pandas/core/base.py
def value_counts(self, normalize=False, sort=True, ascending=False,
                     bins=None, dropna=True):
        """
        Returns object containing counts of unique values.

        The resulting object will be in descending order so that the
        first element is the most frequently-occurring element.
        Excludes NA values by default.

        Parameters
        ----------
        normalize : boolean, default False
            If True then the object returned will contain the relative
            frequencies of the unique values.
        sort : boolean, default True
            Sort by values
        ascending : boolean, default False
            Sort in ascending order
        bins : integer, optional
            Rather than count values, group them into half-open bins,
            a convenience for pd.cut, only works with numeric data
        dropna : boolean, default True
            Don't include counts of NaN.

        Returns
        -------
        counts : Series
        """
        from pandas.core.algorithms import value_counts
        from pandas.tseries.api import DatetimeIndex, PeriodIndex
        result = value_counts(self, sort=sort, ascending=ascending,
                              normalize=normalize, bins=bins, dropna=dropna)

        if isinstance(self, PeriodIndex):
            # preserve freq
            result.index = self._simple_new(result.index.values, self.name,
                                            freq=self.freq)
        elif isinstance(self, DatetimeIndex):
            result.index = self._simple_new(result.index.values, self.name,
                                            tz=getattr(self, 'tz', None))
        return result

        Returns object containing counts of unique values.

        The resulting object will be in descending order so that the
        first element is the most frequently-occurring element.
        Excludes NA values by default.

        Parameters
        ----------
        normalize : boolean, default False
            If True then the object returned will contain the relative
            frequencies of the unique values.
        sort : boolean, default True
            Sort by values
        ascending : boolean, default False
            Sort in ascending order
        bins : integer, optional
            Rather than count values, group them into half-open bins,
            a convenience for pd.cut, only works with numeric data
        dropna : boolean, default True
            Don't include counts of NaN.

        Returns
        -------
        counts : Series
        
Returns object containing counts of unique values.
The resulting object will be in descending order so that the
first element is the most frequently-occurring element.
Excludes NA values by default.

Parameters

normalize : boolean, default False
If True then the object returned will contain the relative
frequencies of the unique values.
sort : boolean, default True
Sort by values
ascending : boolean, default False
Sort in ascending order
bins : integer, optional
Rather than count values, group them into half-open bins,
a convenience for pd.cut, only works with numeric data
dropna : boolean, default True
Don't include counts of NaN.

Returns

counts : Series
Returns object containing counts of unique values . The resulting object will be in descending order so that the first element is the most frequently - occurring element . Excludes NA values by default . Parameters normalize : boolean default False If True then the object returned will contain the relative frequencies of the unique values . sort : boolean default True Sort by values ascending : boolean default False Sort in ascending order bins : integer optional Rather than count values group them into half - open bins a convenience for pd . cut only works with numeric data dropna : boolean default True Don ' t include counts of NaN . Returns counts : Series
def value_counts(self, normalize=False, sort=True, ascending=False,
                     bins=None, dropna=True):
        from pandas.core.algorithms import value_counts
        from pandas.tseries.api import DatetimeIndex, PeriodIndex
        result = value_counts(self, sort=sort, ascending=ascending,
                              normalize=normalize, bins=bins, dropna=dropna)
        if isinstance(self, PeriodIndex):
            result.index = self._simple_new(result.index.values, self.name,
                                            freq=self.freq)
        elif isinstance(self, DatetimeIndex):
            result.index = self._simple_new(result.index.values, self.name,
                                            tz=getattr(self, 'tz', None))
        return result
def value_counts ( self , normalize = False , sort = True , ascending = False , bins = None , dropna = True ) : from pandas . core . algorithms import value_counts from pandas . tseries . api import DatetimeIndex , PeriodIndex result = value_counts ( self , sort = sort , ascending = ascending , normalize = normalize , bins = bins , dropna = dropna ) if isinstance ( self , PeriodIndex ) : result . index = self . _simple_new ( result . index . values , self . name , freq = self . freq ) elif isinstance ( self , DatetimeIndex ) : result . index = self . _simple_new ( result . index . values , self . name , tz = getattr ( self , 'tz' , None ) ) return result
Returns object containing counts of unique values.
Returns object containing counts of unique values .
"""
        Returns object containing counts of unique values.

        The resulting object will be in descending order so that the
        first element is the most frequently-occurring element.
        Excludes NA values by default.

        Parameters
        ----------
        normalize : boolean, default False
            If True then the object returned will contain the relative
            frequencies of the unique values.
        sort : boolean, default True
            Sort by values
        ascending : boolean, default False
            Sort in ascending order
        bins : integer, optional
            Rather than count values, group them into half-open bins,
            a convenience for pd.cut, only works with numeric data
        dropna : boolean, default True
            Don't include counts of NaN.

        Returns
        -------
        counts : Series
        """ # preserve freq
self
normalize
sort
ascending
bins
dropna