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A data validation library for scientists, engineers, and analysts seeking correctness.

pandas data structures contain information that pandera explicitly validates at runtime. This is useful in production-critical or reproducible research settings. With pandera, you can:

1. Check the types and properties of columns in a DataFrame or values in a Series.
2. Perform more complex statistical validation like hypothesis testing.
3. Seamlessly integrate with existing data analysis/processing pipelines via function decorators.
4. Define schema models with the class-based API with pydantic-style syntax and validate dataframes using the typing syntax.
5. Synthesize data from schema objects for property-based testing with pandas data structures.

pandera provides a flexible and expressive API for performing data validation on tidy (long-form) and wide data to make data processing pipelines more readable and robust.

Documentation

The official documentation is hosted on ReadTheDocs: https://pandera.readthedocs.io

Install

Using pip:

pip install pandera

Installing optional functionality:

pip install pandera[hypotheses]  # hypothesis checks
pip install pandera[io]          # yaml/script schema io utilities
pip install pandera[strategies]  # data synthesis strategies
pip install pandera[all]         # all packages

Using conda:

conda install -c conda-forge pandera-core  # core library functionality
conda install -c conda-forge pandera       # pandera with all extensions

Quick Start

import pandas as pd
import pandera as pa


# data to validate
df = pd.DataFrame({
    "column1": [1, 4, 0, 10, 9],
    "column2": [-1.3, -1.4, -2.9, -10.1, -20.4],
    "column3": ["value_1", "value_2", "value_3", "value_2", "value_1"]
})

# define schema
schema = pa.DataFrameSchema({
    "column1": pa.Column(int, checks=pa.Check.le(10)),
    "column2": pa.Column(float, checks=pa.Check.lt(-1.2)),
    "column3": pa.Column(str, checks=[
        pa.Check.str_startswith("value_"),
        # define custom checks as functions that take a series as input and
        # outputs a boolean or boolean Series
        pa.Check(lambda s: s.str.split("_", expand=True).shape[1] == 2)
    ]),
})

validated_df = schema(df)
print(validated_df)

#     column1  column2  column3
#  0        1     -1.3  value_1
#  1        4     -1.4  value_2
#  2        0     -2.9  value_3
#  3       10    -10.1  value_2
#  4        9    -20.4  value_1

Schema Model

pandera also provides an alternative API for expressing schemas inspired by dataclasses and pydantic. The equivalent SchemaModel for the above DataFrameSchema would be:

from pandera.typing import Series

class Schema(pa.SchemaModel):

    column1: Series[int] = pa.Field(le=10)
    column2: Series[float] = pa.Field(lt=-1.2)
    column3: Series[str] = pa.Field(str_startswith="value_")

    @pa.check("column3")
    def column_3_check(cls, series: Series[str]) -> Series[bool]:
        """Check that values have two elements after being split with '_'"""
        return series.str.split("_", expand=True).shape[1] == 2

Schema.validate(df)

Development Installation

git clone https://github.com/pandera-dev/pandera.git
cd pandera
pip install -r requirements-dev.txt
pip install -e .

Tests

pip install pytest
pytest tests

Contributing to pandera

All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome.

A detailed overview on how to contribute can be found in the contributing guide on GitHub.

Issues

Go here to submit feature requests or bugfixes.

Other Data Validation Libraries

Here are a few other alternatives for validating Python data structures.

Generic Python object data validation

• voloptuous
• schema

pandas-specific data validation

• opulent-pandas
• PandasSchema
• pandas-validator
• table_enforcer

Other tools for data validation

• great_expectations

Why pandera?

• pandas-centric data types, column nullability, and uniqueness are first-class concepts.
• check_input and check_output decorators enable seamless integration with existing code.
• Checks provide flexibility and performance by providing access to pandas API by design and offers built-in checks for common data tests.
• Hypothesis class provides a tidy-first interface for statistical hypothesis testing.
• Checks and Hypothesis objects support both tidy and wide data validation.
• Comprehensive documentation on key functionality.

How to Cite

If you use pandera in the context of academic or industry research, please consider citing the paper and/or software package.

Paper

@InProceedings{ niels_bantilan-proc-scipy-2020,
  author    = { {N}iels {B}antilan },
  title     = { pandera: {S}tatistical {D}ata {V}alidation of {P}andas {D}ataframes },
  booktitle = { {P}roceedings of the 19th {P}ython in {S}cience {C}onference },
  pages     = { 116 - 124 },
  year      = { 2020 },
  editor    = { {M}eghann {A}garwal and {C}hris {C}alloway and {D}illon {N}iederhut and {D}avid {S}hupe },
  doi       = { 10.25080/Majora-342d178e-010 }
}

Software Package

License and Credits

pandera is licensed under the MIT license and is written and maintained by Niels Bantilan (niels@pandera.ci)