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# pandas.api.typing.SeriesGroupBy.count

SeriesGroupBy.count()

  Compute count of group, excluding missing values.

  Returns:
    Series or DataFrame

      Count of values within each group.

  See also

  `Series.count`

    Apply function count to a Series.

  `DataFrame.count`

    Apply function count to each row or column of a DataFrame.

  Examples

  For SeriesGroupBy:

      >>> lst = ["a", "a", "b"]
      >>> ser = pd.Series([1, 2, np.nan], index=lst)
      >>> ser
      a    1.0
      a    2.0
      b    NaN
      dtype: float64
      >>> ser.groupby(level=0).count()
      a    2
      b    0
      dtype: int64

  For DataFrameGroupBy:

      >>> data = [[1, np.nan, 3], [1, np.nan, 6], [7, 8, 9]]
      >>> df = pd.DataFrame(
      ...     data, columns=["a", "b", "c"], index=["cow", "horse", "bull"]
      ... )
      >>> df
              a         b     c
      cow     1       NaN     3
      horse   1       NaN     6
      bull    7       8.0     9
      >>> df.groupby("a").count()
          b   c
      a
      1   0   2
      7   1   1

  For Resampler:

      >>> ser = pd.Series(
      ...     [1, 2, 3, 4],
      ...     index=pd.DatetimeIndex(
      ...         ["2023-01-01", "2023-01-15", "2023-02-01", "2023-02-15"]
      ...     ),
      ... )
      >>> ser
      2023-01-01    1
      2023-01-15    2
      2023-02-01    3
      2023-02-15    4
      dtype: int64
      >>> ser.resample("MS").count()
      2023-01-01    2
      2023-02-01    2
      Freq: MS, dtype: int64