You can use the following methods to only keep certain columns in a pandas DataFrame:

Method 1: Specify Columns to Keep

#only keep columns 'col1' and 'col2'
df[['col1', 'col2']]

Method 2: Specify Columns to Drop

#drop columns 'col3' and 'col4'
df[df.columns[~df.columns.isin(['col3', 'col4'])]]

The following examples show how to use each method with the following pandas DataFrame:

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

Method 1: Specify Columns to Keep

The following code shows how to define a new DataFrame that only keeps the “team” and “points” columns:

#create new DataFrame and only keep 'team' and 'points' columns
df2 = df[['team', 'points']]

#view new DataFrame
df2

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

Notice that the resulting DataFrame only keeps the two columns that we specified.

Method 2: Specify Columns to Drop

The following code shows how to define a new DataFrame that drops the “assists” and “rebounds” columns from the original DataFrame:

#create new DataFrame and that drops 'assists' and 'rebounds'
df2 = df[df.columns[~df.columns.isin(['assists', 'rebounds'])]]

#view new DataFrame
df2

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

Notice that the resulting DataFrame drops the “assists” and “rebounds” columns from the original DataFrame and keeps the remaining columns.

Additional Resources

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