%global _empty_manifest_terminate_build 0
Name: python-pandera
Version: 0.14.5
Release: 1
Summary: A light-weight and flexible data validation and testing tool for statistical data objects.
License: MIT
URL: https://github.com/pandera-dev/pandera
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/7b/09/ce690eb6248a37a773e975998fd291e3094c2410649a61ac0c3378814e50/pandera-0.14.5.tar.gz
BuildArch: noarch
Requires: python3-multimethod
Requires: python3-numpy
Requires: python3-packaging
Requires: python3-pandas
Requires: python3-pydantic
Requires: python3-typing-inspect
Requires: python3-wrapt
Requires: python3-typing-extensions
Requires: python3-black
Requires: python3-pandas-stubs
Requires: python3-fastapi
Requires: python3-ray
Requires: python3-dask
Requires: python3-geopandas
Requires: python3-pyspark
Requires: python3-scipy
Requires: python3-pyyaml
Requires: python3-shapely
Requires: python3-modin
Requires: python3-frictionless
Requires: python3-hypothesis
Requires: python3-dask
Requires: python3-fastapi
Requires: python3-geopandas
Requires: python3-shapely
Requires: python3-scipy
Requires: python3-pyyaml
Requires: python3-black
Requires: python3-frictionless
Requires: python3-modin
Requires: python3-ray
Requires: python3-dask
Requires: python3-modin
Requires: python3-dask
Requires: python3-modin
Requires: python3-ray
Requires: python3-pandas-stubs
Requires: python3-pyspark
Requires: python3-hypothesis
%description
# A Statistical Data Testing Toolkit
*A data validation library for scientists, engineers, and analysts seeking
correctness.*
[](https://github.com/pandera-dev/pandera/actions?query=workflow%3A%22CI+Tests%22+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/pandera-dev/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` 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), [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. 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 on ReadTheDocs: https://pandera.readthedocs.io
## Install
Using pip:
```
pip install pandera
```
Using conda:
```
conda install -c conda-forge pandera
```
### Extras
Installing additional functionality:
pip
```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
```
conda
```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
```
## 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: 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 [](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.
## Alternative Data Validation Libraries
Here are a few other alternatives for validating Python data structures.
**Generic Python object data validation**
- [voloptuous](https://github.com/alecthomas/voluptuous)
- [schema](https://github.com/keleshev/schema)
**`pandas`-specific data validation**
- [opulent-pandas](https://github.com/danielvdende/opulent-pandas)
- [PandasSchema](https://github.com/TMiguelT/PandasSchema)
- [pandas-validator](https://github.com/c-data/pandas-validator)
- [table_enforcer](https://github.com/xguse/table_enforcer)
- [dataenforce](https://github.com/CedricFR/dataenforce)
- [strictly typed pandas](https://github.com/nanne-aben/strictly_typed_pandas)
- [marshmallow-dataframe](https://github.com/facultyai/marshmallow-dataframe)
**Other tools for data validation**
- [great_expectations](https://github.com/great-expectations/great_expectations)
- [frictionless schema](https://framework.frictionlessdata.io/docs/guides/framework/schema-guide/)
## 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@pandera.ci)
%package -n python3-pandera
Summary: A light-weight and flexible data validation and testing tool for statistical data objects.
Provides: python-pandera
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-pandera
# A Statistical Data Testing Toolkit
*A data validation library for scientists, engineers, and analysts seeking
correctness.*
[](https://github.com/pandera-dev/pandera/actions?query=workflow%3A%22CI+Tests%22+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/pandera-dev/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` 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), [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. 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 on ReadTheDocs: https://pandera.readthedocs.io
## Install
Using pip:
```
pip install pandera
```
Using conda:
```
conda install -c conda-forge pandera
```
### Extras
Installing additional functionality:
pip
```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
```
conda
```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
```
## 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: 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 [](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.
## Alternative Data Validation Libraries
Here are a few other alternatives for validating Python data structures.
**Generic Python object data validation**
- [voloptuous](https://github.com/alecthomas/voluptuous)
- [schema](https://github.com/keleshev/schema)
**`pandas`-specific data validation**
- [opulent-pandas](https://github.com/danielvdende/opulent-pandas)
- [PandasSchema](https://github.com/TMiguelT/PandasSchema)
- [pandas-validator](https://github.com/c-data/pandas-validator)
- [table_enforcer](https://github.com/xguse/table_enforcer)
- [dataenforce](https://github.com/CedricFR/dataenforce)
- [strictly typed pandas](https://github.com/nanne-aben/strictly_typed_pandas)
- [marshmallow-dataframe](https://github.com/facultyai/marshmallow-dataframe)
**Other tools for data validation**
- [great_expectations](https://github.com/great-expectations/great_expectations)
- [frictionless schema](https://framework.frictionlessdata.io/docs/guides/framework/schema-guide/)
## 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@pandera.ci)
%package help
Summary: Development documents and examples for pandera
Provides: python3-pandera-doc
%description help
# A Statistical Data Testing Toolkit
*A data validation library for scientists, engineers, and analysts seeking
correctness.*
[](https://github.com/pandera-dev/pandera/actions?query=workflow%3A%22CI+Tests%22+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/pandera-dev/pandera)
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[](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` 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), [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. 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 on ReadTheDocs: https://pandera.readthedocs.io
## Install
Using pip:
```
pip install pandera
```
Using conda:
```
conda install -c conda-forge pandera
```
### Extras
Installing additional functionality:
pip
```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
```
conda
```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
```
## 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: 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 [](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.
## Alternative Data Validation Libraries
Here are a few other alternatives for validating Python data structures.
**Generic Python object data validation**
- [voloptuous](https://github.com/alecthomas/voluptuous)
- [schema](https://github.com/keleshev/schema)
**`pandas`-specific data validation**
- [opulent-pandas](https://github.com/danielvdende/opulent-pandas)
- [PandasSchema](https://github.com/TMiguelT/PandasSchema)
- [pandas-validator](https://github.com/c-data/pandas-validator)
- [table_enforcer](https://github.com/xguse/table_enforcer)
- [dataenforce](https://github.com/CedricFR/dataenforce)
- [strictly typed pandas](https://github.com/nanne-aben/strictly_typed_pandas)
- [marshmallow-dataframe](https://github.com/facultyai/marshmallow-dataframe)
**Other tools for data validation**
- [great_expectations](https://github.com/great-expectations/great_expectations)
- [frictionless schema](https://framework.frictionlessdata.io/docs/guides/framework/schema-guide/)
## 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@pandera.ci)
%prep
%autosetup -n pandera-0.14.5
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-pandera -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri Apr 21 2023 Python_Bot - 0.14.5-1
- Package Spec generated