The best way to do this in Pandas is to use drop:
df = df.drop('column_name', axis=1)
where 1 is the axis number (0 for rows and 1 for columns.)
To delete the column without having to reassign df you can do:
df.drop('column_name', axis=1, inplace=True)
Finally, to drop by column number instead of by column label, try this to delete, e.g. the 1st, 2nd and 4th columns:
df = df.drop(df.columns[[0, 1, 3]], axis=1) # df.columns is zero-based pd.Index
Also working with “text” syntax for the columns:
df.drop(['column_nameA', 'column_nameB'], axis=1, inplace=True)
Note: Introduced in v0.21.0 (October 27, 2017), the drop() method accepts index/columns keywords as an alternative to specifying the axis.
So we can now just do:
df = df.drop(columns=['column_nameA', 'column_nameB'])