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pandera documentation

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pandera documentation

Introduction

Welcome to Pandera
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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.Float
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  - pandera.dtypes.Float128
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  - pandera.dtypes.Int
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  - pandera.dtypes.UInt
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  - pandera.dtypes.UInt64
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  - pandera.dtypes.Complex
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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.Geometry
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  - pandera.engines.polars_engine.Int8
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  - pandera.engines.polars_engine.Float32
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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.Null
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  - pandera.engines.polars_engine.Object
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  - pandera.dtypes.is_subdtype
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  - pandera.dtypes.is_complex
  - pandera.dtypes.is_numeric
  - pandera.dtypes.is_bool
  - pandera.dtypes.is_string
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  - 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
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- 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_
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  - 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
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  - pandera.typing
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    - pandera.typing.fastapi.UploadFile
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    - 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.engines.pandas_engine.UINT64.coerce¶

UINT64.coerce(data_container)[source]¶

Pure coerce without catching exceptions.

Return type:: Union[Series, DataFrame]

pandera.engines.pandas_engine.UINT64.coerce_value

pandera.engines.pandas_engine.UINT64.check

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pandera.engines.pandas_engine.UINT64.coerce
- UINT64.coerce()