def count(self, axis: Optional[Axis]=None, numeric_only: bool=False) -> Union[(Scalar, 'Series')]:
    '\n        Count non-NA cells for each column.\n\n        The values `None`, `NaN` are considered NA.\n\n        Parameters\n        ----------\n        axis : {0 or ‘index’, 1 or ‘columns’}, default 0\n            If 0 or ‘index’ counts are generated for each column. If 1 or ‘columns’ counts are\n            generated for each row.\n        numeric_only : bool, default False\n            If True, include only float, int, boolean columns. This parameter is mainly for\n            pandas compatibility.\n\n        Returns\n        -------\n        max : scalar for a Series, and a Series for a DataFrame.\n\n        See Also\n        --------\n        DataFrame.shape: Number of DataFrame rows and columns (including NA\n            elements).\n        DataFrame.isna: Boolean same-sized DataFrame showing places of NA\n            elements.\n\n        Examples\n        --------\n        Constructing DataFrame from a dictionary:\n\n        >>> df = ps.DataFrame({"Person":\n        ...                    ["John", "Myla", "Lewis", "John", "Myla"],\n        ...                    "Age": [24., np.nan, 21., 33, 26],\n        ...                    "Single": [False, True, True, True, False]},\n        ...                   columns=["Person", "Age", "Single"])\n        >>> df\n          Person   Age  Single\n        0   John  24.0   False\n        1   Myla   NaN    True\n        2  Lewis  21.0    True\n        3   John  33.0    True\n        4   Myla  26.0   False\n\n        Notice the uncounted NA values:\n\n        >>> df.count()\n        Person    5\n        Age       4\n        Single    5\n        dtype: int64\n\n        >>> df.count(axis=1)\n        0    3\n        1    2\n        2    3\n        3    3\n        4    3\n        dtype: int64\n\n        On a Series:\n\n        >>> df[\'Person\'].count()\n        5\n\n        >>> df[\'Age\'].count()\n        4\n        '
    return self._reduce_for_stat_function(Frame._count_expr, name='count', axis=axis, numeric_only=numeric_only)
Count non-NA cells for each column.

The values `None`, `NaN` are considered NA.

Parameters
----------
axis : {0 or ‘index’, 1 or ‘columns’}, default 0
    If 0 or ‘index’ counts are generated for each column. If 1 or ‘columns’ counts are
    generated for each row.
numeric_only : bool, default False
    If True, include only float, int, boolean columns. This parameter is mainly for
    pandas compatibility.

Returns
-------
max : scalar for a Series, and a Series for a DataFrame.

See Also
--------
DataFrame.shape: Number of DataFrame rows and columns (including NA
    elements).
DataFrame.isna: Boolean same-sized DataFrame showing places of NA
    elements.

Examples
--------
Constructing DataFrame from a dictionary:

>>> df = ps.DataFrame({"Person":
...                    ["John", "Myla", "Lewis", "John", "Myla"],
...                    "Age": [24., np.nan, 21., 33, 26],
...                    "Single": [False, True, True, True, False]},
...                   columns=["Person", "Age", "Single"])
>>> df
  Person   Age  Single
0   John  24.0   False
1   Myla   NaN    True
2  Lewis  21.0    True
3   John  33.0    True
4   Myla  26.0   False

Notice the uncounted NA values:

>>> df.count()
Person    5
Age       4
Single    5
dtype: int64

>>> df.count(axis=1)
0    3
1    2
2    3
3    3
4    3
dtype: int64

On a Series:

>>> df['Person'].count()
5

>>> df['Age'].count()
4
def count(self, axis: Optional[Axis]=None, numeric_only: bool=False) -> Union[(Scalar, 'Series')]:
    '\n        Count non-NA cells for each column.\n\n        The values `None`, `NaN` are considered NA.\n\n        Parameters\n        ----------\n        axis : {0 or ‘index’, 1 or ‘columns’}, default 0\n            If 0 or ‘index’ counts are generated for each column. If 1 or ‘columns’ counts are\n            generated for each row.\n        numeric_only : bool, default False\n            If True, include only float, int, boolean columns. This parameter is mainly for\n            pandas compatibility.\n\n        Returns\n        -------\n        max : scalar for a Series, and a Series for a DataFrame.\n\n        See Also\n        --------\n        DataFrame.shape: Number of DataFrame rows and columns (including NA\n            elements).\n        DataFrame.isna: Boolean same-sized DataFrame showing places of NA\n            elements.\n\n        Examples\n        --------\n        Constructing DataFrame from a dictionary:\n\n        >>> df = ps.DataFrame({"Person":\n        ...                    ["John", "Myla", "Lewis", "John", "Myla"],\n        ...                    "Age": [24., np.nan, 21., 33, 26],\n        ...                    "Single": [False, True, True, True, False]},\n        ...                   columns=["Person", "Age", "Single"])\n        >>> df\n          Person   Age  Single\n        0   John  24.0   False\n        1   Myla   NaN    True\n        2  Lewis  21.0    True\n        3   John  33.0    True\n        4   Myla  26.0   False\n\n        Notice the uncounted NA values:\n\n        >>> df.count()\n        Person    5\n        Age       4\n        Single    5\n        dtype: int64\n\n        >>> df.count(axis=1)\n        0    3\n        1    2\n        2    3\n        3    3\n        4    3\n        dtype: int64\n\n        On a Series:\n\n        >>> df[\'Person\'].count()\n        5\n\n        >>> df[\'Age\'].count()\n        4\n        '
    return self._reduce_for_stat_function(Frame._count_expr, name='count', axis=axis, numeric_only=numeric_only)