Filtering multiple items in a multi-index Python Panda dataframe

Question

I have the following table:

Note: Both NSRCODE and PBL_AWI are index\'s

Note: the % Of area column would be filled out just have not done so yet.

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Answer 1

You can get_level_values in conjunction with Boolean slicing.

In [50]:

print df[np.in1d(df.index.get_level_values(1), ['Lake', 'River', 'Upland'])]
                          Area
NSRCODE PBL_AWI               
CM      Lake      57124.819333
        River      1603.906642
LBH     Lake     258046.508310
        River     44262.807900

The same idea can be expressed in many different ways, such as df[df.index.get_level_values('PBL_AWI').isin(['Lake', 'River', 'Upland'])]

Note that you have 'upland' in your data instead of 'Upland'

Answer 2

This is an answer to a slight variant of the question asked that might save someone else a little time. If you are looking for a wildcard type match to a label whose exact value you don't know, you can use something like this:

q_labels = [ label for label in df.index.levels[1] if label.startswith('Q') ]
new_df = df[ df.index.isin(q_labels, level=1) ]

Answer 3

Also (from here):

def filter_by(df, constraints):
    """Filter MultiIndex by sublevels."""
    indexer = [constraints[name] if name in constraints else slice(None)
               for name in df.index.names]
    return df.loc[tuple(indexer)] if len(df.shape) == 1 else df.loc[tuple(indexer),]

pd.Series.filter_by = filter_by
pd.DataFrame.filter_by = filter_by

... to be used as

df.filter_by({'PBL_AWI' : ['Lake', 'River', 'Upland']})

(untested with Panels and higher dimension elements, but I do expect it to work)

Answer 4

Another (maybe cleaner) way might be this one:

print(df[df.index.isin(['Lake', 'River', 'Upland'], level=1)])

The parameter level specifies the index number (starting with 0) or index name (here: level='PBL_AWI')