pyspark.pandas.MultiIndex.symmetric_difference#
- MultiIndex.symmetric_difference(other, result_name=None, sort=None)[source]#
Compute the symmetric difference of two MultiIndex objects.
- Parameters
- otherIndex or array-like
- result_namelist
- sortTrue or None, default None
Whether to sort the resulting index. * True : Attempt to sort the result. * None : Do not sort the result.
- Returns
- symmetric_differenceMultiIndex
Notes
symmetric_difference
contains elements that appear in eitheridx1
oridx2
but not both. Equivalent to the Index created byidx1.difference(idx2) | idx2.difference(idx1)
with duplicates dropped.Examples
>>> midx1 = pd.MultiIndex([['lama', 'cow', 'falcon'], ... ['speed', 'weight', 'length']], ... [[0, 0, 0, 1, 1, 1, 2, 2, 2], ... [0, 0, 0, 0, 1, 2, 0, 1, 2]]) >>> midx2 = pd.MultiIndex([['pandas-on-Spark', 'cow', 'falcon'], ... ['speed', 'weight', 'length']], ... [[0, 0, 0, 1, 1, 1, 2, 2, 2], ... [0, 0, 0, 0, 1, 2, 0, 1, 2]]) >>> s1 = ps.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3], ... index=midx1) >>> s2 = ps.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3], ... index=midx2)
>>> s1.index.symmetric_difference(s2.index) MultiIndex([('pandas-on-Spark', 'speed'), ( 'lama', 'speed')], )
You can set names of the result Index.
>>> s1.index.symmetric_difference(s2.index, result_name=['a', 'b']) MultiIndex([('pandas-on-Spark', 'speed'), ( 'lama', 'speed')], names=['a', 'b'])
You can set sort to True, if you want to sort the resulting index.
>>> s1.index.symmetric_difference(s2.index, sort=True) MultiIndex([('pandas-on-Spark', 'speed'), ( 'lama', 'speed')], )
You can also use the
^
operator:>>> s1.index ^ s2.index MultiIndex([('pandas-on-Spark', 'speed'), ( 'lama', 'speed')], )