<br>
<div align="center"><a href="https://www.union.ai/pandera"><img src="docs/source/_static/pandera-banner.png" width="400"></a></div>
<h1 align="center">
The Open-source Framework for Precision Data Testing
</h1>
<p align="center">
📊 🔎 ✅
</p>
<p align="center">
<i>Data validation for scientists, engineers, and analysts seeking correctness.</i>
</p>
<br>
[](https://github.com/unionai-oss/pandera/actions/workflows/ci-tests.yml?query=branch%3Amain)
[](https://pandera.readthedocs.io/en/stable/?badge=stable)
[](https://pypi.org/project/pandera/)
[](https://pypi.python.org/pypi/)
[](https://github.com/pyOpenSci/software-review/issues/12)
[](https://www.repostatus.org/#active)
[](https://pandera.readthedocs.io/en/latest/?badge=latest)
[](https://codecov.io/gh/unionai-oss/pandera)
[](https://pypi.python.org/pypi/pandera/)
[](https://doi.org/10.5281/zenodo.3385265)
[](https://pandera-dev.github.io/pandera-asv-logs/)
[](https://pepy.tech/project/pandera)
[](https://pepy.tech/project/pandera)
[](https://anaconda.org/conda-forge/pandera)
[](https://discord.gg/vyanhWuaKB)
`pandera` is a [Union.ai](https://union.ai/blog-post/pandera-joins-union-ai) open
source project that provides a flexible and expressive API for performing data
validation on dataframe-like objects 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](https://pandera.readthedocs.io/en/stable/supported_libraries.html)
including [pandas](http://pandas.pydata.org), [polars](https://docs.pola.rs/),
[dask](https://dask.org), [modin](https://modin.readthedocs.io/),
and [pyspark](https://spark.apache.org/docs/3.2.0/api/python/user_guide/pandas_on_spark/index.html).
1. [Check](https://pandera.readthedocs.io/en/stable/checks.html) the types and
properties of columns in a `DataFrame` or values in a `Series`.
1. Perform more complex statistical validation like
[hypothesis testing](https://pandera.readthedocs.io/en/stable/hypothesis.html#hypothesis).
1. [Parse](https://pandera.readthedocs.io/en/stable/parsers.html) data to standardize
the preprocessing steps needed to produce valid data.
1. Seamlessly integrate with existing data analysis/processing pipelines
via [function decorators](https://pandera.readthedocs.io/en/stable/decorators.html#decorators).
1. Define dataframe models with the
[class-based API](https://pandera.readthedocs.io/en/stable/dataframe_models.html#dataframe-models)
with pydantic-style syntax and validate dataframes using the typing syntax.
1. [Synthesize data](https://pandera.readthedocs.io/en/stable/data_synthesis_strategies.html#data-synthesis-strategies)
from schema objects for property-based testing with pandas data structures.
1. [Lazily Validate](https://pandera.readthedocs.io/en/stable/lazy_validation.html)
dataframes so that all validation checks are executed before raising an error.
1. [Integrate](https://pandera.readthedocs.io/en/stable/integrations.html) with
a rich ecosystem of python tools like [pydantic](https://pydantic-docs.helpmanual.io),
[fastapi](https://fastapi.tiangolo.com/), and [mypy](http://mypy-lang.org/).
## Documentation
The official documentation is hosted here: https://pandera.readthedocs.io
## Install
Using pip:
```
pip install pandera
```
Using conda:
```
conda install -c conda-forge pandera
```
### Extras
Installing additional functionality:
<details>
<summary><i>pip</i></summary>
```bash
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[mypy]' # enable static type-linting of pandas
pip install 'pandera[fastapi]' # fastapi integration
pip install 'pandera[dask]' # validate dask dataframes
pip install 'pandera[pyspark]' # validate pyspark dataframes
pip install 'pandera[modin]' # validate modin dataframes
pip install 'pandera[modin-ray]' # validate modin dataframes with ray
pip install 'pandera[modin-dask]' # validate modin dataframes with dask
pip install 'pandera[geopandas]' # validate geopandas geodataframes
pip install 'pandera[polars]' # validate polars dataframes
```
</details>
<details>
<summary><i>conda</i></summary>
```bash
conda install -c conda-forge pandera-hypotheses # hypothesis checks
conda install -c conda-forge pandera-io # yaml/script schema io utilities
conda install -c conda-forge pandera-strategies # data synthesis strategies
conda install -c conda-forge pandera-mypy # enable static type-linting of pandas
conda install -c conda-forge pandera-fastapi # fastapi integration
conda install -c conda-forge pandera-dask # validate dask dataframes
conda install -c conda-forge pandera-pyspark # validate pyspark dataframes
conda install -c conda-forge pandera-modin # validate modin dataframes
conda install -c conda-forge pandera-modin-ray # validate modin dataframes with ray
conda install -c conda-forge pandera-modin-dask # validate modin dataframes with dask
conda install -c conda-forge pandera-geopandas # validate geopandas geodataframes
conda install -c conda-forge pandera-polars # validate polars dataframes
```
</details>
## Quick Start
```python
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
```
## DataFrame Model
`pandera` also provides an alternative API for expressing schemas inspired
by [dataclasses](https://docs.python.org/3/library/dataclasses.html) and
[pydantic](https://pydantic-docs.helpmanual.io/). The equivalent `DataFrameModel`
for the above `DataFrameSchema` would be:
```python
from pandera.typing import Series
class Schema(pa.DataFrameModel):
column1: int = pa.Field(le=10)
column2: float = pa.Field(lt=-1.2)
column3: 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
export PYTHON_VERSION=... # specify desired python version
pip install -r dev/requirements-${PYTHON_VERSION}.txt
pip install -e .
```
## Tests
```
pip install pytest
pytest tests
```
## Contributing to pandera [](https://github.com/pandera-dev/pandera/graphs/contributors)
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](https://github.com/pandera-dev/pandera/blob/main/.github/CONTRIBUTING.md)
on GitHub.
## Issues
Go [here](https://github.com/pandera-dev/pandera/issues) 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](https://github.com/pandera-dev/pandera/discussions/categories/q-a)
page or reach out to the maintainers and pandera community on
[Discord](https://discord.gg/vyanhWuaKB)
## Why `pandera`?
- [dataframe-centric data types](https://pandera.readthedocs.io/en/stable/dtypes.html),
[column nullability](https://pandera.readthedocs.io/en/stable/dataframe_schemas.html#null-values-in-columns),
and [uniqueness](https://pandera.readthedocs.io/en/stable/dataframe_schemas.html#validating-the-joint-uniqueness-of-columns)
are first-class concepts.
- Define [dataframe models](https://pandera.readthedocs.io/en/stable/schema_models.html) with the class-based API with
[pydantic](https://pydantic-docs.helpmanual.io/)-style syntax and validate dataframes using the typing syntax.
- `check_input` and `check_output` [decorators](https://pandera.readthedocs.io/en/stable/decorators.html#decorators-for-pipeline-integration)
enable seamless integration with existing code.
- [`Check`s](https://pandera.readthedocs.io/en/stable/checks.html) provide flexibility and performance by providing access to `pandas`
API by design and offers built-in checks for common data tests.
- [`Hypothesis`](https://pandera.readthedocs.io/en/stable/hypothesis.html) class provides a tidy-first interface for statistical hypothesis
testing.
- `Check`s and `Hypothesis` objects support both [tidy and wide data validation](https://pandera.readthedocs.io/en/stable/checks.html#wide-checks).
- Use schemas as generative contracts to [synthesize data](https://pandera.readthedocs.io/en/stable/data_synthesis_strategies.html) for unit testing.
- [Schema inference](https://pandera.readthedocs.io/en/stable/schema_inference.html) allows you to bootstrap schemas from data.
## 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](https://conference.scipy.org/proceedings/scipy2020/niels_bantilan.html)
```
@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
[](https://doi.org/10.5281/zenodo.3385265)
## License and Credits
`pandera` is licensed under the [MIT license](license.txt) and is written and
maintained by Niels Bantilan (niels@union.ai)
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"description": "<br>\n<div align=\"center\"><a href=\"https://www.union.ai/pandera\"><img src=\"docs/source/_static/pandera-banner.png\" width=\"400\"></a></div>\n\n<h1 align=\"center\">\n The Open-source Framework for Precision Data Testing\n</h1>\n\n<p align=\"center\">\n \ud83d\udcca \ud83d\udd0e \u2705\n</p>\n\n<p align=\"center\">\n <i>Data validation for scientists, engineers, and analysts seeking correctness.</i>\n</p>\n\n<br>\n\n\n[](https://github.com/unionai-oss/pandera/actions/workflows/ci-tests.yml?query=branch%3Amain)\n[](https://pandera.readthedocs.io/en/stable/?badge=stable)\n[](https://pypi.org/project/pandera/)\n[](https://pypi.python.org/pypi/)\n[](https://github.com/pyOpenSci/software-review/issues/12)\n[](https://www.repostatus.org/#active)\n[](https://pandera.readthedocs.io/en/latest/?badge=latest)\n[](https://codecov.io/gh/unionai-oss/pandera)\n[](https://pypi.python.org/pypi/pandera/)\n[](https://doi.org/10.5281/zenodo.3385265)\n[](https://pandera-dev.github.io/pandera-asv-logs/)\n[](https://pepy.tech/project/pandera)\n[](https://pepy.tech/project/pandera)\n[](https://anaconda.org/conda-forge/pandera)\n[](https://discord.gg/vyanhWuaKB)\n\n`pandera` is a [Union.ai](https://union.ai/blog-post/pandera-joins-union-ai) open\nsource project that provides a flexible and expressive API for performing data\nvalidation on dataframe-like objects to make data processing pipelines more readable and robust.\n\nDataframes contain information that `pandera` explicitly validates at runtime.\nThis is useful in production-critical or reproducible research settings. With\n`pandera`, you can:\n\n1. Define a schema once and use it to validate\n [different dataframe types](https://pandera.readthedocs.io/en/stable/supported_libraries.html)\n including [pandas](http://pandas.pydata.org), [polars](https://docs.pola.rs/),\n [dask](https://dask.org), [modin](https://modin.readthedocs.io/),\n and [pyspark](https://spark.apache.org/docs/3.2.0/api/python/user_guide/pandas_on_spark/index.html).\n1. [Check](https://pandera.readthedocs.io/en/stable/checks.html) the types and\n properties of columns in a `DataFrame` or values in a `Series`.\n1. Perform more complex statistical validation like\n [hypothesis testing](https://pandera.readthedocs.io/en/stable/hypothesis.html#hypothesis).\n1. [Parse](https://pandera.readthedocs.io/en/stable/parsers.html) data to standardize\n the preprocessing steps needed to produce valid data.\n1. Seamlessly integrate with existing data analysis/processing pipelines\n via [function decorators](https://pandera.readthedocs.io/en/stable/decorators.html#decorators).\n1. Define dataframe models with the\n [class-based API](https://pandera.readthedocs.io/en/stable/dataframe_models.html#dataframe-models)\n with pydantic-style syntax and validate dataframes using the typing syntax.\n1. [Synthesize data](https://pandera.readthedocs.io/en/stable/data_synthesis_strategies.html#data-synthesis-strategies)\n from schema objects for property-based testing with pandas data structures.\n1. [Lazily Validate](https://pandera.readthedocs.io/en/stable/lazy_validation.html)\n dataframes so that all validation checks are executed before raising an error.\n1. [Integrate](https://pandera.readthedocs.io/en/stable/integrations.html) with\n a rich ecosystem of python tools like [pydantic](https://pydantic-docs.helpmanual.io),\n [fastapi](https://fastapi.tiangolo.com/), and [mypy](http://mypy-lang.org/).\n\n## Documentation\n\nThe official documentation is hosted here: https://pandera.readthedocs.io\n\n\n## Install\n\nUsing pip:\n\n```\npip install pandera\n```\n\nUsing conda:\n\n```\nconda install -c conda-forge pandera\n```\n\n### Extras\n\nInstalling additional functionality:\n\n<details>\n\n<summary><i>pip</i></summary>\n\n```bash\npip install 'pandera[hypotheses]' # hypothesis checks\npip install 'pandera[io]' # yaml/script schema io utilities\npip install 'pandera[strategies]' # data synthesis strategies\npip install 'pandera[mypy]' # enable static type-linting of pandas\npip install 'pandera[fastapi]' # fastapi integration\npip install 'pandera[dask]' # validate dask dataframes\npip install 'pandera[pyspark]' # validate pyspark dataframes\npip install 'pandera[modin]' # validate modin dataframes\npip install 'pandera[modin-ray]' # validate modin dataframes with ray\npip install 'pandera[modin-dask]' # validate modin dataframes with dask\npip install 'pandera[geopandas]' # validate geopandas geodataframes\npip install 'pandera[polars]' # validate polars dataframes\n```\n\n</details>\n\n<details>\n\n<summary><i>conda</i></summary>\n\n```bash\nconda install -c conda-forge pandera-hypotheses # hypothesis checks\nconda install -c conda-forge pandera-io # yaml/script schema io utilities\nconda install -c conda-forge pandera-strategies # data synthesis strategies\nconda install -c conda-forge pandera-mypy # enable static type-linting of pandas\nconda install -c conda-forge pandera-fastapi # fastapi integration\nconda install -c conda-forge pandera-dask # validate dask dataframes\nconda install -c conda-forge pandera-pyspark # validate pyspark dataframes\nconda install -c conda-forge pandera-modin # validate modin dataframes\nconda install -c conda-forge pandera-modin-ray # validate modin dataframes with ray\nconda install -c conda-forge pandera-modin-dask # validate modin dataframes with dask\nconda install -c conda-forge pandera-geopandas # validate geopandas geodataframes\nconda install -c conda-forge pandera-polars # validate polars dataframes\n```\n\n</details>\n\n## Quick Start\n\n```python\nimport pandas as pd\nimport pandera as pa\n\n\n# data to validate\ndf = pd.DataFrame({\n \"column1\": [1, 4, 0, 10, 9],\n \"column2\": [-1.3, -1.4, -2.9, -10.1, -20.4],\n \"column3\": [\"value_1\", \"value_2\", \"value_3\", \"value_2\", \"value_1\"]\n})\n\n# define schema\nschema = pa.DataFrameSchema({\n \"column1\": pa.Column(int, checks=pa.Check.le(10)),\n \"column2\": pa.Column(float, checks=pa.Check.lt(-1.2)),\n \"column3\": pa.Column(str, checks=[\n pa.Check.str_startswith(\"value_\"),\n # define custom checks as functions that take a series as input and\n # outputs a boolean or boolean Series\n pa.Check(lambda s: s.str.split(\"_\", expand=True).shape[1] == 2)\n ]),\n})\n\nvalidated_df = schema(df)\nprint(validated_df)\n\n# column1 column2 column3\n# 0 1 -1.3 value_1\n# 1 4 -1.4 value_2\n# 2 0 -2.9 value_3\n# 3 10 -10.1 value_2\n# 4 9 -20.4 value_1\n```\n\n## DataFrame Model\n\n`pandera` also provides an alternative API for expressing schemas inspired\nby [dataclasses](https://docs.python.org/3/library/dataclasses.html) and\n[pydantic](https://pydantic-docs.helpmanual.io/). The equivalent `DataFrameModel`\nfor the above `DataFrameSchema` would be:\n\n\n```python\nfrom pandera.typing import Series\n\nclass Schema(pa.DataFrameModel):\n\n column1: int = pa.Field(le=10)\n column2: float = pa.Field(lt=-1.2)\n column3: str = pa.Field(str_startswith=\"value_\")\n\n @pa.check(\"column3\")\n def column_3_check(cls, series: Series[str]) -> Series[bool]:\n \"\"\"Check that values have two elements after being split with '_'\"\"\"\n return series.str.split(\"_\", expand=True).shape[1] == 2\n\nSchema.validate(df)\n```\n\n## Development Installation\n\n```\ngit clone https://github.com/pandera-dev/pandera.git\ncd pandera\nexport PYTHON_VERSION=... # specify desired python version\npip install -r dev/requirements-${PYTHON_VERSION}.txt\npip install -e .\n```\n\n## Tests\n\n```\npip install pytest\npytest tests\n```\n\n## Contributing to pandera [](https://github.com/pandera-dev/pandera/graphs/contributors)\n\nAll contributions, bug reports, bug fixes, documentation improvements,\nenhancements and ideas are welcome.\n\nA detailed overview on how to contribute can be found in the\n[contributing guide](https://github.com/pandera-dev/pandera/blob/main/.github/CONTRIBUTING.md)\non GitHub.\n\n## Issues\n\nGo [here](https://github.com/pandera-dev/pandera/issues) to submit feature\nrequests or bugfixes.\n\n## Need Help?\n\nThere are many ways of getting help with your questions. You can ask a question\non [Github Discussions](https://github.com/pandera-dev/pandera/discussions/categories/q-a)\npage or reach out to the maintainers and pandera community on\n[Discord](https://discord.gg/vyanhWuaKB)\n\n## Why `pandera`?\n\n- [dataframe-centric data types](https://pandera.readthedocs.io/en/stable/dtypes.html),\n [column nullability](https://pandera.readthedocs.io/en/stable/dataframe_schemas.html#null-values-in-columns),\n and [uniqueness](https://pandera.readthedocs.io/en/stable/dataframe_schemas.html#validating-the-joint-uniqueness-of-columns)\n are first-class concepts.\n- Define [dataframe models](https://pandera.readthedocs.io/en/stable/schema_models.html) with the class-based API with\n [pydantic](https://pydantic-docs.helpmanual.io/)-style syntax and validate dataframes using the typing syntax.\n- `check_input` and `check_output` [decorators](https://pandera.readthedocs.io/en/stable/decorators.html#decorators-for-pipeline-integration)\n enable seamless integration with existing code.\n- [`Check`s](https://pandera.readthedocs.io/en/stable/checks.html) provide flexibility and performance by providing access to `pandas`\n API by design and offers built-in checks for common data tests.\n- [`Hypothesis`](https://pandera.readthedocs.io/en/stable/hypothesis.html) class provides a tidy-first interface for statistical hypothesis\n testing.\n- `Check`s and `Hypothesis` objects support both [tidy and wide data validation](https://pandera.readthedocs.io/en/stable/checks.html#wide-checks).\n- Use schemas as generative contracts to [synthesize data](https://pandera.readthedocs.io/en/stable/data_synthesis_strategies.html) for unit testing.\n- [Schema inference](https://pandera.readthedocs.io/en/stable/schema_inference.html) allows you to bootstrap schemas from data.\n\n## How to Cite\n\nIf you use `pandera` in the context of academic or industry research, please\nconsider citing the **paper** and/or **software package**.\n\n### [Paper](https://conference.scipy.org/proceedings/scipy2020/niels_bantilan.html)\n\n```\n@InProceedings{ niels_bantilan-proc-scipy-2020,\n author = { {N}iels {B}antilan },\n title = { pandera: {S}tatistical {D}ata {V}alidation of {P}andas {D}ataframes },\n booktitle = { {P}roceedings of the 19th {P}ython in {S}cience {C}onference },\n pages = { 116 - 124 },\n year = { 2020 },\n editor = { {M}eghann {A}garwal and {C}hris {C}alloway and {D}illon {N}iederhut and {D}avid {S}hupe },\n doi = { 10.25080/Majora-342d178e-010 }\n}\n```\n\n### Software Package\n\n[](https://doi.org/10.5281/zenodo.3385265)\n\n\n## License and Credits\n\n`pandera` is licensed under the [MIT license](license.txt) and is written and\nmaintained by Niels Bantilan (niels@union.ai)\n",
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