dr.slice_sample() allows randomly sampling rows (observations) from a DataFrame.

---

1. dr.slice_sample(n=..., random_state=...)

2. dr.slice_sample(prop=..., random_state=...)

In [1]:

import datar.all as dr
from datar import f
import pandas as pd
from pathlib import Path

pd.set_option("display.width", 200)

# Get the path object pointing to the ``notebooks`` directory that contains *.csv files 
data_dir = next(Path("/home").rglob("*/notebooks/*.csv")).parent

import datar.all as dr from datar import f import pandas as pd from pathlib import Path pd.set_option("display.width", 200) # Get the path object pointing to the ``notebooks`` directory that contains *.csv files data_dir = next(Path("/home").rglob("*/notebooks/*.csv")).parent

In [2]:

df_baseball = pd.read_csv(
    filepath_or_buffer=data_dir/"baseball.csv",
    dtype={
        "Team": "category",
        "Position": "category",
        "PosCategory": "category"
    }
)

print(df_baseball.info())

df_baseball = pd.read_csv( filepath_or_buffer=data_dir/"baseball.csv", dtype={ "Team": "category", "Position": "category", "PosCategory": "category" } ) print(df_baseball.info())

<class 'pandas.DataFrame'>
RangeIndex: 1015 entries, 0 to 1014
Data columns (total 7 columns):
 #   Column       Non-Null Count  Dtype   
---  ------       --------------  -----   
 0   Name         1015 non-null   str     
 1   Team         1015 non-null   category
 2   Position     1015 non-null   category
 3   Height       1015 non-null   int64   
 4   Weight       1015 non-null   int64   
 5   Age          1015 non-null   float64 
 6   PosCategory  1015 non-null   category
dtypes: category(3), float64(1), int64(2), str(1)
memory usage: 49.0 KB
None

1. dr.slice_sample(n=...)¶

The n=... argument specifies the number of rows to return in the sample.

In [4]:

print(
    df_baseball >>
    dr.slice_sample(n=5, random_state=42) # random_state=... ensures reproducibility (like .seed(42))
)

print( df_baseball >> dr.slice_sample(n=5, random_state=42) # random_state=... ensures reproducibility (like .seed(42)) )

                  Name       Team        Position  Height  Weight       Age PosCategory
                 <str> <category>      <category> <int64> <int64> <float64>  <category>
752     Wilson_Betemit         LA   Third_Baseman      75     190     26.59   Infielder
519        Mark_DeRosa        CHC      Outfielder      73     205     32.01  Outfielder
210       Miguel_Cairo        NYY  Second_Baseman      73     208     32.82   Infielder
611  Edwin_Encarnacion        CIN   Third_Baseman      73     195     24.15   Infielder
914      Humberto_Cota        PIT         Catcher      72     210     28.06     Catcher

2. dr.slice_sample(prop=...)¶

The frac=... argument specifies the fraction of rows to return in the sample.

frac should be between 0 and 1.

If fract > 1, should set replace=True => Upsampling with replacement.

In [6]:

print(
    df_baseball >>
    dr.slice_sample(prop=0.01, random_state=41) # 1%
)

'''
0.01 * 1015 = 10.15 => 10 rows
'''

print( df_baseball >> dr.slice_sample(prop=0.01, random_state=41) # 1% ) ''' 0.01 * 1015 = 10.15 => 10 rows '''

                Name       Team          Position  Height  Weight       Age PosCategory
               <str> <category>        <category> <int64> <int64> <float64>  <category>
149   David_Dellucci        CLE        Outfielder      71     190     33.33  Outfielder
296  George_Sherrill        SEA    Relief_Pitcher      72     210     29.86     Pitcher
848  Adrian_Gonzalez         SD     First_Baseman      74     220     24.81   Infielder
294      Jeff_Weaver        SEA  Starting_Pitcher      77     200     30.52     Pitcher
884  Cristian_Guzman        WAS         Shortstop      72     205     28.94   Infielder
194      Alan_Embree        OAK    Relief_Pitcher      74     190     37.10     Pitcher
524    Carlos_Marmol        CHC  Starting_Pitcher      74     180     24.38     Pitcher
704       Jorge_Sosa        NYM    Relief_Pitcher      74     170     29.84     Pitcher
283      Jeremy_Reed        SEA        Outfielder      72     185     25.71  Outfielder
379    Michael_Young        TEX         Shortstop      73     190     30.36   Infielder

Out[6]:

'\n0.01 * 1015 = 10.15 => 10 rows\n'