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How to use _prepare_series_input method in pandera

Best Python code snippet using pandera_python

hypotheses.py

Source:hypotheses.py

...144    @property145    def is_one_sample_test(self):146        """Return True if hypothesis is a one-sample test."""147        return len(self.samples) <= 1148    def _prepare_series_input(149        self,150        df_or_series: Union[pd.Series, pd.DataFrame],151        column: Optional[str] = None,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]):...

checks.py

Source:checks.py

...21        self._check_fn = check_fn22        self._check_kwargs = check_kwargs23        self.error = error24        self.name = name25    def _prepare_series_input(26        self,27        samples: Union[pd.Series, np.ndarray, List],28    ) -> pd.Series:29        """Prepare input for checking.30        Args:31            samples (Union[pd.Series, np.ndarray, List]): Array32            with samples.33        Returns:34            pd.Series: Samples converted to pandas Series.35        """36        if isinstance(samples, pd.Series):37            return samples38        elif isinstance(samples, pd.DataFrame):39            return samples40        elif isinstance(samples, np.ndarray):41            return pd.Series(samples)42        elif isinstance(samples, list):43            return pd.Series(samples)44        raise TypeError("Type %s not a recognized argument.")45    def __call__(46        self,47        samples: Union[np.ndarray, pd.Series, pd.DataFrame, List],48    ) -> Tuple[bool, pd.Series]:49        """Validate samples given a check method.50        Arguments:51            samples (Union[np.ndarray, pd.Series, pd.DataFrame, List]):52            Array with samples from methods.53        Returns:54            Tuple[bool, pd.Series]: A tuple indicating if a warning has55            to be called for a given samples.56        """57        # prepare check object58        check_obj = self._prepare_series_input(samples)59        # apply check function to check object60        check_fn = partial(self._check_fn, **self._check_kwargs)61        # vectorized check function case62        check_output = check_obj.apply(check_fn)63        # warning cases only apply when the check function returns a boolean64        # series that matches the shape and index of the check_obj65        if (66            isinstance(check_obj, dict) or67            isinstance(check_output, bool) or68            not isinstance(check_output, (pd.Series, pd.DataFrame)) or69            check_obj.shape[0] != check_output.shape[0] or70            (check_obj.index != check_output.index).all()71        ):72            warning_cases = None...

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