pandas.Index.isin#

Index.isin(values,level=None)[source]#

Return a boolean array where the index values are invalues.

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

Parameters:

valuesset or list-like

Sought values.

levelstr or int, optional

Name or position of the index level to use (if the index is aMultiIndex).

Returns:

np.ndarray[bool]

NumPy array of boolean values.

See also

Series.isin

Same for Series.

DataFrame.isin

Same method for DataFrames.

Notes

In the case ofMultiIndex you must either specifyvalues as alist-like object containing tuples that are the same length as thenumber of levels, or specifylevel. Otherwise it will raise aValueError.

Iflevel is specified:

• if it is the name of oneand only one index level, use that level;

• otherwise it should be a number indicating level position.

Examples

>>>idx=pd.Index([1,2,3])>>>idxIndex([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'))>>>midxMultiIndex([(1,   'red'),            (2,  'blue'),            (3, 'green')],           names=['number', 'color'])

Check whether the strings in the ‘color’ level of the MultiIndexare 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])