_emissions': [37.2, 19.66, 1712]},
... index=['Pork', 'Wheat Products', 'Beef'])
>>> df
consumption co2_emissions
Pork 10.51 37.20
Wheat Products 103.11 19.66
Beef 55.48 1712.00
By default, it returns the index for the maximum value in each column.
>>> df.idxmax()
consumption Wheat Products
co2_emissions Beef
dtype: object
To return the index for the maximum value in each row, use axis="columns".
>>> df.idxmax(axis="columns")
Pork co2_emissions
Wheat Products consumption
Beef co2_emissions
dtype: object
mode(axis=0, numeric_only=False, dropna=True)
Get the mode(s) of each element along the selected axis.
The mode of a set of values is the value that appears most often. It can be multiple values.
axis({0 or 'index', 1 or 'columns'}, default 0) — The axis to iterate over while searching for the mode:- 0 or 'index' : get mode of each column
- 1 or 'columns' : get mode of each row.
numeric_only(bool, default False) — If True, only apply to numeric columns.dropna(bool, default True) — Don't consider counts of NaN/NaT.
The modes of each column or row.
Series.mode : Return the highest frequency value in a Series.Series.value_counts : Return the counts of values in a Series.
>>> df = pd.DataFrame([('bird', 2, 2),... ('mammal', 4, np.nan),
... ('arthropod', 8, 0),
... ('bird', 2, np.nan)],
... index=('falcon', 'horse', 'spider', 'ostrich'),
... columns=('species', 'legs', 'wings'))
>>> df
species legs wings
falcon bird 2 2.0
horse mammal 4 NaN
spider arthropod 8 0.0
ostrich bird 2 NaN
By default, missing values are not considered, and the mode of wings
are both 0 and 2. Because the resulting DataFrame has two rows,
the second row of species and legs contains NaN.
>>> df.mode()
species legs wings
0 bird 2.0 0.0
1 NaN NaN 2.0
Setting dropna=False NaN values are considered and they can be
the mode (like for wings).
>>> df.mode(dropna=False)
species legs wings
0 bird 2 NaN
Setting numeric_only=True, only the mode of numeric columns is
computed, and columns of other types are ignored.
>>> df.mode(numeric_only=True)
legs wings
0 2.0 0.0
1 NaN 2.0
To compute the mode over columns and not rows, use the axis parameter:
>>> df.mode(axis='columns', numeric_only=True)
0 1
falcon 2.0 NaN
horse 4.0 NaN
spider 0.0 8.0
ostrich 2.0 NaN
quantile(q=0.5, axis=0, numeric_only=False, interpolation='linear', method='single')
Return values at the given quantile over requested axis.
q(float or array-like, default 0.5 (50% quantile)) — Value between 0 <= q <= 1, the quantile(s) to compute.axis({0 or 'index', 1 or 'columns'}, default 0) — Equals 0 or 'index' for row-wise, 1 or 'columns' for column-wise.numeric_only(bool, default False) — Include onlyfloat,intorbooleandata.
.. versionchanged:: 2.0.0 The default value ofnumeric_onlyis nowFalse.interpolation({'linear', 'lower', 'higher', 'midpoint', 'nearest'}) — This optional parameter specifies the interpolation method to use,when the desired quantile lies between two data pointsiandj:
- linear:
i + (j - i) * fraction, wherefractionis the fractional part of the index surrounded byiandj. - lower:
i. - higher:
j. - nearest:
iorjwhichever is nearest. - midpoint: (
i+j) / 2.
- linear:
method({'single', 'table'}, default 'single') — Whether to compute quantiles per-column ('single') or over all columns('table'). When 'table', the only allowed interpolation methods are 'nearest', 'lower', and 'higher'.
ee . e .
core.window.rolling.Rolling.quantile: Rolling quantile.numpy.percentile: Numpy function to compute the percentile.
>>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),... columns=['a', 'b'])
>>> df.quantile(.1)
a 1.3
b 3.7
Name: 0.1, dtype: float64
>>> df.quantile([.1, .5])
a b
0.1 1.3 3.7
0.5 2.5 55.0
Specifying method='table' will compute the quantile over all columns.
>>> df.quantile(.1, method="table", interpolation="nearest")
a 1
b 1
Name: 0.1, dtype: int64
>>> df.quantile([.1, .5], method="table", interpolation="nearest")
a b
0.1 1 1
0.5 3 100
Specifying numeric_only=False will also compute the quantile of
datetime and timedelta data.
>>> df = pd.DataFrame({'A': [1, 2],
... 'B': [pd.Timestamp('2010'),
... pd.Timestamp('2011')],
... 'C': [pd.Timedelta('1 days'),
... pd.Timedelta('2 days')]})
>>> df.quantile(0.5, numeric_only=False)
A 1.5
B 2010-07-02 12:00:00
C 1 days 12:00:00
Name: 0.5, dtype: object
to_timestamp(freq=None, how='start', axis=0, copy=None)
Cast to DatetimeIndex of timestamps, at beginning of period.
freq(str, default frequency of PeriodIndex) — Desired frequency.how({'s', 'e', 'start', 'end'}) — Convention for converting period to timestamp; start of periodvs. end.axis({0 or 'index', 1 or 'columns'}, default 0) — The axis to convert (the index by default).copy(bool, default True) — If False then underlying input data is not copied.
.. note:: Thecopykeyword will change behavior in pandas 3.0.Copy-on-Write <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>__ will be enabled by default, which means that all methods with acopykeyword will use a lazy copy mechanism to defer the copy and ignore thecopykeyword. Thecopykeyword will be removed in a future version of pandas.
You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True``
The DataFrame has a DatetimeIndex.
>>> idx = pd.PeriodIndex(['2023', '2024'], freq='Y')>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df1 = pd.DataFrame(data=d, index=idx)
>>> df1
col1 col2
2023 1 3
2024 2 4
The resulting timestamps will be at the beginning of the year in this case
>>> df1 = df1.to_timestamp()
>>> df1
col1 col2
2023-01-01 1 3
2024-01-01 2 4
>>> df1.index
DatetimeIndex(['2023-01-01', '2024-01-01'], dtype='datetime64[ns]', freq=None)
Using freq which is the offset that the Timestamps will have
>>> df2 = pd.DataFrame(data=d, index=idx)
>>> df2 = df2.to_timestamp(freq='M')
>>> df2
col1 col2
2023-01-31 1 3
2024-01-31 2 4
>>> df2.index
DatetimeIndex(['2023-01-31', '2024-01-31'], dtype='datetime64[ns]', freq=None)
to_period(freq=None, axis=0, copy=None)
Convert DataFrame from DatetimeIndex to PeriodIndex.
Convert DataFrame from DatetimeIndex to PeriodIndex with desired frequency (inferred from index if not passed).
freq(str, default) — Frequency of the PeriodIndex.axis({0 or 'index', 1 or 'columns'}, default 0) — The axis to convert (the index by default).copy(bool, default True) — If False then underlying input data is not copied.
.. note:: Thecopykeyword will change behavior in pandas 3.0.Copy-on-Write <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>__ will be enabled by default, which means that all methods with acopykeyword will use a lazy copy mechanism to defer the copy and ignore thecopykeyword. Thecopykeyword will be removed in a future version of pandas.
You can already get the future behavior and improvements through enabling copy on write ``pd.options.mode.copy_on_write = True``
The DataFrame has a PeriodIndex.
>>> idx = pd.to_datetime(... [
... "2001-03-31 00:00:00",
... "2002-05-31 00:00:00",
... "2003-08-31 00:00:00",
... ]
... )
>>> idx
DatetimeIndex(['2001-03-31', '2002-05-31', '2003-08-31'],
dtype='datetime64[ns]', freq=None)
>>> idx.to_period("M")
PeriodIndex(['2001-03', '2002-05', '2003-08'], dtype='period[M]')
For the yearly frequency
>>> idx.to_period("Y")
PeriodIndex(['2001', '2002', '2003'], dtype='period[Y-DEC]')
isin(values)
Whether each element in the DataFrame is contained in values.
values(iterable, Series, DataFrame or dict) — The result will only be true at a location if all thelabels match. Ifvaluesis a Series, that's the index. Ifvaluesis a dict, the keys must be the column names, which must match. Ifvaluesis a DataFrame, then both the index and column labels must match.
DataFrame of booleans showing whether each element in the DataFrameis contained in values.
DataFrame.eq: Equality test for DataFrame.Series.isin: Equivalent method on Series. Series.str.contains: Test if pattern or regex is contained within a string of a Series or Index.
>>> df = pd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]},... index=['falcon', 'dog'])
>>> df
num_legs num_wings
falcon 2 2
dog 4 0
When values is a list check whether every value in the DataFrame
is present in the list (which animals have 0 or 2 legs or wings)
>>> df.isin([0, 2])
num_legs num_wings
falcon True True
dog False True
To check if values is not in the DataFrame, use the ~ operator:
>>> ~df.isin([0, 2])
num_legs num_wings
falcon False False
dog True False
When values is a dict, we can pass values to check for each
column separately:
>>> df.isin({'num_wings': [0, 3]})
num_legs num_wings
falcon False False
dog False True
When values is a Series or DataFrame the index and column must
match. Note that 'falcon' does not match based on the number of legs
in other.
>>> other = pd.DataFrame({'num_legs': [8, 3], 'num_wings': [0, 2]},
... index=['spider', 'falcon'])
>>> df.isin(other)
num_legs num_wings
falcon False True
dog False False
create(value)
Create a channel from a list.
The second dimension is identified by tuple. if all elements are tuple, then a channel is created directly. Otherwise, elements are converted to tuples first and channels are created then.
>>> Channel.create([1, 2, 3]) # 3 rows, 1 column>>> Channel.create([(1,2,3)]) # 1 row, 3 columns
value(Union) — The value to create a channel
A channel (dataframe)
from_glob(pattern, ftype='any', sortby='name', reverse=False)
Create a channel with a glob pattern
ftype(str, optional) — The file type, one of any, link, dir and filesortby(str, optional) — How the files should be sorted. One of name, mtime and sizereverse(bool, optional) — Whether sort them in a reversed way.
The channel
from_pairs(pattern, ftype='any', sortby='name', reverse=False)
Create a width=2 channel with a glob pattern
ftype(str, optional) — The file type, one of any, link, dir and filesortby(str, optional) — How the files should be sorted. One of name, mtime and sizereverse(bool, optional) — Whether sort them in a reversed way.
The channel
from_csv(*args, **kwargs)
Create a channel from a csv file
Uses pandas.read_csv() to create a channel
*args— and**kwargs— Arguments passing to pandas.read_csv()
from_excel(*args, **kwargs)
Create a channel from an excel file.
Uses pandas.read_excel() to create a channel
*args— and**kwargs— Arguments passing to pandas.read_excel()
from_table(*args, **kwargs)
Create a channel from a table file.
Uses pandas.read_table() to create a channel
*args— and**kwargs— Arguments passing to pandas.read_table()
pipen.channel.expand_dir(data, col=0, pattern='*', ftype='any', sortby='name', reverse=False)
Expand a Channel according to the files in
This is only applicable to a 1-row channel.
>>> ch = channel.create([('./', 1)])>>> ch >> expand()
>>> [['./a', 1], ['./b', 1], ['./c', 1]]
col(str | int, optional) — the index or name of the column used to expandpattern(str, optional) — use a pattern to filter the files/dirs, default:*ftype(str, optional) — the type of the files/dirs to include- - 'dir', 'file', 'link' or 'any' (default)
sortby(str, optional) — how the list is sorted- - 'name' (default), 'mtime', 'size'
reverse(bool, optional) — reverse sort.
The expanded channel
pipen.channel.collapse_files(data, col=0)
Collapse a Channel according to the files in
Note that other values in other rows will be discarded.
>>> ch = channel.create([['./a', 1], ['./b', 1], ['./c', 1]])>>> ch >> collapse()
>>> [['.', 1]]
data(DataFrame) — The original channelcol(str | int, optional) — the index or name of the column used to collapse on
The collapsed channel