parameters: (self, dropna=True)
return_statement: return Series(count, index=CategoricalIndex(ix), dtype="int64")
docstring: Return a Series containing counts of each category.
  
          Every category will have an entry, even those with a count of 0.
  
          Parameters
          ----------
          dropna : bool, default True
              Don't include counts of NaN.
  
          Returns
          -------
          counts : Series
  
          See Also
          --------
          Series.value_counts
docstring_summary: Return a Series containing counts of each category.
docstring_tokens: Return a Series containing counts of each category .
function: def value_counts(self, dropna=True):
          """
          Return a Series containing counts of each category.
  
          Every category will have an entry, even those with a count of 0.
  
          Parameters
          ----------
          dropna : bool, default True
              Don't include counts of NaN.
  
          Returns
          -------
          counts : Series
  
          See Also
          --------
          Series.value_counts
          """
          from pandas import Series, CategoricalIndex
  
          code, cat = self._codes, self.categories
          ncat, mask = len(cat), 0 <= code
          ix, clean = np.arange(ncat), mask.all()
  
          if dropna or clean:
              obs = code if clean else code[mask]
              count = np.bincount(obs, minlength=ncat or 0)
          else:
              count = np.bincount(np.where(mask, code, ncat))
              ix = np.append(ix, -1)
  
          ix = self._constructor(ix, dtype=self.dtype, fastpath=True)
  
          return Series(count, index=CategoricalIndex(ix), dtype="int64")
function_tokens: def value_counts ( self , dropna = True ) : from pandas import Series , CategoricalIndex code , cat = self . _codes , self . categories ncat , mask = len ( cat ) , 0 <= code ix , clean = np . arange ( ncat ) , mask . all ( ) if dropna or clean : obs = code if clean else code [ mask ] count = np . bincount ( obs , minlength = ncat or 0 ) else : count = np . bincount ( np . where ( mask , code , ncat ) ) ix = np . append ( ix , - 1 ) ix = self . _constructor ( ix , dtype = self . dtype , fastpath = True ) return Series ( count , index = CategoricalIndex ( ix ) , dtype = "int64" )