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

pandera provides a flexible and expressive API for performing data validation on dataframes to make data processing pipelines more readable and robust.

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

1. Define a schema once and use it to validate different dataframe types including pandas, dask, modin, and koalas.
2. Check the types and properties of columns in a DataFrame or values in a Series.
3. Perform more complex statistical validation like hypothesis testing.
4. Seamlessly integrate with existing data analysis/processing pipelines via function decorators.
5. Define schema models with the class-based API with pydantic-style syntax and validate dataframes using the typing syntax.
6. Synthesize data from schema objects for property-based testing with pandas data structures.
7. Lazily Validate dataframes so that all validation checks are executed before raising an error.
8. Integrate with a rich ecosystem of python tools like pydantic and mypy.

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.

Need Help?

There are many ways of getting help with your questions. You can ask a question on Github Discussions page or reach out to the maintainers and pandera community on Discord

Why pandera?

• dataframe-centric data types, column nullability, and uniqueness are first-class concepts.
• Define schema models with the class-based API with pydantic-style syntax and validate dataframes using the typing syntax.
• 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.
• Use schemas as generative contracts to synthesize data for unit testing.
• Schema inference allows you to bootstrap schemas from data.

Alternative 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
• dataenforce
• strictly typed pandas
• marshmallow-dataframe

Other tools for data validation

• great_expectations
• frictionless schema

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 ([email protected])