pyspark.pandas.groupby.SeriesGroupBy.value_counts

SeriesGroupBy.value_counts(sort: Optional[bool] = None, ascending: Optional[bool] = None, dropna: bool = True) → pyspark.pandas.series.Series

Compute group sizes.

Parameters

sortboolean, default None

Sort by frequencies.

ascendingboolean, default False

Sort in ascending order.

dropnaboolean, default True

Don’t include counts of NaN.

See also

pyspark.pandas.Series.groupby

pyspark.pandas.DataFrame.groupby

Examples

>>> df = ps.DataFrame({'A': [1, 2, 2, 3, 3, 3],
...                    'B': [1, 1, 2, 3, 3, np.nan]},
...                   columns=['A', 'B'])
>>> df
   A    B
0  1  1.0
1  2  1.0
2  2  2.0
3  3  3.0
4  3  3.0
5  3  NaN

>>> df.groupby('A')['B'].value_counts().sort_index()  
A  B
1  1.0    1
2  1.0    1
   2.0    1
3  3.0    2
Name: B, dtype: int64

Don’t include counts of NaN when dropna is False.

>>> df.groupby('A')['B'].value_counts(
...   dropna=False).sort_index()  
A  B
1  1.0    1
2  1.0    1
   2.0    1
3  3.0    2
   NaN    1
Name: B, dtype: int64

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