Drop multiple columns in pandas

Drop multiple columns in pandas

You can drop multiple columns from a Pandas DataFrame using the drop() method. You need to specify the column names you want to drop and set the axis parameter to 1 to indicate that you're dropping columns (as opposed to rows). Here's how you can do it:

import pandas as pd # Sample DataFrame data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]} df = pd.DataFrame(data) # Columns to drop columns_to_drop = ['A', 'C'] # Drop the specified columns df_dropped = df.drop(columns=columns_to_drop) print(df_dropped)

In this example, the columns_to_drop list contains the names of the columns you want to drop. The drop() method is then used with the columns parameter set to columns_to_drop. This will create a new DataFrame with the specified columns removed.

If you want to modify the original DataFrame in place without creating a new DataFrame, you can set the inplace parameter to True:

df.drop(columns=columns_to_drop, inplace=True)

Both approaches will achieve the same result: dropping the specified columns from the DataFrame.

Examples

  1. How to drop multiple columns in pandas DataFrame?

    Description: This query seeks a method to drop multiple columns from a pandas DataFrame efficiently.

    import pandas as pd # Assuming df is your DataFrame and ['col1', 'col2'] are the columns to drop df.drop(['col1', 'col2'], axis=1, inplace=True)

  2. Drop columns by index in pandas DataFrame?

    Description: This query focuses on dropping multiple columns from a pandas DataFrame using column indices.

    import pandas as pd # Assuming df is your DataFrame and [0, 1] are the indices of columns to drop df.drop(df.columns[[0, 1]], axis=1, inplace=True)

  3. How to remove several columns in pandas DataFrame at once?

    Description: This query addresses the need to remove multiple columns simultaneously from a pandas DataFrame.

    import pandas as pd # Assuming df is your DataFrame and ['col1', 'col2'] are the columns to drop df.drop(columns=['col1', 'col2'], inplace=True)

  4. Drop columns with specific names in pandas DataFrame?

    Description: This query seeks to drop multiple columns from a pandas DataFrame based on specific column names.

    import pandas as pd # Assuming df is your DataFrame and ['col1', 'col2'] are the columns to drop df.drop(columns=['col1', 'col2'], inplace=True)

  5. How to drop multiple columns except specific ones in pandas DataFrame?

    Description: This query addresses the requirement to drop multiple columns from a pandas DataFrame except for specific ones.

    import pandas as pd # Assuming df is your DataFrame and ['col1', 'col2'] are the columns to preserve df.drop(df.columns.difference(['col1', 'col2']), axis=1, inplace=True)

  6. Drop columns based on conditions in pandas DataFrame?

    Description: This query focuses on dropping multiple columns from a pandas DataFrame based on specified conditions.

    import pandas as pd # Assuming df is your DataFrame and ['col1', 'col2'] are the columns to drop based on conditions df.drop(columns=[col for col in df.columns if condition], inplace=True)

  7. How to remove multiple columns with NaN values in pandas DataFrame?

    Description: This query seeks a method to remove multiple columns from a pandas DataFrame that contain NaN (missing) values.

    import pandas as pd # Assuming df is your DataFrame df.dropna(axis=1, inplace=True)

  8. Drop columns by data type in pandas DataFrame?

    Description: This query addresses the need to drop multiple columns from a pandas DataFrame based on their data types.

    import pandas as pd # Assuming df is your DataFrame and ['float64', 'int64'] are the data types of columns to drop df.drop(df.select_dtypes(['float64', 'int64']), axis=1, inplace=True)

  9. Remove multiple columns with specific prefixes in pandas DataFrame?

    Description: This query focuses on removing multiple columns from a pandas DataFrame that have specific prefixes in their names.

    import pandas as pd # Assuming df is your DataFrame and ['prefix1', 'prefix2'] are the prefixes of columns to drop df.drop(df.filter(like='prefix').columns, axis=1, inplace=True)

  10. How to drop columns with low variance in pandas DataFrame?

    Description: This query seeks a method to drop multiple columns from a pandas DataFrame with low variance.

    import pandas as pd from sklearn.feature_selection import VarianceThreshold # Assuming df is your DataFrame and threshold is the variance threshold selector = VarianceThreshold(threshold=threshold) df_selected = selector.fit_transform(df)

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