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How to use get_dataframe_schema_statistics method in pandera

Best Python code snippet using pandera_python

io.py

Source:io.py

...94    }95def _serialize_schema(dataframe_schema):96    """Serialize dataframe schema into into json/yaml-compatible format."""97    from pandera import __version__  # pylint: disable=import-outside-toplevel98    statistics = get_dataframe_schema_statistics(dataframe_schema)99    columns, index, checks = None, None, None100    if statistics["columns"] is not None:101        columns = {102            col_name: _serialize_component_stats(column_stats)103            for col_name, column_stats in statistics["columns"].items()104        }105    if statistics["index"] is not None:106        index = [107            _serialize_component_stats(index_stats)108            for index_stats in statistics["index"]109        ]110    if statistics["checks"] is not None:111        checks = _serialize_dataframe_stats(statistics["checks"])112    return {113        "schema_type": "dataframe",114        "version": __version__,115        "columns": columns,116        "checks": checks,117        "index": index,118        "coerce": dataframe_schema.coerce,119        "strict": dataframe_schema.strict,120        "unique": dataframe_schema.unique,121    }122def _deserialize_check_stats(check, serialized_check_stats, dtype=None):123    def handle_stat_dtype(stat):124        try:125            if pandas_engine.Engine.dtype(dtypes.DateTime).check(dtype):126                return pd.to_datetime(stat, format=DATETIME_FORMAT)127            elif pandas_engine.Engine.dtype(dtypes.Timedelta).check(dtype):128                # serialize to int in nanoseconds129                return pd.to_timedelta(stat, unit="ns")130        except (TypeError, ValueError):131            return stat132        return stat133    if isinstance(serialized_check_stats, dict):134        # handle case where serialized check stats are in the form of a135        # dictionary mapping Check arg names to values.136        check_stats = {}137        for arg, stat in serialized_check_stats.items():138            check_stats[arg] = handle_stat_dtype(stat)139        return check(**check_stats)140    # otherwise assume unary check function signature141    return check(handle_stat_dtype(serialized_check_stats))142def _deserialize_component_stats(serialized_component_stats):143    dtype = serialized_component_stats.get("dtype")144    if dtype:145        dtype = pandas_engine.Engine.dtype(dtype)146    checks = serialized_component_stats.get("checks")147    if checks is not None:148        checks = [149            _deserialize_check_stats(150                getattr(Check, check_name), check_stats, dtype151            )152            for check_name, check_stats in checks.items()153        ]154    return {155        "dtype": dtype,156        "checks": checks,157        **{158            key: serialized_component_stats.get(key)159            for key in [160                "name",161                "nullable",162                "unique",163                # deserialize allow_duplicates property for backwards164                # compatibility. Remove this for 0.8.0 release165                "allow_duplicates",166                "coerce",167                "required",168                "regex",169            ]170            if key in serialized_component_stats171        },172    }173def _deserialize_schema(serialized_schema):174    # pylint: disable=import-outside-toplevel175    from pandera import Index, MultiIndex176    # GH#475177    serialized_schema = serialized_schema if serialized_schema else {}178    if not isinstance(serialized_schema, Mapping):179        raise pandera.errors.SchemaDefinitionError(180            "Schema representation must be a mapping."181        )182    columns = serialized_schema.get("columns")183    index = serialized_schema.get("index")184    checks = serialized_schema.get("checks")185    if columns is not None:186        columns = {187            col_name: Column(**_deserialize_component_stats(column_stats))188            for col_name, column_stats in columns.items()189        }190    if index is not None:191        index = [192            _deserialize_component_stats(index_component)193            for index_component in index194        ]195    if checks is not None:196        # handles unregistered checks by raising AttributeErrors from getattr197        checks = [198            _deserialize_check_stats(getattr(Check, check_name), check_stats)199            for check_name, check_stats in checks.items()200        ]201    if index is None:202        pass203    elif len(index) == 1:204        index = Index(**index[0])205    else:206        index = MultiIndex(207            indexes=[Index(**index_properties) for index_properties in index]208        )209    return DataFrameSchema(210        columns=columns,211        checks=checks,212        index=index,213        coerce=serialized_schema.get("coerce", False),214        strict=serialized_schema.get("strict", False),215        unique=serialized_schema.get("unique", None),216    )217def from_yaml(yaml_schema):218    """Create :class:`~pandera.schemas.DataFrameSchema` from yaml file.219    :param yaml_schema: str or Path to yaml schema, or serialized yaml string.220    :returns: dataframe schema.221    """222    try:223        with Path(yaml_schema).open("r", encoding="utf-8") as f:224            serialized_schema = yaml.safe_load(f)225    except (TypeError, OSError):226        serialized_schema = yaml.safe_load(yaml_schema)227    return _deserialize_schema(serialized_schema)228def to_yaml(dataframe_schema, stream=None):229    """Write :class:`~pandera.schemas.DataFrameSchema` to yaml file.230    :param dataframe_schema: schema to write to file or dump to string.231    :param stream: file stream to write to. If None, dumps to string.232    :returns: yaml string if stream is None, otherwise returns None.233    """234    statistics = _serialize_schema(dataframe_schema)235    def _write_yaml(obj, stream):236        return yaml.safe_dump(obj, stream=stream, sort_keys=False)237    try:238        with Path(stream).open("w", encoding="utf-8") as f:239            _write_yaml(statistics, f)240    except (TypeError, OSError):241        return _write_yaml(statistics, stream)242SCRIPT_TEMPLATE = """243from pandera import (244    DataFrameSchema, Column, Check, Index, MultiIndex245)246schema = DataFrameSchema(247    columns={{{columns}}},248    index={index},249    coerce={coerce},250    strict={strict},251    name={name},252)253"""254COLUMN_TEMPLATE = """255Column(256    dtype={dtype},257    checks={checks},258    nullable={nullable},259    unique={unique},260    coerce={coerce},261    required={required},262    regex={regex},263)264"""265INDEX_TEMPLATE = (266    "Index(dtype={dtype},checks={checks},"267    "nullable={nullable},coerce={coerce},name={name})"268)269MULTIINDEX_TEMPLATE = """270MultiIndex(indexes=[{indexes}])271"""272def _format_checks(checks_dict):273    if checks_dict is None:274        return "None"275    checks = []276    for check_name, check_kwargs in checks_dict.items():277        if check_kwargs is None:278            warnings.warn(279                f"Check {check_name} cannot be serialized. "280                "This check will be ignored"281            )282        else:283            args = ", ".join(284                f"{k}={v.__repr__()}" for k, v in check_kwargs.items()285            )286            checks.append(f"Check.{check_name}({args})")287    return f"[{', '.join(checks)}]"288def _format_index(index_statistics):289    index = []290    for properties in index_statistics:291        dtype = properties.get("dtype")292        index_code = INDEX_TEMPLATE.format(293            dtype=f"{_get_qualified_name(dtype.__class__)}",294            checks=(295                "None"296                if properties["checks"] is None297                else _format_checks(properties["checks"])298            ),299            nullable=properties["nullable"],300            coerce=properties["coerce"],301            name=(302                "None"303                if properties["name"] is None304                else f"\"{properties['name']}\""305            ),306        )307        index.append(index_code.strip())308    if len(index) == 1:309        return index[0]310    return MULTIINDEX_TEMPLATE.format(indexes=",".join(index)).strip()311def _format_script(script):312    formatter = partial(black.format_str, mode=black.FileMode(line_length=80))313    return formatter(script)314def to_script(dataframe_schema, path_or_buf=None):315    """Write :class:`~pandera.schemas.DataFrameSchema` to a python script.316    :param dataframe_schema: schema to write to file or dump to string.317    :param path_or_buf: filepath or buf stream to write to. If None, outputs318        string representation of the script.319    :returns: yaml string if stream is None, otherwise returns None.320    """321    statistics = get_dataframe_schema_statistics(dataframe_schema)322    columns = {}323    for colname, properties in statistics["columns"].items():324        dtype = properties.get("dtype")325        column_code = COLUMN_TEMPLATE.format(326            dtype=(327                None if dtype is None else _get_qualified_name(dtype.__class__)328            ),329            checks=_format_checks(properties["checks"]),330            nullable=properties["nullable"],331            unique=properties["unique"],332            coerce=properties["coerce"],333            required=properties["required"],334            regex=properties["regex"],335        )...

schema_statistics.py

Source:schema_statistics.py

...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        },...

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