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How to use _pandas_obj_to_validate method in pandera

Best Python code snippet using pandera_python
schemas.py
Source:schemas.py
...585                    col.dtype = self.dtype586                schema_components.append(col)587        if self.index is not None:588            schema_components.append(self.index)589        df_to_validate = _pandas_obj_to_validate(590            check_obj, head, tail, sample, random_state591        )592        check_results = []593        # schema-component-level checks594        for schema_component in schema_components:595            try:596                result = schema_component(597                    df_to_validate,598                    lazy=lazy,599                    # don't make a copy of the data600                    inplace=True,601                )602                check_results.append(check_utils.is_table(result))603            except errors.SchemaError as err:604                error_handler.collect_error("schema_component_check", err)605            except errors.SchemaErrors as err:606                for schema_error_dict in err.schema_errors:607                    error_handler.collect_error(608                        "schema_component_check", schema_error_dict["error"]609                    )610        # dataframe-level checks611        for check_index, check in enumerate(self.checks):612            try:613                check_results.append(614                    _handle_check_results(615                        self, check_index, check, df_to_validate616                    )617                )618            except errors.SchemaError as err:619                error_handler.collect_error("dataframe_check", err)620        if self.unique:621            # NOTE: fix this pylint error622            # pylint: disable=not-an-iterable623            temp_unique: List[List] = (624                [self.unique]625                if all(isinstance(x, str) for x in self.unique)626                else self.unique627            )628            for lst in temp_unique:629                duplicates = df_to_validate.duplicated(subset=lst, keep=False)630                if duplicates.any():631                    # NOTE: this is a hack to support koalas, need to figure632                    # out a workaround to error: "Cannot combine the series or633                    # dataframe because it comes from a different dataframe."634                    if type(duplicates).__module__.startswith(635                        "databricks.koalas"636                    ):637                        # pylint: disable=import-outside-toplevel638                        import databricks.koalas as ks639                        with ks.option_context(640                            "compute.ops_on_diff_frames", True641                        ):642                            failure_cases = df_to_validate.loc[duplicates, lst]643                    else:644                        failure_cases = df_to_validate.loc[duplicates, lst]645                    failure_cases = reshape_failure_cases(failure_cases)646                    error_handler.collect_error(647                        "duplicates",648                        errors.SchemaError(649                            self,650                            check_obj,651                            f"columns '{*lst,}' not unique:\n{failure_cases}",652                            failure_cases=failure_cases,653                            check="multiple_fields_uniqueness",654                        ),655                    )656        if lazy and error_handler.collected_errors:657            raise errors.SchemaErrors(658                error_handler.collected_errors, check_obj659            )660        assert all(check_results), "all check results must be True."661        return check_obj662    def __call__(663        self,664        dataframe: pd.DataFrame,665        head: Optional[int] = None,666        tail: Optional[int] = None,667        sample: Optional[int] = None,668        random_state: Optional[int] = None,669        lazy: bool = False,670        inplace: bool = False,671    ):672        """Alias for :func:`DataFrameSchema.validate` method.673        :param pd.DataFrame dataframe: the dataframe to be validated.674        :param head: validate the first n rows. Rows overlapping with `tail` or675            `sample` are de-duplicated.676        :type head: int677        :param tail: validate the last n rows. Rows overlapping with `head` or678            `sample` are de-duplicated.679        :type tail: int680        :param sample: validate a random sample of n rows. Rows overlapping681            with `head` or `tail` are de-duplicated.682        :param random_state: random seed for the ``sample`` argument.683        :param lazy: if True, lazily evaluates dataframe against all validation684            checks and raises a ``SchemaErrors``. Otherwise, raise685            ``SchemaError`` as soon as one occurs.686        :param inplace: if True, applies coercion to the object of validation,687            otherwise creates a copy of the data.688        """689        return self.validate(690            dataframe, head, tail, sample, random_state, lazy, inplace691        )692    def __repr__(self) -> str:693        """Represent string for logging."""694        return (695            f"<Schema {self.__class__.__name__}("696            f"columns={self.columns}, "697            f"checks={self.checks}, "698            f"index={self.index.__repr__()}, "699            f"coerce={self.coerce}, "700            f"dtype={self._dtype},"701            f"strict={self.strict},"702            f"name={self.name},"703            f"ordered={self.ordered}"704            ")>"705        )706    def __str__(self) -> str:707        """Represent string for user inspection."""708        def _format_multiline(json_str, arg):709            return "\n".join(710                f"{indent}{line}" if i != 0 else f"{indent}{arg}={line}"711                for i, line in enumerate(json_str.split("\n"))712            )713        indent = " " * N_INDENT_SPACES714        if self.columns:715            columns_str = f"{indent}columns={{\n"716            for colname, col in self.columns.items():717                columns_str += f"{indent * 2}'{colname}': {col}\n"718            columns_str += f"{indent}}}"719        else:720            columns_str = f"{indent}columns={{}}"721        if self.checks:722            checks_str = f"{indent}checks=[\n"723            for check in self.checks:724                checks_str += f"{indent * 2}{check}\n"725            checks_str += f"{indent}]"726        else:727            checks_str = f"{indent}checks=[]"728        # add additional indents729        index_ = str(self.index).split("\n")730        if len(index_) == 1:731            index = str(self.index)732        else:733            index = "\n".join(734                x if i == 0 else f"{indent}{x}" for i, x in enumerate(index_)735            )736        return (737            f"<Schema {self.__class__.__name__}(\n"738            f"{columns_str},\n"739            f"{checks_str},\n"740            f"{indent}coerce={self.coerce},\n"741            f"{indent}dtype={self._dtype},\n"742            f"{indent}index={index},\n"743            f"{indent}strict={self.strict}\n"744            f"{indent}name={self.name},\n"745            f"{indent}ordered={self.ordered}\n"746            ")>"747        )748    def __eq__(self, other: object) -> bool:749        if not isinstance(other, type(self)):750            return NotImplemented751        def _compare_dict(obj):752            return {753                k: v for k, v in obj.__dict__.items() if k != "_IS_INFERRED"754            }755        return _compare_dict(self) == _compare_dict(other)756    @st.strategy_import_error757    def strategy(758        self, *, size: Optional[int] = None, n_regex_columns: int = 1759    ):760        """Create a ``hypothesis`` strategy for generating a DataFrame.761        :param size: number of elements to generate762        :param n_regex_columns: number of regex columns to generate.763        :returns: a strategy that generates pandas DataFrame objects.764        """765        return st.dataframe_strategy(766            self.dtype,767            columns=self.columns,768            checks=self.checks,769            unique=self.unique,770            index=self.index,771            size=size,772            n_regex_columns=n_regex_columns,773        )774    def example(775        self, size: Optional[int] = None, n_regex_columns: int = 1776    ) -> pd.DataFrame:777        """Generate an example of a particular size.778        :param size: number of elements in the generated DataFrame.779        :returns: pandas DataFrame object.780        """781        # pylint: disable=import-outside-toplevel,cyclic-import,import-error782        import hypothesis783        with warnings.catch_warnings():784            warnings.simplefilter(785                "ignore",786                category=hypothesis.errors.NonInteractiveExampleWarning,787            )788            return self.strategy(789                size=size, n_regex_columns=n_regex_columns790            ).example()791    @_inferred_schema_guard792    def add_columns(793        self, extra_schema_cols: Dict[str, Any]794    ) -> "DataFrameSchema":795        """Create a copy of the :class:`DataFrameSchema` with extra columns.796        :param extra_schema_cols: Additional columns of the format797        :type extra_schema_cols: DataFrameSchema798        :returns: a new :class:`DataFrameSchema` with the extra_schema_cols799            added.800        :example:801        To add columns to the schema, pass a dictionary with column name and802        ``Column`` instance key-value pairs.803        >>> import pandera as pa804        >>>805        >>> example_schema = pa.DataFrameSchema(806        ...    {807        ...        "category": pa.Column(str),808        ...        "probability": pa.Column(float),809        ...    }810        ... )811        >>> print(812        ...     example_schema.add_columns({"even_number": pa.Column(pa.Bool)})813        ... )814        <Schema DataFrameSchema(815            columns={816                'category': <Schema Column(name=category, type=DataType(str))>817                'probability': <Schema Column(name=probability, type=DataType(float64))>818                'even_number': <Schema Column(name=even_number, type=DataType(bool))>819            },820            checks=[],821            coerce=False,822            dtype=None,823            index=None,824            strict=False825            name=None,826            ordered=False827        )>828        .. seealso:: :func:`remove_columns`829        """830        schema_copy = copy.deepcopy(self)831        schema_copy.columns = {832            **schema_copy.columns,833            **DataFrameSchema(extra_schema_cols).columns,834        }835        return schema_copy836    @_inferred_schema_guard837    def remove_columns(self, cols_to_remove: List[str]) -> "DataFrameSchema":838        """Removes columns from a :class:`DataFrameSchema` and returns a new839        copy.840        :param cols_to_remove: Columns to be removed from the841            ``DataFrameSchema``842        :type cols_to_remove: List843        :returns: a new :class:`DataFrameSchema` without the cols_to_remove844        :raises: :class:`~pandera.errors.SchemaInitError`: if column not in845            schema.846        :example:847        To remove a column or set of columns from a schema, pass a list of848        columns to be removed:849        >>> import pandera as pa850        >>>851        >>> example_schema = pa.DataFrameSchema(852        ...     {853        ...         "category" : pa.Column(str),854        ...         "probability": pa.Column(float)855        ...     }856        ... )857        >>>858        >>> print(example_schema.remove_columns(["category"]))859        <Schema DataFrameSchema(860            columns={861                'probability': <Schema Column(name=probability, type=DataType(float64))>862            },863            checks=[],864            coerce=False,865            dtype=None,866            index=None,867            strict=False868            name=None,869            ordered=False870        )>871        .. seealso:: :func:`add_columns`872        """873        schema_copy = copy.deepcopy(self)874        # ensure all specified keys are present in the columns875        not_in_cols: List[str] = [876            x for x in cols_to_remove if x not in schema_copy.columns.keys()877        ]878        if not_in_cols:879            raise errors.SchemaInitError(880                f"Keys {not_in_cols} not found in schema columns!"881            )882        for col in cols_to_remove:883            schema_copy.columns.pop(col)884        return schema_copy885    @_inferred_schema_guard886    def update_column(self, column_name: str, **kwargs) -> "DataFrameSchema":887        """Create copy of a :class:`DataFrameSchema` with updated column888        properties.889        :param column_name:890        :param kwargs: key-word arguments supplied to891            :class:`~pandera.schema_components.Column`892        :returns: a new :class:`DataFrameSchema` with updated column893        :raises: :class:`~pandera.errors.SchemaInitError`: if column not in894            schema or you try to change the name.895        :example:896        Calling ``schema.1`` returns the :class:`DataFrameSchema`897        with the updated column.898        >>> import pandera as pa899        >>>900        >>> example_schema = pa.DataFrameSchema({901        ...     "category" : pa.Column(str),902        ...     "probability": pa.Column(float)903        ... })904        >>> print(905        ...     example_schema.update_column(906        ...         'category', dtype=pa.Category907        ...     )908        ... )909        <Schema DataFrameSchema(910            columns={911                'category': <Schema Column(name=category, type=DataType(category))>912                'probability': <Schema Column(name=probability, type=DataType(float64))>913            },914            checks=[],915            coerce=False,916            dtype=None,917            index=None,918            strict=False919            name=None,920            ordered=False921        )>922        .. seealso:: :func:`rename_columns`923        """924        # check that columns exist in schema925        if "name" in kwargs:926            raise ValueError("cannot update 'name' of the column.")927        if column_name not in self.columns:928            raise ValueError(f"column '{column_name}' not in {self}")929        schema_copy = copy.deepcopy(self)930        column_copy = copy.deepcopy(self.columns[column_name])931        new_column = column_copy.__class__(932            **{**column_copy.properties, **kwargs}933        )934        schema_copy.columns.update({column_name: new_column})935        return schema_copy936    def update_columns(937        self, update_dict: Dict[str, Dict[str, Any]]938    ) -> "DataFrameSchema":939        """940        Create copy of a :class:`DataFrameSchema` with updated column941        properties.942        :param update_dict:943        :return: a new :class:`DataFrameSchema` with updated columns944        :raises: :class:`~pandera.errors.SchemaInitError`: if column not in945            schema or you try to change the name.946        :example:947        Calling ``schema.update_columns`` returns the :class:`DataFrameSchema`948        with the updated columns.949        >>> import pandera as pa950        >>>951        >>> example_schema = pa.DataFrameSchema({952        ...     "category" : pa.Column(str),953        ...     "probability": pa.Column(float)954        ... })955        >>>956        >>> print(957        ...     example_schema.update_columns(958        ...         {"category": {"dtype":pa.Category}}959        ...     )960        ... )961        <Schema DataFrameSchema(962            columns={963                'category': <Schema Column(name=category, type=DataType(category))>964                'probability': <Schema Column(name=probability, type=DataType(float64))>965            },966            checks=[],967            coerce=False,968            dtype=None,969            index=None,970            strict=False971            name=None,972            ordered=False973        )>974        .. note:: This is the successor to the ``update_column`` method, which975            will be deprecated.976        """977        new_schema = copy.deepcopy(self)978        # ensure all specified keys are present in the columns979        not_in_cols: List[str] = [980            x for x in update_dict.keys() if x not in new_schema.columns.keys()981        ]982        if not_in_cols:983            raise errors.SchemaInitError(984                f"Keys {not_in_cols} not found in schema columns!"985            )986        new_columns: Dict[str, Column] = {}987        for col in new_schema.columns:988            # check989            if update_dict.get(col):990                if update_dict[col].get("name"):991                    raise errors.SchemaInitError(992                        "cannot update 'name' \993                                             property of the column."994                    )995            original_properties = new_schema.columns[col].properties996            if update_dict.get(col):997                new_properties = copy.deepcopy(original_properties)998                new_properties.update(update_dict[col])999                new_columns[col] = new_schema.columns[col].__class__(1000                    **new_properties1001                )1002            else:1003                new_columns[col] = new_schema.columns[col].__class__(1004                    **original_properties1005                )1006        new_schema.columns = new_columns1007        return new_schema1008    def rename_columns(self, rename_dict: Dict[str, str]) -> "DataFrameSchema":1009        """Rename columns using a dictionary of key-value pairs.1010        :param rename_dict: dictionary of 'old_name': 'new_name' key-value1011            pairs.1012        :returns: :class:`DataFrameSchema` (copy of original)1013        :raises: :class:`~pandera.errors.SchemaInitError` if column not in the1014            schema.1015        :example:1016        To rename a column or set of columns, pass a dictionary of old column1017        names and new column names, similar to the pandas DataFrame method.1018        >>> import pandera as pa1019        >>>1020        >>> example_schema = pa.DataFrameSchema({1021        ...     "category" : pa.Column(str),1022        ...     "probability": pa.Column(float)1023        ... })1024        >>>1025        >>> print(1026        ...     example_schema.rename_columns({1027        ...         "category": "categories",1028        ...         "probability": "probabilities"1029        ...     })1030        ... )1031        <Schema DataFrameSchema(1032            columns={1033                'categories': <Schema Column(name=categories, type=DataType(str))>1034                'probabilities': <Schema Column(name=probabilities, type=DataType(float64))>1035            },1036            checks=[],1037            coerce=False,1038            dtype=None,1039            index=None,1040            strict=False1041            name=None,1042            ordered=False1043        )>1044        .. seealso:: :func:`update_column`1045        """1046        new_schema = copy.deepcopy(self)1047        # ensure all specified keys are present in the columns1048        not_in_cols: List[str] = [1049            x for x in rename_dict.keys() if x not in new_schema.columns.keys()1050        ]1051        if not_in_cols:1052            raise errors.SchemaInitError(1053                f"Keys {not_in_cols} not found in schema columns!"1054            )1055        # ensure all new keys are not present in the current column names1056        already_in_columns: List[str] = [1057            x for x in rename_dict.values() if x in new_schema.columns.keys()1058        ]1059        if already_in_columns:1060            raise errors.SchemaInitError(1061                f"Keys {already_in_columns} already found in schema columns!"1062            )1063        # We iterate over the existing columns dict and replace those keys1064        # that exist in the rename_dict1065        new_columns = {1066            (rename_dict[col_name] if col_name in rename_dict else col_name): (1067                col_attrs.set_name(rename_dict[col_name])1068                if col_name in rename_dict1069                else col_attrs1070            )1071            for col_name, col_attrs in new_schema.columns.items()1072        }1073        new_schema.columns = new_columns1074        return new_schema1075    def select_columns(self, columns: List[Any]) -> "DataFrameSchema":1076        """Select subset of columns in the schema.1077        *New in version 0.4.5*1078        :param columns: list of column names to select.1079        :returns:  :class:`DataFrameSchema` (copy of original) with only1080            the selected columns.1081        :raises: :class:`~pandera.errors.SchemaInitError` if column not in the1082            schema.1083        :example:1084        To subset a schema by column, and return a new schema:1085        >>> import pandera as pa1086        >>>1087        >>> example_schema = pa.DataFrameSchema({1088        ...     "category" : pa.Column(str),1089        ...     "probability": pa.Column(float)1090        ... })1091        >>>1092        >>> print(example_schema.select_columns(['category']))1093        <Schema DataFrameSchema(1094            columns={1095                'category': <Schema Column(name=category, type=DataType(str))>1096            },1097            checks=[],1098            coerce=False,1099            dtype=None,1100            index=None,1101            strict=False1102            name=None,1103            ordered=False1104        )>1105        .. note:: If an index is present in the schema, it will also be1106            included in the new schema.1107        """1108        new_schema = copy.deepcopy(self)1109        # ensure all specified keys are present in the columns1110        not_in_cols: List[str] = [1111            x for x in columns if x not in new_schema.columns.keys()1112        ]1113        if not_in_cols:1114            raise errors.SchemaInitError(1115                f"Keys {not_in_cols} not found in schema columns!"1116            )1117        new_columns = {1118            col_name: column1119            for col_name, column in self.columns.items()1120            if col_name in columns1121        }1122        new_schema.columns = new_columns1123        return new_schema1124    def to_script(self, fp: Union[str, Path] = None) -> "DataFrameSchema":1125        """Create DataFrameSchema from yaml file.1126        :param path: str, Path to write script1127        :returns: dataframe schema.1128        """1129        # pylint: disable=import-outside-toplevel,cyclic-import1130        import pandera.io1131        return pandera.io.to_script(self, fp)1132    @classmethod1133    def from_yaml(cls, yaml_schema) -> "DataFrameSchema":1134        """Create DataFrameSchema from yaml file.1135        :param yaml_schema: str, Path to yaml schema, or serialized yaml1136            string.1137        :returns: dataframe schema.1138        """1139        # pylint: disable=import-outside-toplevel,cyclic-import1140        import pandera.io1141        return pandera.io.from_yaml(yaml_schema)1142    def to_yaml(self, stream: Optional[os.PathLike] = None):1143        """Write DataFrameSchema to yaml file.1144        :param stream: file stream to write to. If None, dumps to string.1145        :returns: yaml string if stream is None, otherwise returns None.1146        """1147        # pylint: disable=import-outside-toplevel,cyclic-import1148        import pandera.io1149        return pandera.io.to_yaml(self, stream=stream)1150    def set_index(1151        self, keys: List[str], drop: bool = True, append: bool = False1152    ) -> "DataFrameSchema":1153        """1154        A method for setting the :class:`Index` of a :class:`DataFrameSchema`,1155        via an existing :class:`Column` or list of columns.1156        :param keys: list of labels1157        :param drop: bool, default True1158        :param append: bool, default False1159        :return: a new :class:`DataFrameSchema` with specified column(s) in the1160            index.1161        :raises: :class:`~pandera.errors.SchemaInitError` if column not in the1162            schema.1163        :examples:1164        Just as you would set the index in a ``pandas`` DataFrame from an1165        existing column, you can set an index within the schema from an1166        existing column in the schema.1167        >>> import pandera as pa1168        >>>1169        >>> example_schema = pa.DataFrameSchema({1170        ...     "category" : pa.Column(str),1171        ...     "probability": pa.Column(float)})1172        >>>1173        >>> print(example_schema.set_index(['category']))1174        <Schema DataFrameSchema(1175            columns={1176                'probability': <Schema Column(name=probability, type=DataType(float64))>1177            },1178            checks=[],1179            coerce=False,1180            dtype=None,1181            index=<Schema Index(name=category, type=DataType(str))>,1182            strict=False1183            name=None,1184            ordered=False1185        )>1186        If you have an existing index in your schema, and you would like to1187        append a new column as an index to it (yielding a :class:`Multiindex`),1188        just use set_index as you would in pandas.1189        >>> example_schema = pa.DataFrameSchema(1190        ...     {1191        ...         "column1": pa.Column(str),1192        ...         "column2": pa.Column(int)1193        ...     },1194        ...     index=pa.Index(name = "column3", dtype = int)1195        ... )1196        >>>1197        >>> print(example_schema.set_index(["column2"], append = True))1198        <Schema DataFrameSchema(1199            columns={1200                'column1': <Schema Column(name=column1, type=DataType(str))>1201            },1202            checks=[],1203            coerce=False,1204            dtype=None,1205            index=<Schema MultiIndex(1206                indexes=[1207                    <Schema Index(name=column3, type=DataType(int64))>1208                    <Schema Index(name=column2, type=DataType(int64))>1209                ]1210                coerce=False,1211                strict=False,1212                name=None,1213                ordered=True1214            )>,1215            strict=False1216            name=None,1217            ordered=False1218        )>1219        .. seealso:: :func:`reset_index`1220        """1221        # pylint: disable=import-outside-toplevel,cyclic-import1222        from pandera.schema_components import Index, MultiIndex1223        new_schema = copy.deepcopy(self)1224        keys_temp: List = (1225            list(set(keys)) if not isinstance(keys, list) else keys1226        )1227        # ensure all specified keys are present in the columns1228        not_in_cols: List[str] = [1229            x for x in keys_temp if x not in new_schema.columns.keys()1230        ]1231        if not_in_cols:1232            raise errors.SchemaInitError(1233                f"Keys {not_in_cols} not found in schema columns!"1234            )1235        # if there is already an index, append or replace according to1236        # parameters1237        ind_list: List = (1238            []1239            if new_schema.index is None or not append1240            else list(new_schema.index.indexes)1241            if isinstance(new_schema.index, MultiIndex) and append1242            else [new_schema.index]1243        )1244        for col in keys_temp:1245            ind_list.append(1246                Index(1247                    dtype=new_schema.columns[col].dtype,1248                    name=col,1249                    checks=new_schema.columns[col].checks,1250                    nullable=new_schema.columns[col].nullable,1251                    unique=new_schema.columns[col].unique,1252                    coerce=new_schema.columns[col].coerce,1253                )1254            )1255        new_schema.index = (1256            ind_list[0] if len(ind_list) == 1 else MultiIndex(ind_list)1257        )1258        # if drop is True as defaulted, drop the columns moved into the index1259        if drop:1260            new_schema = new_schema.remove_columns(keys_temp)1261        return new_schema1262    def reset_index(1263        self, level: List[str] = None, drop: bool = False1264    ) -> "DataFrameSchema":1265        """1266        A method for resetting the :class:`Index` of a :class:`DataFrameSchema`1267        :param level: list of labels1268        :param drop: bool, default True1269        :return: a new :class:`DataFrameSchema` with specified column(s) in the1270            index.1271        :raises: :class:`~pandera.errors.SchemaInitError` if no index set in1272            schema.1273        :examples:1274        Similar to the ``pandas`` reset_index method on a pandas DataFrame,1275        this method can be used to to fully or partially reset indices of a1276        schema.1277        To remove the entire index from the schema, just call the reset_index1278        method with default parameters.1279        >>> import pandera as pa1280        >>>1281        >>> example_schema = pa.DataFrameSchema(1282        ...     {"probability" : pa.Column(float)},1283        ...     index = pa.Index(name="unique_id", dtype=int)1284        ... )1285        >>>1286        >>> print(example_schema.reset_index())1287        <Schema DataFrameSchema(1288            columns={1289                'probability': <Schema Column(name=probability, type=DataType(float64))>1290                'unique_id': <Schema Column(name=unique_id, type=DataType(int64))>1291            },1292            checks=[],1293            coerce=False,1294            dtype=None,1295            index=None,1296            strict=False1297            name=None,1298            ordered=False1299        )>1300        This reclassifies an index (or indices) as a column (or columns).1301        Similarly, to partially alter the index, pass the name of the column1302        you would like to be removed to the ``level`` parameter, and you may1303        also decide whether to drop the levels with the ``drop`` parameter.1304        >>> example_schema = pa.DataFrameSchema({1305        ...     "category" : pa.Column(str)},1306        ...     index = pa.MultiIndex([1307        ...         pa.Index(name="unique_id1", dtype=int),1308        ...         pa.Index(name="unique_id2", dtype=str)1309        ...         ]1310        ...     )1311        ... )1312        >>> print(example_schema.reset_index(level = ["unique_id1"]))1313        <Schema DataFrameSchema(1314            columns={1315                'category': <Schema Column(name=category, type=DataType(str))>1316                'unique_id1': <Schema Column(name=unique_id1, type=DataType(int64))>1317            },1318            checks=[],1319            coerce=False,1320            dtype=None,1321            index=<Schema Index(name=unique_id2, type=DataType(str))>,1322            strict=False1323            name=None,1324            ordered=False1325        )>1326        .. seealso:: :func:`set_index`1327        """1328        # pylint: disable=import-outside-toplevel,cyclic-import1329        from pandera.schema_components import Column, Index, MultiIndex1330        new_schema = copy.deepcopy(self)1331        if new_schema.index is None:1332            raise errors.SchemaInitError(1333                "There is currently no index set for this schema."1334            )1335        # ensure no duplicates1336        level_temp: Union[List[Any], List[str]] = (1337            list(set(level)) if level is not None else []1338        )1339        # ensure all specified keys are present in the index1340        level_not_in_index: Union[List[Any], List[str], None] = (1341            [x for x in level_temp if x not in new_schema.index.names]1342            if isinstance(new_schema.index, MultiIndex) and level_temp1343            else []1344            if isinstance(new_schema.index, Index)1345            and (level_temp == [new_schema.index.name])1346            else level_temp1347        )1348        if level_not_in_index:1349            raise errors.SchemaInitError(1350                f"Keys {level_not_in_index} not found in schema columns!"1351            )1352        new_index = (1353            None1354            if not level_temp or isinstance(new_schema.index, Index)1355            else new_schema.index.remove_columns(level_temp)1356        )1357        new_index = (1358            new_index1359            if new_index is None1360            else Index(1361                dtype=new_index.columns[list(new_index.columns)[0]].dtype,1362                checks=new_index.columns[list(new_index.columns)[0]].checks,1363                nullable=new_index.columns[1364                    list(new_index.columns)[0]1365                ].nullable,1366                unique=new_index.columns[list(new_index.columns)[0]].unique,1367                coerce=new_index.columns[list(new_index.columns)[0]].coerce,1368                name=new_index.columns[list(new_index.columns)[0]].name,1369            )1370            if (len(list(new_index.columns)) == 1) and (new_index is not None)1371            else None1372            if (len(list(new_index.columns)) == 0) and (new_index is not None)1373            else new_index1374        )1375        if not drop:1376            additional_columns: Dict[str, Any] = (1377                {col: new_schema.index.columns.get(col) for col in level_temp}1378                if isinstance(new_schema.index, MultiIndex)1379                else {new_schema.index.name: new_schema.index}1380            )1381            new_schema = new_schema.add_columns(1382                {1383                    k: Column(1384                        dtype=v.dtype,1385                        checks=v.checks,1386                        nullable=v.nullable,1387                        unique=v.unique,1388                        coerce=v.coerce,1389                        name=v.name,1390                    )1391                    for (k, v) in additional_columns.items()1392                }1393            )1394        new_schema.index = new_index1395        return new_schema1396    @classmethod1397    def __get_validators__(cls):1398        yield cls._pydantic_validate1399    @classmethod1400    def _pydantic_validate(cls, schema: Any) -> "DataFrameSchema":1401        """Verify that the input is a compatible DataFrameSchema."""1402        if not isinstance(schema, cls):  # type: ignore1403            raise TypeError(f"{schema} is not a {cls}.")1404        return cast("DataFrameSchema", schema)1405class SeriesSchemaBase:1406    """Base series validator object."""1407    @deprecate_pandas_dtype1408    def __init__(1409        self,1410        dtype: PandasDtypeInputTypes = None,1411        checks: CheckList = None,1412        nullable: bool = False,1413        unique: bool = False,1414        allow_duplicates: Optional[bool] = None,1415        coerce: bool = False,1416        name: Any = None,1417        pandas_dtype: PandasDtypeInputTypes = None,1418        title: Optional[str] = None,1419        description: Optional[str] = None,1420    ) -> None:1421        """Initialize series schema base object.1422        :param dtype: datatype of the column. If a string is specified,1423            then assumes one of the valid pandas string values:1424            http://pandas.pydata.org/pandas-docs/stable/basics.html#dtypes1425        :param checks: If element_wise is True, then callable signature should1426            be:1427            ``Callable[Any, bool]`` where the ``Any`` input is a scalar element1428            in the column. Otherwise, the input is assumed to be a1429            pandas.Series object.1430        :param nullable: Whether or not column can contain null values.1431        :param unique: Whether or not column can contain duplicate1432            values.1433        :param allow_duplicates: Whether or not column can contain duplicate1434            values.1435        .. warning::1436            This option will be deprecated in 0.8.0. Use the ``unique``1437            argument instead.1438        :param coerce: If True, when schema.validate is called the column will1439            be coerced into the specified dtype. This has no effect on columns1440            where ``dtype=None``.1441        :param name: column name in dataframe to validate.1442        :param pandas_dtype: alias of ``dtype`` for backwards compatibility.1443            .. warning:: This option will be deprecated in 0.8.01444        :param title: A human-readable label for the series.1445        :param description: An arbitrary textual description of the series.1446        :type nullable: bool1447        """1448        if checks is None:1449            checks = []1450        if isinstance(checks, (Check, Hypothesis)):1451            checks = [checks]1452        if allow_duplicates is not None:1453            warnings.warn(1454                "The `allow_duplicates` will be deprecated in "1455                "favor of the `unique` keyword. The value of "1456                "`unique` will be set to the opposite of "1457                "the `allow_duplicates` keyword.",1458                DeprecationWarning,1459            )1460            unique = not allow_duplicates1461        self.dtype = dtype or pandas_dtype  # type: ignore1462        self._nullable = nullable1463        self._coerce = coerce1464        self._checks = checks1465        self._name = name1466        self._unique = unique1467        self._title = title1468        self._description = description1469        for check in self.checks:1470            if check.groupby is not None and not self._allow_groupby:1471                raise errors.SchemaInitError(1472                    f"Cannot use groupby checks with type {type(self)}"1473                )1474        # make sure pandas dtype is valid1475        self.dtype  # pylint: disable=pointless-statement1476        # this attribute is not meant to be accessed by users and is explicitly1477        # set to True in the case that a schema is created by infer_schema.1478        self._IS_INFERRED = False1479    # the _is_inferred getter and setter methods are not public1480    @property1481    def _is_inferred(self):1482        return self._IS_INFERRED1483    @_is_inferred.setter1484    def _is_inferred(self, value: bool):1485        self._IS_INFERRED = value1486    @property1487    def checks(self):1488        """Return list of checks or hypotheses."""1489        return self._checks1490    @checks.setter1491    def checks(self, checks):1492        self._checks = checks1493    @_inferred_schema_guard1494    def set_checks(self, checks: CheckList):1495        """Create a new SeriesSchema with a new set of Checks1496        :param checks: checks to set on the new schema1497        :returns: a new SeriesSchema with a new set of checks1498        """1499        schema_copy = copy.deepcopy(self)1500        schema_copy.checks = checks1501        return schema_copy1502    @property1503    def nullable(self) -> bool:1504        """Whether the series is nullable."""1505        return self._nullable1506    @property1507    def unique(self) -> bool:1508        """Whether to check for duplicates in check object"""1509        return self._unique1510    @unique.setter1511    def unique(self, value: bool) -> None:1512        """Set unique attribute"""1513        self._unique = value1514    @property1515    def allow_duplicates(self) -> bool:1516        """Whether to allow duplicate values."""1517        return not self._unique1518    @allow_duplicates.setter1519    def allow_duplicates(self, value: bool) -> None:1520        """Set allow_duplicates attribute."""1521        self._unique = not value1522    @property1523    def coerce(self) -> bool:1524        """Whether to coerce series to specified type."""1525        return self._coerce1526    @coerce.setter1527    def coerce(self, value: bool) -> None:1528        """Set coerce attribute."""1529        self._coerce = value1530    @property1531    def name(self) -> Union[str, None]:1532        """Get SeriesSchema name."""1533        return self._name1534    @property1535    def title(self):1536        """A human-readable label for the series."""1537        return self._title1538    @property1539    def description(self):1540        """An arbitrary textual description of the series."""1541        return self._description1542    @property1543    def dtype(1544        self,1545    ) -> DataType:1546        """Get the pandas dtype"""1547        return self._dtype  # type: ignore1548    @dtype.setter1549    def dtype(self, value: PandasDtypeInputTypes) -> None:1550        """Set the pandas dtype"""1551        self._dtype = pandas_engine.Engine.dtype(value) if value else None1552    def coerce_dtype(self, obj: Union[pd.Series, pd.Index]) -> pd.Series:1553        """Coerce type of a pd.Series by type specified in dtype.1554        :param pd.Series series: One-dimensional ndarray with axis labels1555            (including time series).1556        :returns: ``Series`` with coerced data type1557        """1558        if self.dtype is None:1559            return obj1560        try:1561            return self.dtype.try_coerce(obj)1562        except errors.ParserError as exc:1563            msg = (1564                f"Error while coercing '{self.name}' to type "1565                f"{self.dtype}: {exc}:\n{exc.failure_cases}"1566            )1567            raise errors.SchemaError(1568                self,1569                obj,1570                msg,1571                failure_cases=exc.failure_cases,1572                check=f"coerce_dtype('{self.dtype}')",1573            ) from exc1574    @property1575    def _allow_groupby(self):1576        """Whether the schema or schema component allows groupby operations."""1577        raise NotImplementedError(  # pragma: no cover1578            "The _allow_groupby property must be implemented by subclasses "1579            "of SeriesSchemaBase"1580        )1581    def validate(1582        self,1583        check_obj: Union[pd.DataFrame, pd.Series],1584        head: Optional[int] = None,1585        tail: Optional[int] = None,1586        sample: Optional[int] = None,1587        random_state: Optional[int] = None,1588        lazy: bool = False,1589        inplace: bool = False,1590    ) -> Union[pd.DataFrame, pd.Series]:1591        # pylint: disable=too-many-locals,too-many-branches,too-many-statements1592        """Validate a series or specific column in dataframe.1593        :check_obj: pandas DataFrame or Series to validate.1594        :param head: validate the first n rows. Rows overlapping with `tail` or1595            `sample` are de-duplicated.1596        :param tail: validate the last n rows. Rows overlapping with `head` or1597            `sample` are de-duplicated.1598        :param sample: validate a random sample of n rows. Rows overlapping1599            with `head` or `tail` are de-duplicated.1600        :param random_state: random seed for the ``sample`` argument.1601        :param lazy: if True, lazily evaluates dataframe against all validation1602            checks and raises a ``SchemaErrors``. Otherwise, raise1603            ``SchemaError`` as soon as one occurs.1604        :param inplace: if True, applies coercion to the object of validation,1605            otherwise creates a copy of the data.1606        :returns: validated DataFrame or Series.1607        """1608        if self._is_inferred:1609            warnings.warn(1610                f"This {type(self)} is an inferred schema that hasn't been "1611                "modified. It's recommended that you refine the schema "1612                "by calling `set_checks` before using it to validate data.",1613                UserWarning,1614            )1615        error_handler = SchemaErrorHandler(lazy)1616        if not inplace:1617            check_obj = check_obj.copy()1618        series = (1619            check_obj1620            if check_utils.is_field(check_obj)1621            else check_obj[self.name]1622        )1623        series = _pandas_obj_to_validate(1624            series, head, tail, sample, random_state1625        )1626        check_obj = _pandas_obj_to_validate(1627            check_obj, head, tail, sample, random_state1628        )1629        if self.name is not None and series.name != self._name:1630            msg = (1631                f"Expected {type(self)} to have name '{self._name}', found "1632                f"'{series.name}'"1633            )1634            error_handler.collect_error(1635                "wrong_field_name",1636                errors.SchemaError(1637                    self,1638                    check_obj,1639                    msg,1640                    failure_cases=scalar_failure_case(series.name),1641                    check=f"field_name('{self._name}')",1642                ),1643            )1644        if not self._nullable:1645            nulls = series.isna()1646            if nulls.sum() > 0:1647                failed = series[nulls]1648                msg = (1649                    f"non-nullable series '{series.name}' contains null "1650                    f"values:\n{failed}"1651                )1652                error_handler.collect_error(1653                    "series_contains_nulls",1654                    errors.SchemaError(1655                        self,1656                        check_obj,1657                        msg,1658                        failure_cases=reshape_failure_cases(1659                            series[nulls], ignore_na=False1660                        ),1661                        check="not_nullable",1662                    ),1663                )1664        # Check if the series contains duplicate values1665        if self._unique:1666            if type(series).__module__.startswith("databricks.koalas"):1667                duplicates = (1668                    series.to_frame().duplicated().reindex(series.index)1669                )1670                # pylint: disable=import-outside-toplevel1671                import databricks.koalas as ks1672                with ks.option_context("compute.ops_on_diff_frames", True):1673                    failed = series[duplicates]1674            else:1675                duplicates = series.duplicated()1676                failed = series[duplicates]1677            if duplicates.any():1678                msg = (1679                    f"series '{series.name}' contains duplicate values:\n"1680                    f"{failed}"1681                )1682                error_handler.collect_error(1683                    "series_contains_duplicates",1684                    errors.SchemaError(1685                        self,1686                        check_obj,1687                        msg,1688                        failure_cases=reshape_failure_cases(failed),1689                        check="field_uniqueness",1690                    ),1691                )1692        if self._dtype is not None and (1693            not self._dtype.check(pandas_engine.Engine.dtype(series.dtype))1694        ):1695            msg = (1696                f"expected series '{series.name}' to have type {self._dtype}, "1697                + f"got {series.dtype}"1698            )1699            error_handler.collect_error(1700                "wrong_dtype",1701                errors.SchemaError(1702                    self,1703                    check_obj,1704                    msg,1705                    failure_cases=scalar_failure_case(str(series.dtype)),1706                    check=f"dtype('{self.dtype}')",1707                ),1708            )1709        check_results = []1710        if check_utils.is_field(check_obj):1711            check_obj, check_args = series, [None]1712        else:1713            check_args = [self.name]  # type: ignore1714        for check_index, check in enumerate(self.checks):1715            try:1716                check_results.append(1717                    _handle_check_results(1718                        self, check_index, check, check_obj, *check_args1719                    )1720                )1721            except errors.SchemaError as err:1722                error_handler.collect_error("dataframe_check", err)1723            except Exception as err:  # pylint: disable=broad-except1724                # catch other exceptions that may occur when executing the1725                # Check1726                err_msg = f'"{err.args[0]}"' if len(err.args) > 0 else ""1727                err_str = f"{err.__class__.__name__}({ err_msg})"1728                msg = (1729                    f"Error while executing check function: {err_str}\n"1730                    + traceback.format_exc()1731                )1732                error_handler.collect_error(1733                    "check_error",1734                    errors.SchemaError(1735                        self,1736                        check_obj,1737                        msg,1738                        failure_cases=scalar_failure_case(err_str),1739                        check=check,1740                        check_index=check_index,1741                    ),1742                    original_exc=err,1743                )1744        if lazy and error_handler.collected_errors:1745            raise errors.SchemaErrors(1746                error_handler.collected_errors, check_obj1747            )1748        assert all(check_results)1749        return check_obj1750    def __call__(1751        self,1752        check_obj: Union[pd.DataFrame, pd.Series],1753        head: Optional[int] = None,1754        tail: Optional[int] = None,1755        sample: Optional[int] = None,1756        random_state: Optional[int] = None,1757        lazy: bool = False,1758        inplace: bool = False,1759    ) -> Union[pd.DataFrame, pd.Series]:1760        """Alias for ``validate`` method."""1761        return self.validate(1762            check_obj, head, tail, sample, random_state, lazy, inplace1763        )1764    def __eq__(self, other):1765        return self.__dict__ == other.__dict__1766    @st.strategy_import_error1767    def strategy(self, *, size=None):1768        """Create a ``hypothesis`` strategy for generating a Series.1769        :param size: number of elements to generate1770        :returns: a strategy that generates pandas Series objects.1771        """1772        return st.series_strategy(1773            self.dtype,1774            checks=self.checks,1775            nullable=self.nullable,1776            unique=self.unique,1777            name=self.name,1778            size=size,1779        )1780    def example(self, size=None) -> pd.Series:1781        """Generate an example of a particular size.1782        :param size: number of elements in the generated Series.1783        :returns: pandas Series object.1784        """1785        # pylint: disable=import-outside-toplevel,cyclic-import,import-error1786        import hypothesis1787        with warnings.catch_warnings():1788            warnings.simplefilter(1789                "ignore",1790                category=hypothesis.errors.NonInteractiveExampleWarning,1791            )1792            return self.strategy(size=size).example()1793    def __repr__(self):1794        return (1795            f"<Schema {self.__class__.__name__}"1796            f"(name={self._name}, type={self.dtype!r})>"1797        )1798    @classmethod1799    def __get_validators__(cls):1800        yield cls._pydantic_validate1801    @classmethod1802    def _pydantic_validate(  # type: ignore1803        cls: TSeriesSchemaBase, schema: Any1804    ) -> TSeriesSchemaBase:1805        """Verify that the input is a compatible DataFrameSchema."""1806        if not isinstance(schema, cls):  # type: ignore1807            raise TypeError(f"{schema} is not a {cls}.")1808        return cast(TSeriesSchemaBase, schema)1809class SeriesSchema(SeriesSchemaBase):1810    """Series validator."""1811    @deprecate_pandas_dtype1812    def __init__(1813        self,1814        dtype: PandasDtypeInputTypes = None,1815        checks: CheckList = None,1816        index=None,1817        nullable: bool = False,1818        unique: bool = False,1819        allow_duplicates: Optional[bool] = None,1820        coerce: bool = False,1821        name: str = None,1822        pandas_dtype: PandasDtypeInputTypes = None,1823        title: Optional[str] = None,1824        description: Optional[str] = None,1825    ) -> None:1826        """Initialize series schema base object.1827        :param dtype: datatype of the column. If a string is specified,1828            then assumes one of the valid pandas string values:1829            http://pandas.pydata.org/pandas-docs/stable/basics.html#dtypes1830        :param checks: If element_wise is True, then callable signature should1831            be:1832            ``Callable[Any, bool]`` where the ``Any`` input is a scalar element1833            in the column. Otherwise, the input is assumed to be a1834            pandas.Series object.1835        :param index: specify the datatypes and properties of the index.1836        :param nullable: Whether or not column can contain null values.1837        :param unique: Whether or not column can contain duplicate1838            values.1839        :param allow_duplicates: Whether or not column can contain duplicate1840            values.1841        .. warning::1842            This option will be deprecated in 0.8.0. Use the ``unique``1843            argument instead.1844        :param coerce: If True, when schema.validate is called the column will1845            be coerced into the specified dtype. This has no effect on columns1846            where ``pandas_dtype=None``.1847        :param name: series name.1848        :param pandas_dtype: alias of ``dtype`` for backwards compatibility.1849        :param title: A human-readable label for the series.1850        :param description: An arbitrary textual description of the series.1851            .. warning:: This option will be deprecated in 0.8.01852        """1853        super().__init__(1854            dtype,1855            checks,1856            nullable,1857            unique,1858            allow_duplicates,1859            coerce,1860            name,1861            pandas_dtype,1862            title,1863            description,1864        )1865        self.index = index1866    @property1867    def _allow_groupby(self) -> bool:1868        """Whether the schema or schema component allows groupby operations."""1869        return False1870    def validate(1871        self,1872        check_obj: pd.Series,1873        head: Optional[int] = None,1874        tail: Optional[int] = None,1875        sample: Optional[int] = None,1876        random_state: Optional[int] = None,1877        lazy: bool = False,1878        inplace: bool = False,1879    ) -> pd.Series:1880        """Validate a Series object.1881        :param check_obj: One-dimensional ndarray with axis labels1882            (including time series).1883        :param head: validate the first n rows. Rows overlapping with `tail` or1884            `sample` are de-duplicated.1885        :param tail: validate the last n rows. Rows overlapping with `head` or1886            `sample` are de-duplicated.1887        :param sample: validate a random sample of n rows. Rows overlapping1888            with `head` or `tail` are de-duplicated.1889        :param random_state: random seed for the ``sample`` argument.1890        :param lazy: if True, lazily evaluates dataframe against all validation1891            checks and raises a ``SchemaErrors``. Otherwise, raise1892            ``SchemaError`` as soon as one occurs.1893        :param inplace: if True, applies coercion to the object of validation,1894            otherwise creates a copy of the data.1895        :returns: validated Series.1896        :raises SchemaError: when ``DataFrame`` violates built-in or custom1897            checks.1898        :example:1899        >>> import pandas as pd1900        >>> import pandera as pa1901        >>>1902        >>> series_schema = pa.SeriesSchema(1903        ...     float, [1904        ...         pa.Check(lambda s: s > 0),1905        ...         pa.Check(lambda s: s < 1000),1906        ...         pa.Check(lambda s: s.mean() > 300),1907        ...     ])1908        >>> series = pd.Series([1, 100, 800, 900, 999], dtype=float)1909        >>> print(series_schema.validate(series))1910        0      1.01911        1    100.01912        2    800.01913        3    900.01914        4    999.01915        dtype: float641916        """1917        if not check_utils.is_field(check_obj):1918            raise TypeError(f"expected pd.Series, got {type(check_obj)}")1919        if hasattr(check_obj, "dask"):1920            # special case for dask series1921            if inplace:1922                check_obj = check_obj.pandera.add_schema(self)1923            else:1924                check_obj = check_obj.copy()1925            check_obj = check_obj.map_partitions(1926                self._validate,1927                head=head,1928                tail=tail,1929                sample=sample,1930                random_state=random_state,1931                lazy=lazy,1932                inplace=inplace,1933                meta=check_obj,1934            )1935            return check_obj.pandera.add_schema(self)1936        return self._validate(1937            check_obj=check_obj,1938            head=head,1939            tail=tail,1940            sample=sample,1941            random_state=random_state,1942            lazy=lazy,1943            inplace=inplace,1944        )1945    def _validate(1946        self,1947        check_obj: pd.Series,1948        head: Optional[int] = None,1949        tail: Optional[int] = None,1950        sample: Optional[int] = None,1951        random_state: Optional[int] = None,1952        lazy: bool = False,1953        inplace: bool = False,1954    ) -> pd.Series:1955        if not inplace:1956            check_obj = check_obj.copy()1957        if hasattr(check_obj, "pandera"):1958            check_obj = check_obj.pandera.add_schema(self)1959        error_handler = SchemaErrorHandler(lazy=lazy)1960        if self.coerce:1961            try:1962                check_obj = self.coerce_dtype(check_obj)1963                if hasattr(check_obj, "pandera"):1964                    check_obj = check_obj.pandera.add_schema(self)1965            except errors.SchemaError as exc:1966                error_handler.collect_error("dtype_coercion_error", exc)1967        # validate index1968        if self.index:1969            # coerce data type using index schema copy to prevent mutation1970            # of original index schema attribute.1971            _index = copy.deepcopy(self.index)1972            _index.coerce = _index.coerce or self.coerce1973            try:1974                check_obj = _index(1975                    check_obj, head, tail, sample, random_state, lazy, inplace1976                )1977            except errors.SchemaError as exc:1978                error_handler.collect_error("dtype_coercion_error", exc)1979            except errors.SchemaErrors as err:1980                for schema_error_dict in err.schema_errors:1981                    error_handler.collect_error(1982                        "index_check", schema_error_dict["error"]1983                    )1984        # validate series1985        try:1986            super().validate(1987                check_obj, head, tail, sample, random_state, lazy, inplace1988            )1989        except errors.SchemaErrors as err:1990            for schema_error_dict in err.schema_errors:1991                error_handler.collect_error(1992                    "series_check", schema_error_dict["error"]1993                )1994        if error_handler.collected_errors:1995            raise errors.SchemaErrors(1996                error_handler.collected_errors, check_obj1997            )1998        return check_obj1999    def __call__(2000        self,2001        check_obj: pd.Series,2002        head: Optional[int] = None,2003        tail: Optional[int] = None,2004        sample: Optional[int] = None,2005        random_state: Optional[int] = None,2006        lazy: bool = False,2007        inplace: bool = False,2008    ) -> pd.Series:2009        """Alias for :func:`SeriesSchema.validate` method."""2010        return self.validate(2011            check_obj, head, tail, sample, random_state, lazy, inplace2012        )2013    def __eq__(self, other):2014        return self.__dict__ == other.__dict__2015def _pandas_obj_to_validate(2016    dataframe_or_series: Union[pd.DataFrame, pd.Series],2017    head: Optional[int],2018    tail: Optional[int],2019    sample: Optional[int],2020    random_state: Optional[int],2021) -> Union[pd.DataFrame, pd.Series]:2022    pandas_obj_subsample = []2023    if head is not None:2024        pandas_obj_subsample.append(dataframe_or_series.head(head))2025    if tail is not None:2026        pandas_obj_subsample.append(dataframe_or_series.tail(tail))2027    if sample is not None:2028        pandas_obj_subsample.append(2029            dataframe_or_series.sample(sample, random_state=random_state)...
Automation Testing Tutorials

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