.loc

The .loc property of the DataFrame object allows the return of specified rows and/or columns from that DataFrame. It can do so using a label or label(s), or a boolean array of the same size as the axis being filtered.

Syntax

dataframevalue.loc[rows,columns]

Note: .loc is not a method, it is a property indexed via square brackets.

Inside the square brackets are specified indices for rows and, optionally, specified indices for columns.

The indices to access can be specified in several ways:

• A single label. Note: an integer is treated as a label, not a row or column position.
• An array of labels.
• An array of booleans the same size as the index being accessed.
• Labels using the Python slice notation. Note: slices for .loc will include both endpoints.

Example

The example below creates a DataFrame and uses the .loc property to return various combinations of rows and columns:

import pandas as pd

d = {'col 1' : [1,2,3,4], 'col 2' : ['A','B','C','D'], 'col 3' : [5,6,7,8], 'col 4' : ['E','F','G','H']}

df = pd.DataFrame(data = d)
print(f'Original df:\n{df}\n')

first_loc = df.loc[:,'col 3']
print(f'First .loc:\n{first_loc}\n')

second_loc = df.loc[:2]
print(f'Second .loc:\n{second_loc}\n')

third_loc = df.loc[[0,3],'col 2':]
print(f'Third .loc:\n{third_loc}')

This will print the following:

Original df:
   col 1 col 2  col 3 col 4
0      1     A      5     E
1      2     B      6     F
2      3     C      7     G
3      4     D      8     H

First .loc:
0    5
1    6
2    7
3    8
Name: col 3, dtype: int64

Second .loc:
   col 1 col 2  col 3 col 4
0      1     A      5     E
1      2     B      6     F
2      3     C      7     G

Third .loc:
  col 2  col 3 col 4
0     A      5     E
3     D      8     H