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You can use the following syntax to exclude columns in a pandas DataFrame:
#exclude column1 df.loc[:, df.columns!='column1'] #exclude column1, column2, ... df.loc[:, ~df.columns.isin(['column1', 'column2', ...])]
The following examples show how to use this syntax in practice.
Example 1: Exclude One Column
The following code shows how to select all columns except one in a pandas DataFrame:
import pandas as pd #create DataFrame df = pd.DataFrame({'points': [25, 12, 15, 14, 19, 23, 25, 29], 'assists': [5, 7, 7, 9, 12, 9, 9, 4], 'rebounds': [11, 8, 10, 6, 6, 5, 9, 12], 'blocks': [2, 3, 3, 5, 3, 2, 1, 2]}) #view DataFrame df points assists rebounds blocks 0 25 5 11 2 1 12 7 8 3 2 15 7 10 3 3 14 9 6 5 4 19 12 6 3 5 23 9 5 2 6 25 9 9 1 7 29 4 12 2 #select all columns except 'rebounds' df.loc[:, df.columns!='rebounds'] points assists blocks 0 25 5 2 1 12 7 3 2 15 7 3 3 14 9 5 4 19 12 3 5 23 9 2 6 25 9 1 7 29 4 2
Example 2: Exclude Multiple Columns
The following code shows how to select all columns except specific ones in a pandas DataFrame:
import pandas as pd #create DataFrame df = pd.DataFrame({'points': [25, 12, 15, 14, 19, 23, 25, 29], 'assists': [5, 7, 7, 9, 12, 9, 9, 4], 'rebounds': [11, 8, 10, 6, 6, 5, 9, 12], 'blocks': [2, 3, 3, 5, 3, 2, 1, 2]}) #view DataFrame df points assists rebounds blocks 0 25 5 11 2 1 12 7 8 3 2 15 7 10 3 3 14 9 6 5 4 19 12 6 3 5 23 9 5 2 6 25 9 9 1 7 29 4 12 2 #select all columns except 'rebounds' and 'assists' df.loc[:, ~df.columns.isin(['rebounds', 'assists'])] points blocks 0 25 2 1 12 3 2 15 3 3 14 5 4 19 3 5 23 2 6 25 1 7 29 2
Using this syntax, you can exclude any number of columns that you’d like by name.
Additional Resources
How to Add Rows to a Pandas DataFrame
How to Add a Numpy Array to a Pandas DataFrame
How to Count Number of Rows in Pandas DataFrame