You can use the following basic syntax to drop columns from a pandas DataFrame that are not in a specific list:
#define columns to keep keep_cols = ['col1', 'col2', 'col3'] #create new dataframe by dropping columns not in list new_df = df[df.columns.intersection(keep_cols)]
This particular example will drop any columns from the DataFrame that are not equal to col1, col2, or col3.
The following example shows how to use this syntax in practice.
Example: Drop Columns Not in List in Pandas
Suppose we have the following pandas DataFrame that contains information about various basketball players:
import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'], 'points': [18, 22, 19, 14, 14, 11, 20, 28], 'assists': [5, 7, 7, 9, 12, 9, 9, 4], 'rebounds': [11, 8, 10, 6, 6, 5, 9, 12], 'steals': [4, 4, 10, 12, 8, 5, 5, 2]}) #view DataFrame print(df) team points assists rebounds steals 0 A 18 5 11 4 1 B 22 7 8 4 2 C 19 7 10 10 3 D 14 9 6 12 4 E 14 12 6 8 5 F 11 9 5 5 6 G 20 9 9 5 7 H 28 4 12 2
Now suppose that we would like to create a new DataFrame that drops all columns that are not in the following list of columns: team, points, and steals.
We can use the following syntax to do so:
#define columns to keep keep_cols = ['team', 'points', 'steals'] #create new dataframe by dropping columns not in list new_df = df[df.columns.intersection(keep_cols)] #view new dataframe print(new_df) team points steals 0 A 18 4 1 B 22 4 2 C 19 10 3 D 14 12 4 E 14 8 5 F 11 5 6 G 20 5 7 H 28 2
Notice that each of the columns from the original DataFrame that are not in the keep_cols list have been dropped from the new DataFrame.
Additional Resources
The following tutorials explain how to perform other common tasks in pandas:
How to Drop First Row in Pandas
How to Drop First Column in Pandas
How to Drop Duplicate Columns in Pandas
How to Drop All Columns Except Specific Ones in Pandas