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How to use _prepare_dataframe_input method in pandera

Best Python code snippet using pandera_python

checks.py

Source:checks.py

...289                self.groupby(df_or_series)[column],290                self.groups,291            )292        raise TypeError("Type %s not recognized for `groupby` argument.")293    def _prepare_dataframe_input(294        self, dataframe: pd.DataFrame295    ) -> DataFrameCheckObj:296        """Prepare input for DataFrameSchema check.297        :param dataframe: dataframe to validate.298        :returns: a DataFrame, or a dictionary mapping groups to pd.DataFrame299            to be used by `_check_fn` and `_vectorized_check`300        """301        if self.groupby is None:302            return dataframe303        groupby_obj = dataframe.groupby(self.groupby)304        return self._format_groupby_input(groupby_obj, self.groups)305    def __call__(306        self,307        df_or_series: Union[pd.DataFrame, pd.Series],308        column: Optional[str] = None,309    ) -> CheckResult:310        # pylint: disable=too-many-branches311        """Validate pandas DataFrame or Series.312        :param df_or_series: pandas DataFrame of Series to validate.313        :param column: for dataframe checks, apply the check function to this314            column.315        :returns: CheckResult tuple containing:316            ``check_output``: boolean scalar, ``Series`` or ``DataFrame``317            indicating which elements passed the check.318            ``check_passed``: boolean scalar that indicating whether the check319            passed overall.320            ``checked_object``: the checked object itself. Depending on the321            options provided to the ``Check``, this will be a pandas Series,322            DataFrame, or if the ``groupby`` option is specified, a323            ``Dict[str, Series]`` or ``Dict[str, DataFrame]`` where the keys324            are distinct groups.325            ``failure_cases``: subset of the check_object that failed.326        """327        # prepare check object328        if check_utils.is_field(df_or_series) or (329            column is not None and check_utils.is_table(df_or_series)330        ):331            check_obj = self._prepare_series_input(df_or_series, column)332        elif check_utils.is_table(df_or_series):333            check_obj = self._prepare_dataframe_input(df_or_series)334        else:335            raise ValueError(336                f"object of type {type(df_or_series)} not supported. Must be "337                "a Series, a dictionary of Series, or DataFrame"338            )339        # apply check function to check object340        check_fn = partial(self._check_fn, **self._check_kwargs)341        if self.element_wise:342            check_output = (343                check_obj.apply(check_fn, axis=1)  # type: ignore344                if check_utils.is_table(check_obj)345                else check_obj.map(check_fn)  # type: ignore346                if check_utils.is_field(check_obj)347                else check_fn(check_obj)...

hypotheses.py

Source:hypotheses.py

...152    ) -> SeriesCheckObj:153        """Prepare Series input for Hypothesis check."""154        self.groups = self.samples155        return super()._prepare_series_input(df_or_series, column)156    def _prepare_dataframe_input(157        self, dataframe: pd.DataFrame158    ) -> DataFrameCheckObj:159        """Prepare input for DataFrameSchema Hypothesis check."""160        if self.groupby is not None:161            raise errors.SchemaDefinitionError(162                "`groupby` cannot be used for DataFrameSchema checks, must "163                "be used in Column checks."164            )165        if self.is_one_sample_test:166            return dataframe[self.samples[0]]167        check_obj = [(sample, dataframe[sample]) for sample in self.samples]168        return self._format_groupby_input(check_obj, self.samples)169    def _relationships(self, relationship: Union[str, Callable]):170        """Impose a relationship on a supplied Test function....

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