Drop specific rows from multiindex Dataframe

Question

I have a multi-index dataframe that looks like this:

 start      grad
 1995-96    1995-96 15  15  
            1996-97 6   6   
            2002-03 1   1   
            2007-08 1   1

I'd like to drop by the specific values for the first level (level=0). In this case, I'd like to drop everything that has 1995-96 in the first index.

there are better ways, but: df.select(lambda row: row[0] != '1995-96', axis=0) — Paul H
– Paul H, Commented Aug 30, 2016 at 23:43

Paul H · Accepted Answer · 2018-02-27 17:56:08Z

32

pandas.DataFrame.drop takes level as an optional argument

df.drop('1995-96', level='start')

As of v0.18.1, its docstring says:

"""
Signature: df.drop(labels, axis=0, level=None, inplace=False, errors='raise') 
Docstring: Return new object with labels in requested axis removed.

    Parameters
    ----------
    labels : single label or list-like
    axis : int or axis name
    level : int or level name, default None
        For MultiIndex
    inplace : bool, default False
        If True, do operation inplace and return None.
    errors : {'ignore', 'raise'}, default 'raise'
        If 'ignore', suppress error and existing labels are dropped.

    .. versionadded:: 0.16.1
"""

edited Feb 27, 2018 at 17:56

answered Aug 30, 2016 at 23:46

Paul H

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2 Comments

dreme Over a year ago

If the first index had not been named as 'start', you could use level=0

misantroop Over a year ago

Always best to use named index levels due to possible reordering.

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