How to Safely Delete Columns from a Pandas DataFrame?

Deleting a Column from a Pandas DataFrame

While using the del keyword on the DataFrame itself (del df.column_name) may seem intuitive, it is not the recommended method for deleting columns in Pandas. Unexpected errors can arise because the del keyword removes the entire column from the DataFrame object, not just its values.

Preferred Approach: Using the drop() Method

The proper way to remove a column from a DataFrame is through the drop() method. It allows for precise targeting and control over the deletion process. The general syntax is:

df = df.drop('column_name', axis=1)

where 1 represents the axis number for columns (0 is for rows). This approach ensures that only the specified column is deleted, leaving the remaining data intact.

Alternative Syntax: Using the columns Keyword

An alternative syntax for drop() is to use the columns keyword:

df = df.drop(columns=['column_nameA', 'column_nameB'])

This method is particularly useful when deleting multiple columns.

In-place Modification

If you wish to modify the original DataFrame inplace without reassignment, use:

df.drop('column_name', axis=1, inplace=True)

Dropping by Column Number

To delete columns by their position (number) instead of label, use:

df = df.drop(df.columns[[0, 1, 3]], axis=1)  # df.columns is zero-based pd.Index

Using Text Syntax

Similar to the columns keyword, you can also use text syntax to specify the columns to be dropped:

df.drop(['column_nameA', 'column_nameB'], axis=1, inplace=True)

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