(df, axis=0, level=None, numeric_only=False)
return sdc_pandas_dataframe_reduce_columns(df, name, params, ser_par)
Intel Scalable Dataframe Compiler User Guide
    ********************************************

    Pandas API: pandas.DataFrame.count

    Limitations
    -----------
    Parameters ``axis``, ``level`` and ``numeric_only`` are unsupported.

    Examples
    --------
    .. literalinclude:: ../../../examples/dataframe/dataframe_count.py
       :language: python
       :lines: 33-
       :caption: Count non-NA cells for each column or row.
       :name: ex_dataframe_count

    .. command-output:: python ./dataframe/dataframe_count.py
       :cwd: ../../../examples

    .. seealso::
        :ref:`Series.count <pandas.Series.count>`
            Number of non-NA elements in a Series.
        :ref:`DataFrame.shape <pandas.DataFrame.shape>`
            Number of DataFrame rows and columns (including NA elements).
        :ref:`DataFrame.isna <pandas.DataFrame.isna>`
            Boolean same-sized DataFrame showing places of NA elements.

    Intel Scalable Dataframe Compiler Developer Guide
    *************************************************

    Pandas DataFrame method :meth:`pandas.DataFrame.count` implementation.

        .. only:: developer

            Test: python -m sdc.runtests -k sdc.tests.test_dataframe.TestDataFrame.test_count*
Intel Scalable Dataframe Compiler User Guide
    ********************************************
Intel Scalable Dataframe Compiler User Guide ********************************************
def count_overload(df, axis=0, level=None, numeric_only=False):
    """
    Intel Scalable Dataframe Compiler User Guide
    ********************************************

    Pandas API: pandas.DataFrame.count

    Limitations
    -----------
    Parameters ``axis``, ``level`` and ``numeric_only`` are unsupported.

    Examples
    --------
    .. literalinclude:: ../../../examples/dataframe/dataframe_count.py
       :language: python
       :lines: 33-
       :caption: Count non-NA cells for each column or row.
       :name: ex_dataframe_count

    .. command-output:: python ./dataframe/dataframe_count.py
       :cwd: ../../../examples

    .. seealso::
        :ref:`Series.count <pandas.Series.count>`
            Number of non-NA elements in a Series.
        :ref:`DataFrame.shape <pandas.DataFrame.shape>`
            Number of DataFrame rows and columns (including NA elements).
        :ref:`DataFrame.isna <pandas.DataFrame.isna>`
            Boolean same-sized DataFrame showing places of NA elements.

    Intel Scalable Dataframe Compiler Developer Guide
    *************************************************

    Pandas DataFrame method :meth:`pandas.DataFrame.count` implementation.

        .. only:: developer

            Test: python -m sdc.runtests -k sdc.tests.test_dataframe.TestDataFrame.test_count*
    """

    name = 'count'

    ty_checker = TypeChecker('Method {}().'.format(name))
    ty_checker.check(df, DataFrameType)

    if not (isinstance(axis, types.Omitted) or axis == 0):
        ty_checker.raise_exc(axis, 'unsupported', 'axis')

    if not (isinstance(level, types.Omitted) or level is None):
        ty_checker.raise_exc(level, 'unsupported', 'level')

    if not (isinstance(numeric_only, types.Omitted) or numeric_only is False):
        ty_checker.raise_exc(numeric_only, 'unsupported', 'numeric_only')

    params = {'axis': 0, 'level': None, 'numeric_only': False}
    ser_par = {'level': 'level'}

    return sdc_pandas_dataframe_reduce_columns(df, name, params, ser_par)
def count_overload ( df , axis = 0 , level = None , numeric_only = False ) : name = 'count' ty_checker = TypeChecker ( 'Method {}().' . format ( name ) ) ty_checker . check ( df , DataFrameType ) if not ( isinstance ( axis , types . Omitted ) or axis == 0 ) : ty_checker . raise_exc ( axis , 'unsupported' , 'axis' ) if not ( isinstance ( level , types . Omitted ) or level is None ) : ty_checker . raise_exc ( level , 'unsupported' , 'level' ) if not ( isinstance ( numeric_only , types . Omitted ) or numeric_only is False ) : ty_checker . raise_exc ( numeric_only , 'unsupported' , 'numeric_only' ) params = { 'axis' : 0 , 'level' : None , 'numeric_only' : False } ser_par = { 'level' : 'level' } return sdc_pandas_dataframe_reduce_columns ( df , name , params , ser_par )