modin/pandas/series.py
def value_counts(
        self, normalize=False, sort=True, ascending=False, bins=None, dropna=True
    ):  # noqa: PR01, RT01, D200
        """
        Return a Series containing counts of unique values.
        """
        if bins is not None:
            # Potentially we could implement `cut` function from pandas API, which
            # bins values into intervals, and then we can just count them as regular values.
            # TODO #1333: new_self = Series(pd.cut(self, bins, include_lowest=True), dtype="interval")
            return self._default_to_pandas(
                pandas.Series.value_counts,
                normalize=normalize,
                sort=sort,
                ascending=ascending,
                bins=bins,
                dropna=dropna,
            )
        counted_values = super(Series, self).value_counts(
            subset=self,
            normalize=normalize,
            sort=sort,
            ascending=ascending,
            dropna=dropna,
        )
        # pandas sets output index names to None because the Series name already contains it
        counted_values._query_compiler.set_index_name(None)
        return counted_values

        Return a Series containing counts of unique values.
        
Return a Series containing counts of unique values.
Return a Series containing counts of unique values .
def value_counts(
        self, normalize=False, sort=True, ascending=False, bins=None, dropna=True
    ):  
        if bins is not None:
            return self._default_to_pandas(
                pandas.Series.value_counts,
                normalize=normalize,
                sort=sort,
                ascending=ascending,
                bins=bins,
                dropna=dropna,
            )
        counted_values = super(Series, self).value_counts(
            subset=self,
            normalize=normalize,
            sort=sort,
            ascending=ascending,
            dropna=dropna,
        )
        counted_values._query_compiler.set_index_name(None)
        return counted_values
def value_counts ( self , normalize = False , sort = True , ascending = False , bins = None , dropna = True ) : if bins is not None : return self . _default_to_pandas ( pandas . Series . value_counts , normalize = normalize , sort = sort , ascending = ascending , bins = bins , dropna = dropna , ) counted_values = super ( Series , self ) . value_counts ( subset = self , normalize = normalize , sort = sort , ascending = ascending , dropna = dropna , ) counted_values . _query_compiler . set_index_name ( None ) return counted_values
Return a Series containing counts of unique values.
Return a Series containing counts of unique values .
# noqa: PR01, RT01, D200 """
        Return a Series containing counts of unique values.
        """ # Potentially we could implement `cut` function from pandas API, which # bins values into intervals, and then we can just count them as regular values. # TODO #1333: new_self = Series(pd.cut(self, bins, include_lowest=True), dtype="interval") # pandas sets output index names to None because the Series name already contains it
self
normalize
sort
ascending
bins
dropna