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Excluding columns in Pandas is easy to do. You can do it by either selecting the column names you want to exclude as a list, or by indexing the columns you want to keep in the dataframe. You can also use the drop() method and specify the column names you want to exclude. All of these methods provide a quick and efficient way to select the columns you’re interested in.
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.