parameters: (
          self, normalize=False, sort=True, ascending=False, bins=None, dropna=True
      )
return_statement: return result
docstring: Return a Series 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 : bool, default False
              If True then the object returned will contain the relative
              frequencies of the unique values.
          sort : bool, default True
              Sort by frequencies.
          ascending : bool, default False
              Sort in ascending order.
          bins : int, optional
              Rather than count values, group them into half-open bins,
              a convenience for ``pd.cut``, only works with numeric data.
          dropna : bool, default True
              Don't include counts of NaN.
  
          Returns
          -------
          Series
  
          See Also
          --------
          Series.count: Number of non-NA elements in a Series.
          DataFrame.count: Number of non-NA elements in a DataFrame.
  
          Examples
          --------
          >>> index = pd.Index([3, 1, 2, 3, 4, np.nan])
          >>> index.value_counts()
          3.0    2
          4.0    1
          2.0    1
          1.0    1
          dtype: int64
  
          With `normalize` set to `True`, returns the relative frequency by
          dividing all values by the sum of values.
  
          >>> s = pd.Series([3, 1, 2, 3, 4, np.nan])
          >>> s.value_counts(normalize=True)
          3.0    0.4
          4.0    0.2
          2.0    0.2
          1.0    0.2
          dtype: float64
  
          **bins**
  
          Bins can be useful for going from a continuous variable to a
          categorical variable; instead of counting unique
          apparitions of values, divide the index in the specified
          number of half-open bins.
  
          >>> s.value_counts(bins=3)
          (2.0, 3.0]      2
          (0.996, 2.0]    2
          (3.0, 4.0]      1
          dtype: int64
  
          **dropna**
  
          With `dropna` set to `False` we can also see NaN index values.
  
          >>> s.value_counts(dropna=False)
          3.0    2
          NaN    1
          4.0    1
          2.0    1
          1.0    1
          dtype: int64
docstring_summary: Return a Series containing counts of unique values.
docstring_tokens: Return a Series containing counts of unique values .
function: def value_counts(
          self, normalize=False, sort=True, ascending=False, bins=None, dropna=True
      ):
          """
          Return a Series 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 : bool, default False
              If True then the object returned will contain the relative
              frequencies of the unique values.
          sort : bool, default True
              Sort by frequencies.
          ascending : bool, default False
              Sort in ascending order.
          bins : int, optional
              Rather than count values, group them into half-open bins,
              a convenience for ``pd.cut``, only works with numeric data.
          dropna : bool, default True
              Don't include counts of NaN.
  
          Returns
          -------
          Series
  
          See Also
          --------
          Series.count: Number of non-NA elements in a Series.
          DataFrame.count: Number of non-NA elements in a DataFrame.
  
          Examples
          --------
          >>> index = pd.Index([3, 1, 2, 3, 4, np.nan])
          >>> index.value_counts()
          3.0    2
          4.0    1
          2.0    1
          1.0    1
          dtype: int64
  
          With `normalize` set to `True`, returns the relative frequency by
          dividing all values by the sum of values.
  
          >>> s = pd.Series([3, 1, 2, 3, 4, np.nan])
          >>> s.value_counts(normalize=True)
          3.0    0.4
          4.0    0.2
          2.0    0.2
          1.0    0.2
          dtype: float64
  
          **bins**
  
          Bins can be useful for going from a continuous variable to a
          categorical variable; instead of counting unique
          apparitions of values, divide the index in the specified
          number of half-open bins.
  
          >>> s.value_counts(bins=3)
          (2.0, 3.0]      2
          (0.996, 2.0]    2
          (3.0, 4.0]      1
          dtype: int64
  
          **dropna**
  
          With `dropna` set to `False` we can also see NaN index values.
  
          >>> s.value_counts(dropna=False)
          3.0    2
          NaN    1
          4.0    1
          2.0    1
          1.0    1
          dtype: int64
          """
          result = value_counts(
              self,
              sort=sort,
              ascending=ascending,
              normalize=normalize,
              bins=bins,
              dropna=dropna,
          )
          return result
function_tokens: def value_counts ( self , normalize = False , sort = True , ascending = False , bins = None , dropna = True ) : result = value_counts ( self , sort = sort , ascending = ascending , normalize = normalize , bins = bins , dropna = dropna , ) return result