1. dr.slice(): allows selecting rows by their positions (indices).
   • Basic usage: dr.slice_(index1, index2, ...)
   • Range indexing: dr.slice_(dr.c[start:end]) to select rows from start to end-1.

==================================

2. dr.slice_min(): allows selecting rows with the smallest values in a specified column.

==================================

3. dr.slice_max(): allows selecting rows with the largest values in a specified column.

In [11]:

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

warnings.filterwarnings("ignore")
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 import warnings warnings.filterwarnings("ignore") 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.head())

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

              Name       Team       Position  Height  Weight       Age PosCategory
             <str> <category>     <category> <int64> <int64> <float64>  <category>
0    Adam_Donachie        BAL        Catcher      74     180     22.99     Catcher
1        Paul_Bako        BAL        Catcher      74     215     34.69     Catcher
2  Ramon_Hernandez        BAL        Catcher      72     210     30.78     Catcher
3     Kevin_Millar        BAL  First_Baseman      72     210     35.43   Infielder
4      Chris_Gomez        BAL  First_Baseman      73     188     35.71   Infielder

1. dr.slice(...)¶

In [3]:

from pipda import register_verb

dr.slice = register_verb(func=dr.slice_)

'''
dr.slice_() to avoid conflict with Python built-in function slice()
=> Use registter_verb to bring it back as dr.slice()
'''

from pipda import register_verb dr.slice = register_verb(func=dr.slice_) ''' dr.slice_() to avoid conflict with Python built-in function slice() => Use registter_verb to bring it back as dr.slice() '''

Out[3]:

'\ndr.slice_() to avoid conflict with Python built-in function slice()\n=> Use registter_verb to bring it back as dr.slice()\n'

In [4]:

##-------------##
## Basic usage ##
##-------------##

print(
    df_baseball
    >> dr.slice(0)
)

##-------------## ## Basic usage ## ##-------------## print( df_baseball >> dr.slice(0) )

            Name       Team   Position  Height  Weight       Age PosCategory
           <str> <category> <category> <int64> <int64> <float64>  <category>
0  Adam_Donachie        BAL    Catcher      74     180     22.99     Catcher

In [5]:

print(
    df_baseball
    >> dr.slice(0, 2, 5, 9)
)

print( df_baseball >> dr.slice(0, 2, 5, 9) )

              Name       Team        Position  Height  Weight       Age PosCategory
             <str> <category>      <category> <int64> <int64> <float64>  <category>
0    Adam_Donachie        BAL         Catcher      74     180     22.99     Catcher
2  Ramon_Hernandez        BAL         Catcher      72     210     30.78     Catcher
5    Brian_Roberts        BAL  Second_Baseman      69     176     29.39   Infielder
9       Adam_Stern        BAL      Outfielder      71     180     27.05  Outfielder

In [6]:

print(
    df_baseball
    >> dr.slice(-1, -2)
)

print( df_baseball >> dr.slice(-1, -2) )

               Name       Team        Position  Height  Weight       Age PosCategory
              <str> <category>      <category> <int64> <int64> <float64>  <category>
1014    Josh_Kinney        STL  Relief_Pitcher      73     195     27.92     Pitcher
1013  Randy_Keisler        STL  Relief_Pitcher      75     190     31.01     Pitcher

In [12]:

##----------------##
## Range indexing ##
##----------------##

print(
    df_baseball
    >> dr.slice(dr.c[0:4])
)

##----------------## ## Range indexing ## ##----------------## print( df_baseball >> dr.slice(dr.c[0:4]) )

              Name       Team       Position  Height  Weight       Age PosCategory
             <str> <category>     <category> <int64> <int64> <float64>  <category>
0    Adam_Donachie        BAL        Catcher      74     180     22.99     Catcher
1        Paul_Bako        BAL        Catcher      74     215     34.69     Catcher
2  Ramon_Hernandez        BAL        Catcher      72     210     30.78     Catcher
3     Kevin_Millar        BAL  First_Baseman      72     210     35.43   Infielder

In [13]:

print(
    df_baseball
    >> dr.slice(dr.c[:3])
)

print( df_baseball >> dr.slice(dr.c[:3]) )

              Name       Team   Position  Height  Weight       Age PosCategory
             <str> <category> <category> <int64> <int64> <float64>  <category>
0    Adam_Donachie        BAL    Catcher      74     180     22.99     Catcher
1        Paul_Bako        BAL    Catcher      74     215     34.69     Catcher
2  Ramon_Hernandez        BAL    Catcher      72     210     30.78     Catcher

In [14]:

print(
    df_baseball
    >> dr.slice(dr.c[1010:])
)

print( df_baseball >> dr.slice(dr.c[1010:]) )

                Name       Team        Position  Height  Weight       Age PosCategory
               <str> <category>      <category> <int64> <int64> <float64>  <category>
1010   Brad_Thompson        STL  Relief_Pitcher      73     190     25.08     Pitcher
1011   Tyler_Johnson        STL  Relief_Pitcher      74     180     25.73     Pitcher
1012  Chris_Narveson        STL  Relief_Pitcher      75     205     25.19     Pitcher
1013   Randy_Keisler        STL  Relief_Pitcher      75     190     31.01     Pitcher
1014     Josh_Kinney        STL  Relief_Pitcher      73     195     27.92     Pitcher

In [15]:

print(
    df_baseball
    >> dr.slice(dr.c[0:10:2])
)

print( df_baseball >> dr.slice(dr.c[0:10:2]) )

              Name       Team       Position  Height  Weight       Age PosCategory
             <str> <category>     <category> <int64> <int64> <float64>  <category>
0    Adam_Donachie        BAL        Catcher      74     180     22.99     Catcher
2  Ramon_Hernandez        BAL        Catcher      72     210     30.78     Catcher
4      Chris_Gomez        BAL  First_Baseman      73     188     35.71   Infielder
6    Miguel_Tejada        BAL      Shortstop      69     209     30.77   Infielder
8      Aubrey_Huff        BAL  Third_Baseman      76     231     30.19   Infielder

2. dr.slice_min(...)¶

allows selecting rows with the smallest values in a specified column

In [16]:

print(
    df_baseball
    >> dr.slice_min(f.Age, n=3) # Select 3 rows with the smallest Age
)

print( df_baseball >> dr.slice_min(f.Age, n=3) # Select 3 rows with the smallest Age )

                Name       Team          Position  Height  Weight       Age PosCategory
               <str> <category>        <category> <int64> <int64> <float64>  <category>
288  Felix_Hernandez        SEA  Starting_Pitcher      75     225     20.90     Pitcher
318     Delmon_Young         TB        Outfielder      75     205     21.46  Outfielder
289  Ryan_Feierabend        SEA  Starting_Pitcher      75     190     21.52     Pitcher

In [17]:

print(
    df_baseball
    >> dr.slice_min(f.Age, n=5, with_ties=True) # Include ties
)

print( df_baseball >> dr.slice_min(f.Age, n=5, with_ties=True) # Include ties )

                Name       Team          Position  Height  Weight       Age PosCategory
               <str> <category>        <category> <int64> <int64> <float64>  <category>
288  Felix_Hernandez        SEA  Starting_Pitcher      75     225     20.90     Pitcher
318     Delmon_Young         TB        Outfielder      75     205     21.46  Outfielder
289  Ryan_Feierabend        SEA  Starting_Pitcher      75     190     21.52     Pitcher
285       Adam_Jones        SEA        Outfielder      74     200     21.58  Outfielder
267    Andrew_Miller        DET    Relief_Pitcher      78     210     21.78     Pitcher

3. dr.slice_max(...)¶

allows selecting rows with the largest values in a specified column.

In [18]:

print(
    df_baseball
    >> dr.slice_max(f.Height, n=3) # Select 3 rows with the largest Height
)

print( df_baseball >> dr.slice_max(f.Height, n=3) # Select 3 rows with the largest Height )

              Name       Team          Position  Height  Weight       Age PosCategory
             <str> <category>        <category> <int64> <int64> <float64>  <category>
909      Jon_Rauch        WAS    Relief_Pitcher      83     260     28.42     Pitcher
558  Randy_Johnson        ARZ  Starting_Pitcher      82     231     43.47     Pitcher
862    Chris_Young         SD  Starting_Pitcher      82     250     27.77     Pitcher

In [19]:

print(
    df_baseball
    >> dr.slice_max(f.Height, n=5, with_ties=True) # Include ties
)

print( df_baseball >> dr.slice_max(f.Height, n=5, with_ties=True) # Include ties )

                 Name       Team          Position  Height  Weight       Age PosCategory
                <str> <category>        <category> <int64> <int64> <float64>  <category>
909         Jon_Rauch        WAS    Relief_Pitcher      83     260     28.42     Pitcher
558     Randy_Johnson        ARZ  Starting_Pitcher      82     231     43.47     Pitcher
862       Chris_Young         SD  Starting_Pitcher      82     250     27.77     Pitcher
59       Andrew_Sisco        CWS    Relief_Pitcher      81     260     24.13     Pitcher
764  Mark_Hendrickson         LA  Starting_Pitcher      81     230     32.69     Pitcher