xorbits.pandas.Index.isin#

Index.isin(values, level=None) → npt.NDArray[np.bool_][source]#

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

NumPy array of boolean values.

Return type

np.ndarray[bool]

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  
Index([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])

Warning

This method has not been implemented yet. Xorbits will try to execute it with pandas.

This docstring was copied from pandas.core.indexes.base.Index.