Often you may want to select the columns of a pandas DataFrame based on their index value.

If you’d like to select columns based on integer indexing, you can use the .iloc function.

If you’d like to select columns based on label indexing, you can use the .loc function.

This tutorial provides an example of how to use each of these functions in practice.

Example 1: Select Columns Based on Integer Indexing

The following code shows how to create a pandas DataFrame and use .iloc to select the column with an index integer value of 3:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['A', 'A', 'A', 'B', 'B', 'B'],
                   'points': [11, 7, 8, 10, 13, 13],
                   'assists': [5, 7, 7, 9, 12, 9],
                   'rebounds': [11, 8, 10, 6, 6, 5]})

#view DataFrame
df

	team	points	assists	rebounds
0	A	11	5	11
1	A	7	7	8
2	A	8	7	10
3	B	10	9	6
4	B	13	12	6
5	B	13	9	5

#select column with index position 3
df.iloc[:, 3]

0    11
1     8
2    10
3     6
4     6
5     5
Name: rebounds, dtype: int64

We can use similar syntax to select multiple columns:

#select columns with index positions 1 and 3
df.iloc[:, [1, 3]]


        points	rebounds
0	11	11
1	7	8
2	8	10
3	10	6
4	13	6
5	13	5

Or we could select all columns in a range:

#select columns with index positions in range 0 through 3
df.iloc[:, 0:3]

        team	points	assists
0	A	11	5
1	A	7	7
2	A	8	7
3	B	10	9
4	B	13	12
5	B	13	9

Example 2: Select Columns Based on Label Indexing

The following code shows how to create a pandas DataFrame and use .loc to select the column with an index label of ‘rebounds’:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['A', 'A', 'A', 'B', 'B', 'B'],
                   'points': [11, 7, 8, 10, 13, 13],
                   'assists': [5, 7, 7, 9, 12, 9],
                   'rebounds': [11, 8, 10, 6, 6, 5]})

#view DataFrame
df

	team	points	assists	rebounds
0	A	11	5	11
1	A	7	7	8
2	A	8	7	10
3	B	10	9	6
4	B	13	12	6
5	B	13	9	5

#select column with index label 'rebounds'
df.loc[:, 'rebounds']

0    11
1     8
2    10
3     6
4     6
5     5
Name: rebounds, dtype: int64

We can use similar syntax to select multiple columns with different index labels:

#select the columns with index labels 'points' and 'rebounds'
df.loc[:, ['points', 'rebounds']]

	points	rebounds
0	11	11
1	7	8
2	8	10
3	10	6
4	13	6
5	13	5

Or we could select all columns in a range:

#select columns with index labels between 'team' and 'assists'
df.loc[:, 'team':'assists']

	team	points	assists
0	A	11	5
1	A	7	7
2	A	8	7
3	B	10	9
4	B	13	12
5	B	13	9

Related:

Additional Resources

The following tutorials explain how to perform other common operations in pandas:

How to Get Row Numbers in a Pandas DataFrame
How to Drop the Index Column in a Pandas DataFrame