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How to use _get_array_type method in pandera

Best Python code snippet using pandera_python

schema_statistics.py

Source:schema_statistics.py

...7from .engines import pandas_engine8def infer_dataframe_statistics(df: pd.DataFrame) -> Dict[str, Any]:9    """Infer column and index statistics from a pandas DataFrame."""10    nullable_columns = df.isna().any()11    inferred_column_dtypes = {col: _get_array_type(df[col]) for col in df}12    column_statistics = {13        col: {14            "dtype": dtype,15            "nullable": bool(nullable_columns[col]),16            "checks": _get_array_check_statistics(df[col], dtype),17        }18        for col, dtype in inferred_column_dtypes.items()19    }20    return {21        "columns": column_statistics if column_statistics else None,22        "index": infer_index_statistics(df.index),23    }24def infer_series_statistics(series: pd.Series) -> Dict[str, Any]:25    """Infer column and index statistics from a pandas Series."""26    dtype = _get_array_type(series)27    return {28        "dtype": dtype,29        "nullable": bool(series.isna().any()),30        "checks": _get_array_check_statistics(series, dtype),31        "name": series.name,32    }33def infer_index_statistics(index: Union[pd.Index, pd.MultiIndex]):34    """Infer index statistics given a pandas Index object."""35    def _index_stats(index_level):36        dtype = _get_array_type(index_level)37        return {38            "dtype": dtype,39            "nullable": bool(index_level.isna().any()),40            "checks": _get_array_check_statistics(index_level, dtype),41            "name": index_level.name,42        }43    if isinstance(index, pd.MultiIndex):44        index_statistics = [45            _index_stats(index.get_level_values(i))46            for i in range(index.nlevels)47        ]48    elif isinstance(index, pd.Index):49        index_statistics = [_index_stats(index)]50    else:51        warnings.warn(52            f"index type {type(index)} not recognized, skipping index inference",53            UserWarning,54        )55        index_statistics = []56    return index_statistics if index_statistics else None57def parse_check_statistics(check_stats: Union[Dict[str, Any], None]):58    """Convert check statistics to a list of Check objects."""59    if check_stats is None:60        return None61    checks = []62    for check_name, stats in check_stats.items():63        check = getattr(Check, check_name)64        try:65            checks.append(check(**stats))66        except TypeError:67            # if stats cannot be unpacked as key-word args, assume unary check.68            checks.append(check(stats))69    return checks if checks else None70def get_dataframe_schema_statistics(dataframe_schema):71    """Get statistical properties from dataframe schema."""72    statistics = {73        "columns": {74            col_name: {75                "dtype": column.dtype,76                "nullable": column.nullable,77                "coerce": column.coerce,78                "required": column.required,79                "regex": column.regex,80                "checks": parse_checks(column.checks),81                "unique": column.unique,82            }83            for col_name, column in dataframe_schema.columns.items()84        },85        "checks": parse_checks(dataframe_schema.checks),86        "index": (87            None88            if dataframe_schema.index is None89            else get_index_schema_statistics(dataframe_schema.index)90        ),91        "coerce": dataframe_schema.coerce,92    }93    return statistics94def _get_series_base_schema_statistics(series_schema_base):95    return {96        "dtype": series_schema_base.dtype,97        "nullable": series_schema_base.nullable,98        "checks": parse_checks(series_schema_base.checks),99        "coerce": series_schema_base.coerce,100        "name": series_schema_base.name,101    }102def get_index_schema_statistics(index_schema_component):103    """Get statistical properties of index schema component."""104    try:105        # get index components from MultiIndex106        index_components = index_schema_component.indexes107    except AttributeError:108        index_components = [index_schema_component]109    return [110        _get_series_base_schema_statistics(index_component)111        for index_component in index_components112    ]113def get_series_schema_statistics(series_schema):114    """Get statistical properties from series schema."""115    return _get_series_base_schema_statistics(series_schema)116def parse_checks(checks) -> Union[Dict[str, Any], None]:117    """Convert Check object to check statistics."""118    check_statistics = {}119    _check_memo = {}120    for check in checks:121        if check not in Check:122            warnings.warn(123                "Only registered checks may be serialized to statistics. "124                "Did you forget to register it with the extension API? "125                f"Check `{check.name}` will be skipped."126            )127            continue128        check_statistics[check.name] = (129            {} if check.statistics is None else check.statistics130        )131        _check_memo[check.name] = check132    # raise ValueError on incompatible checks133    if (134        "greater_than_or_equal_to" in check_statistics135        and "less_than_or_equal_to" in check_statistics136    ):137        min_value = check_statistics.get(138            "greater_than_or_equal_to", float("-inf")139        )["min_value"]140        max_value = check_statistics.get(141            "less_than_or_equal_to", float("inf")142        )["max_value"]143        if min_value > max_value:144            raise ValueError(145                f"checks {_check_memo['greater_than_or_equal_to']} "146                f"and {_check_memo['less_than_or_equal_to']} are incompatible, reason: "147                f"min value {min_value} > max value {max_value}"148            )149    return check_statistics if check_statistics else None150def _get_array_type(x):151    # get most granular type possible152    data_type = pandas_engine.Engine.dtype(x.dtype)153    # for object arrays, try to infer dtype154    if data_type is pandas_engine.Engine.dtype("object"):155        inferred_alias = pd.api.types.infer_dtype(x, skipna=True)156        if inferred_alias != "string":157            data_type = pandas_engine.Engine.dtype(inferred_alias)158    return data_type159def _get_array_check_statistics(160    x, data_type: dtypes.DataType161) -> Union[Dict[str, Any], None]:162    """Get check statistics from an array-like object."""163    if dtypes.is_datetime(data_type):164        check_stats = {...

array_1_registrator.py

Source:array_1_registrator.py

...17        registration_based.registration_based_t.__init__( self )18        self._array_type = array_type19        self._call_policies = self._guess_call_policies()20        self.works_on_instance = False21    def _get_array_type( self ):22        return self._array_type23    def _set_array_type( self, new_array_type ):24        self._array_type = new_array_type25    array_type = property( _get_array_type, _set_array_type )26    def _get_call_policies( self ):27        return self._call_policies28    def _set_call_policies( self, new_call_policies ):29        self._call_policies = new_call_policies30    call_policies = property( _get_call_policies, _set_call_policies )31    def _create_name( self ):32        item_type = declarations.array_item_type(self.array_type)33        return "__array_1_%(type)s_%(size)d" \34               % dict( type=algorithm.create_valid_name( item_type.decl_string )35                       , size=declarations.array_size(self.array_type) )...

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