Classe « MultiIndex »

Méthode pandas.MultiIndex.isin

Signature de la méthode isin

def isin(self, values, level=None) 

Description

isin.__doc__

Return a boolean array where the index values are in `values`.

Compute boolean array of whether each index value is found in the
passed set of values. The length of the returned boolean array matches
the length of the index.

Parameters
----------
values : set or list-like
    Sought values.
level : str or int, optional
    Name or position of the index level to use (if the index is a
    `MultiIndex`).

Returns
-------
is_contained : ndarray
    NumPy array of boolean values.

See Also
--------
Series.isin : Same for Series.
DataFrame.isin : Same method for DataFrames.

Notes
-----
In the case of `MultiIndex` you must either specify `values` as a
list-like object containing tuples that are the same length as the
number of levels, or specify `level`. Otherwise it will raise a
``ValueError``.

If `level` is specified:

- if it is the name of one *and only one* index level, use that level;
- otherwise it should be a number indicating level position.

Examples
--------
>>> idx = pd.Index([1,2,3])
>>> idx
Int64Index([1, 2, 3], dtype='int64')

Check whether each index value in a list of values.

>>> idx.isin([1, 4])
array([ True, False, False])

>>> midx = pd.MultiIndex.from_arrays([[1,2,3],
...                                  ['red', 'blue', 'green']],
...                                  names=('number', 'color'))
>>> midx
MultiIndex([(1,   'red'),
            (2,  'blue'),
            (3, 'green')],
           names=['number', 'color'])

Check whether the strings in the 'color' level of the MultiIndex
are in a list of colors.

>>> midx.isin(['red', 'orange', 'yellow'], level='color')
array([ True, False, False])

To check across the levels of a MultiIndex, pass a list of tuples:

>>> midx.isin([(1, 'red'), (3, 'red')])
array([ True, False, False])

For a DatetimeIndex, string values in `values` are converted to
Timestamps.

>>> dates = ['2000-03-11', '2000-03-12', '2000-03-13']
>>> dti = pd.to_datetime(dates)
>>> dti
DatetimeIndex(['2000-03-11', '2000-03-12', '2000-03-13'],
dtype='datetime64[ns]', freq=None)

>>> dti.isin(['2000-03-11'])
array([ True, False, False])