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pandera documentation

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pandera documentation

Introduction

Welcome to Pandera
▶️ Try Pandera
Official Website

User Guide

DataFrame Schemas
DataFrame Models
Series Schemas
Data Type Validation
Validating with Checks
Hypothesis Testing
Preprocessing with Parsers
Pandera Data Types
Decorators for Pipeline Integration
Dropping Invalid Rows
Schema Inference
Lazy Validation
Error Reports
Data Synthesis Strategies
Extensions
Data Format Conversion
Supported DataFrame Libraries
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- GeoPandas
- Dask
- Fugue
- Modin
- Polars
- Pyspark Pandas
- Pyspark SQL
Integrations
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- FastAPI
- Frictionless
- Hypothesis
- Mypy
- Pydantic
Configuration

Reference

API
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- Core
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- Data Types
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  - pandera.dtypes.DataType
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  - pandera.dtypes.Bool
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  - pandera.dtypes.Timestamp
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  - pandera.dtypes.DateTime
  - pandera.dtypes.Timedelta
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  - pandera.dtypes.Category
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  - pandera.dtypes.Float
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  - pandera.dtypes.Float16
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  - pandera.dtypes.Float32
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  - pandera.dtypes.Float64
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  - pandera.dtypes.Float128
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  - pandera.dtypes.Int
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  - pandera.dtypes.Int8
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  - pandera.dtypes.Int16
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  - pandera.dtypes.Int32
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  - pandera.dtypes.Int64
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  - pandera.dtypes.UInt
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  - pandera.dtypes.UInt8
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  - pandera.dtypes.UInt16
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  - pandera.dtypes.UInt32
    Toggle navigation of pandera.dtypes.UInt32
  - pandera.dtypes.UInt64
    Toggle navigation of pandera.dtypes.UInt64
  - pandera.dtypes.Complex
    Toggle navigation of pandera.dtypes.Complex
  - pandera.dtypes.Complex64
    Toggle navigation of pandera.dtypes.Complex64
  - pandera.dtypes.Complex128
    Toggle navigation of pandera.dtypes.Complex128
  - pandera.dtypes.Complex256
    Toggle navigation of pandera.dtypes.Complex256
  - pandera.dtypes.Decimal
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  - pandera.dtypes.String
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  - pandera.engines.pandas_engine.BOOL
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  - pandera.engines.pandas_engine.INT8
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  - pandera.engines.pandas_engine.INT16
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  - pandera.engines.pandas_engine.INT32
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  - pandera.engines.pandas_engine.INT64
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  - pandera.engines.pandas_engine.UINT8
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  - pandera.engines.pandas_engine.UINT16
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  - pandera.engines.pandas_engine.UINT32
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  - pandera.engines.pandas_engine.UINT64
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  - pandera.engines.pandas_engine.STRING
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  - pandera.engines.numpy_engine.Object
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  - pandera.engines.pandas_engine.DateTime
  - pandera.engines.pandas_engine.Date
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  - pandera.engines.pandas_engine.Decimal
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  - pandera.engines.pandas_engine.Category
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  - pandera.engines.pandas_engine.Geometry
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  - pandera.engines.pandas_engine.PydanticModel
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  - pandera.engines.polars_engine.Int8
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  - pandera.engines.polars_engine.Int16
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  - pandera.engines.polars_engine.Int32
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  - pandera.engines.polars_engine.Int64
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  - pandera.engines.polars_engine.UInt8
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  - pandera.engines.polars_engine.UInt16
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  - pandera.engines.polars_engine.UInt32
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  - pandera.engines.polars_engine.UInt64
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  - pandera.engines.polars_engine.Float32
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  - pandera.engines.polars_engine.Float64
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  - pandera.engines.polars_engine.Decimal
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  - pandera.engines.polars_engine.Date
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  - pandera.engines.polars_engine.DateTime
  - pandera.engines.polars_engine.Time
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  - pandera.engines.polars_engine.Timedelta
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  - pandera.engines.polars_engine.Array
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  - pandera.engines.polars_engine.List
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  - pandera.engines.polars_engine.Struct
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  - pandera.engines.polars_engine.Bool
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  - pandera.engines.polars_engine.String
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  - pandera.engines.polars_engine.Categorical
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  - pandera.engines.polars_engine.Category
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  - pandera.engines.polars_engine.Null
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  - pandera.engines.polars_engine.Object
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  - pandera.dtypes.is_subdtype
  - pandera.dtypes.is_float
  - pandera.dtypes.is_int
  - pandera.dtypes.is_uint
  - pandera.dtypes.is_complex
  - pandera.dtypes.is_numeric
  - pandera.dtypes.is_bool
  - pandera.dtypes.is_string
  - pandera.dtypes.is_datetime
  - pandera.dtypes.is_timedelta
  - pandera.dtypes.immutable
  - pandera.engines.engine.Engine
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  - pandera.engines.numpy_engine.Engine
    Toggle navigation of pandera.engines.numpy_engine.Engine
    - pandera.engines.numpy_engine.Engine.dtype
  - pandera.engines.pandas_engine.Engine
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    - pandera.engines.pandas_engine.Engine.dtype
    - pandera.engines.pandas_engine.Engine.numpy_dtype
- DataFrame Models
  Toggle navigation of DataFrame Models
  - pandera.api.pandas.model.DataFrameModel
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    - pandera.api.pandas.model.DataFrameModel.build_schema_
    - pandera.api.pandas.model.DataFrameModel.to_json_schema
  - pandera.api.polars.model.DataFrameModel
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    - pandera.api.polars.model.DataFrameModel.build_schema_
    - pandera.api.polars.model.DataFrameModel.to_json_schema
  - pandera.api.pyspark.model.DataFrameModel
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  - pandera.api.dataframe.model_components.Field
  - pandera.api.dataframe.model_components.check
  - pandera.api.dataframe.model_components.dataframe_check
  - pandera.api.dataframe.model_components.parser
  - pandera.api.dataframe.model_components.dataframe_parser
  - pandera.typing
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    - pandera.typing.fastapi.UploadFile
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      - pandera.typing.fastapi.UploadFile.__init__
      - pandera.typing.fastapi.UploadFile.pydantic_validate_v2
    - pandera.typing.formats.Formats
  - pandera.api.pandas.model_config.BaseConfig
  - pandera.api.polars.model_config.BaseConfig
  - pandera.api.pyspark.model_config.BaseConfig
- Decorators
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- Schema Inference
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  - pandera.schema_inference.pandas.infer_schema
- IO Utilities
  Toggle navigation of IO Utilities
- Data Synthesis Strategies
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  - pandera.strategies
- Extensions
  Toggle navigation of Extensions
  - pandera.extensions
- Errors
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Community

Contributing

v: latest

Versions: latest; stable; v0.18.3; v0.18.2; v0.18.1; v0.18.0; v0.17.2; v0.17.1; v0.17.0; v0.16.1; v0.16.0; v0.15.2; v0.14.5; v0.13.4

Downloads

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pandera.dtypes.Complex.coerce_value¶

Complex.coerce_value(value)[source]¶: Coerce an value to a particular type.

pandera.dtypes.Complex.try_coerce

pandera.dtypes.Complex.coerce

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pandera.dtypes.Complex.coerce_value
- Complex.coerce_value()