def value_counts(self, dropna=True):
"""
Returns a Series containing counts of unique values.
Parameters
----------
dropna : boolean, default True
Don't include counts of NaN, even if NaN is in sp_values.
Returns
-------
counts : Series
"""
from pandas import Index, Series
keys, counts = algos.value_counts_arraylike(self.sp_values,
dropna=dropna)
fcounts = self.sp_index.ngaps
if fcounts > 0 and (not self._null_fill_value or not dropna):
mask = isna(
keys) if self._null_fill_value else keys == self.fill_value
if mask.any():
counts[mask] += fcounts
else:
keys = np.insert(keys, 0, self.fill_value)
counts = np.insert(counts, 0, fcounts)
if not isinstance(keys, ABCIndexClass):
keys = Index(keys)
return Series(counts, index=keys)
def value_counts(self, dropna: bool = True) -> Series:
"""
Returns a Series containing counts of each unique value.
Parameters
----------
dropna : bool, default True
Don't include counts of missing values.
Returns
-------
counts : Series
See Also
--------
Series.value_counts
"""
from pandas import (
Index,
Series,
)
from pandas.arrays import IntegerArray
if dropna:
keys, counts = algos.value_counts_arraylike(self._data,
dropna=True,
mask=self._mask)
res = Series(counts, index=keys)
res.index = res.index.astype(self.dtype)
res = res.astype("Int64")
return res
# compute counts on the data with no nans
data = self._data[~self._mask]
value_counts = Index(data).value_counts()
index = value_counts.index
# if we want nans, count the mask
if dropna:
counts = value_counts._values
else:
counts = np.empty(len(value_counts) + 1, dtype="int64")
counts[:-1] = value_counts
counts[-1] = self._mask.sum()
index = index.insert(len(index), self.dtype.na_value)
index = index.astype(self.dtype)
mask = np.zeros(len(counts), dtype="bool")
counts = IntegerArray(counts, mask)
return Series(counts, index=index)
