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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
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  - pandera.dtypes.UInt64
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  - pandera.dtypes.Complex
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  - pandera.dtypes.Complex64
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  - pandera.dtypes.Complex128
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  - pandera.dtypes.Complex256
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  - 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
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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
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    - 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
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  - 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
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- Data Synthesis Strategies
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  - pandera.strategies
- Extensions
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  - 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.Float64.coerce_value¶

Float64.coerce_value(value)[source]¶: Coerce an value to a particular type.

pandera.dtypes.Float64.try_coerce

pandera.dtypes.Float64.coerce

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pandera.dtypes.Float64.coerce_value
- Float64.coerce_value()