>>> lst = ['a', 'a', 'b']  
>>> ser = pd.Series([1, 2, np.nan], index=lst)  
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"])
        a         b     c
cow     1       NaN     3
horse   1       NaN     6
bull    7       8.0     9
>>> df.groupby("a").count()  
    b   c
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']))
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