Column Removal Methods¶

CRM-1: del statement¶

del df['A']

In-place removal of a column using the Python del statement.

Not Supported

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

Returns a new DataFrame with the specified column removed.

Not Supported

df = df.drop(columns=['A', 'B'])

More explicit way to drop columns using the columns parameter.

Not Supported

df = df.drop(['A','B'], axis=1)

Removes multiple columns at once using a list of column names.

Not Supported

removed = df.pop('A')

Removes a column from the DataFrame and returns it as a Series.

Not Supported

df = df.assign(col=None)

Indirect method of column removal by assigning None (not common).

Not Supported

df = df[['A', 'B']]

Keeps only the specified columns, effectively removing all others.

Not Supported

df = df.loc[:, ['A', 'B']]

Label-based selection that keeps only the specified columns.

Not Supported

df = df.iloc[:, [0, 1]]

Position-based selection that keeps only columns at specified indices.

Not Supported

df = df.loc[:, ~df.columns.str.startswith('temp')]

Uses boolean masking to remove columns based on conditions.

Not Supported

df = df.filter(regex='^[AB]')

Keeps columns that match certain patterns, removing all others.

Not Supported

df = df.T.drop_duplicates().T

Can indirectly remove columns when applied after transposing.

Not Supported

df = df.reindex(columns=['A','B'])

Keeps only specified columns and reorders them, removing all others.

Not Supported