 most frequently-occurring row.

        Examples
        --------
        >>> df = pd.DataFrame({'num_legs': [2, 4, 4, 6],
        ...                    'num_wings': [2, 0, 0, 0]},
        ...                   index=['falcon', 'dog', 'cat', 'ant'])
        >>> df
                num_legs  num_wings
        falcon         2          2
        dog            4          0
        cat            4          0
        ant            6          0

        >>> df.value_counts()
        num_legs  num_wings
        4         0            2
        2         2            1
        6         0            1
        Name: count, dtype: int64

        >>> df.value_counts(sort=False)
        num_legs  num_wings
        2         2            1
        4         0            2
        6         0            1
        Name: count, dtype: int64

        >>> df.value_counts(ascending=True)
        num_legs  num_wings
        2         2            1
        6         0            1
        4         0            2
        Name: count, dtype: int64

        >>> df.value_counts(normalize=True)
        num_legs  num_wings
        4         0            0.50
        2         2            0.25
        6         0            0.25
        Name: proportion, dtype: float64

        With `dropna` set to `False` we can also count rows with NA values.

        >>> df = pd.DataFrame({'first_name': ['John', 'Anne', 'John', 'Beth'],
        ...                    'middle_name': ['Smith', pd.NA, pd.NA, 'Louise']})
        >>> df
          first_name middle_name
        0       John       Smith
        1       Anne        <NA>
        2       John        <NA>
        3       Beth      Louise

        >>> df.value_counts()
        first_name  middle_name
        Beth        Louise         1
        John        Smith          1
        Name: count, dtype: int64

        >>> df.value_counts(dropna=False)
        first_name  middle_name
        Anne        NaN            1
        Beth        Louise         1
        John        Smith          1
                    NaN            1
        Name: count, dtype: int64

        >>> df.value_counts("first_name")
        first_name
        John    2
        Anne    1
        Beth    1
        Name: count, dtype: int64
        NZ
proportionrl