(self)
return hpat_pandas_df_values_impl(self, numba_common_dtype)
Intel Scalable Dataframe Compiler User Guide
    ********************************************
    Pandas API: pandas.DataFrame.values

    Limitations
    -----------
    - Only numeric values supported as an output
    - The dtype will be a lower-common-denominator dtype (implicit upcasting);
    that is to say if the dtypes (even of numeric types) are mixed, the one that accommodates all will be chosen.
    Use this with care if you are not dealing with the blocks.
    e.g. If the dtypes are float16 and float32, dtype will be upcast to float32. If dtypes are int32 and uint8,
    dtype will be upcast to int32. By numpy.find_common_type() convention,
    mixing int64 and uint64 will result in a float64 dtype.

    Examples
    --------
    .. literalinclude:: ../../../examples/dataframe/dataframe_values.py
      :language: python
      :lines: 27-
      :caption: The values data of the DataFrame.
      :name: ex_dataframe_values

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

    .. seealso::

        :ref:`DataFrame.to_numpy <pandas.DataFrame.to_numpy>`
            Recommended alternative to this method.
        :ref:`DataFrame.index <pandas.DataFrame.index>`
            Retrieve the index labels.
        :ref:`DataFrame.columns <pandas.DataFrame.columns>`
            Retrieving the column names.

    Intel Scalable Dataframe Compiler Developer Guide
    *************************************************
    Pandas DataFrame attribute :attr:`pandas.DataFrame.values` implementation.

    .. only:: developer
        Test: python -m sdc.runtests -k sdc.tests.test_dataframe.TestDataFrame.test_df_values*
Intel Scalable Dataframe Compiler User Guide
    ********************************************
    Pandas API: pandas.DataFrame.values
Intel Scalable Dataframe Compiler User Guide ******************************************** Pandas API : pandas . DataFrame . values
def hpat_pandas_dataframe_values(self):
    """
    Intel Scalable Dataframe Compiler User Guide
    ********************************************
    Pandas API: pandas.DataFrame.values

    Limitations
    -----------
    - Only numeric values supported as an output
    - The dtype will be a lower-common-denominator dtype (implicit upcasting);
    that is to say if the dtypes (even of numeric types) are mixed, the one that accommodates all will be chosen.
    Use this with care if you are not dealing with the blocks.
    e.g. If the dtypes are float16 and float32, dtype will be upcast to float32. If dtypes are int32 and uint8,
    dtype will be upcast to int32. By numpy.find_common_type() convention,
    mixing int64 and uint64 will result in a float64 dtype.

    Examples
    --------
    .. literalinclude:: ../../../examples/dataframe/dataframe_values.py
      :language: python
      :lines: 27-
      :caption: The values data of the DataFrame.
      :name: ex_dataframe_values

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

    .. seealso::

        :ref:`DataFrame.to_numpy <pandas.DataFrame.to_numpy>`
            Recommended alternative to this method.
        :ref:`DataFrame.index <pandas.DataFrame.index>`
            Retrieve the index labels.
        :ref:`DataFrame.columns <pandas.DataFrame.columns>`
            Retrieving the column names.

    Intel Scalable Dataframe Compiler Developer Guide
    *************************************************
    Pandas DataFrame attribute :attr:`pandas.DataFrame.values` implementation.

    .. only:: developer
        Test: python -m sdc.runtests -k sdc.tests.test_dataframe.TestDataFrame.test_df_values*
    """

    func_name = 'Attribute values.'
    ty_checker = TypeChecker(func_name)
    ty_checker.check(self, DataFrameType)

    # TODO: Handle StringArrayType
    for i, column in enumerate(self.data):
        if isinstance(column, StringArrayType):
            ty_checker.raise_exc(column, 'Numeric type', f'df.data["{self.columns[i]}"]')

    numba_common_dtype = find_common_dtype_from_numpy_dtypes([column.dtype for column in self.data], [])

    def hpat_pandas_df_values_impl(self, numba_common_dtype):
        loc_vars = {}
        func_text, global_vars = sdc_pandas_dataframe_values_codegen(self, numba_common_dtype)

        exec(func_text, global_vars, loc_vars)
        _values_impl = loc_vars['sdc_pandas_dataframe_values_impl']
        return _values_impl

    return hpat_pandas_df_values_impl(self, numba_common_dtype)
def hpat_pandas_dataframe_values ( self ) : func_name = 'Attribute values.' ty_checker = TypeChecker ( func_name ) ty_checker . check ( self , DataFrameType ) # TODO: Handle StringArrayType for i , column in enumerate ( self . data ) : if isinstance ( column , StringArrayType ) : ty_checker . raise_exc ( column , 'Numeric type' , f'df.data["{self.columns[i]}"]' ) numba_common_dtype = find_common_dtype_from_numpy_dtypes ( [ column . dtype for column in self . data ] , [ ] ) def hpat_pandas_df_values_impl ( self , numba_common_dtype ) : loc_vars = { } func_text , global_vars = sdc_pandas_dataframe_values_codegen ( self , numba_common_dtype ) exec ( func_text , global_vars , loc_vars ) _values_impl = loc_vars [ 'sdc_pandas_dataframe_values_impl' ] return _values_impl return hpat_pandas_df_values_impl ( self , numba_common_dtype )