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:
- Define a schema once and use it to validate different dataframe types including pandas, dask, modin, and koalas.
- Check the types and
properties of columns in a
DataFrameor values in aSeries. - Perform more complex statistical validation like hypothesis testing.
- Seamlessly integrate with existing data analysis/processing pipelines via function decorators.
- Define schema models with the class-based API with pydantic-style syntax and validate dataframes using the typing syntax.
- Synthesize data from schema objects for property-based testing with pandas data structures.
- Lazily Validate dataframes so that all validation checks are executed before raising an error.
- Integrate with a rich ecosystem of python tools like pydantic and mypy.
The official documentation is hosted on ReadTheDocs: https://pandera.readthedocs.io
Using pip:
pip install pandera
Using conda:
conda install -c conda-forge pandera-core # core library functionality
conda install -c conda-forge pandera # pandera with all extensions
Installing additional 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[mypy] # enable static type-linting of pandas
pip install pandera[dask] # validate dask dataframes
pip install pandera[koalas] # validate koalas 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[all] # all packages
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_1pandera 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)git clone https://github.com/pandera-dev/pandera.git
cd pandera
pip install -r requirements-dev.txt
pip install -e .
pip install pytest
pytest tests
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.
Go here to submit feature requests or bugfixes.
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
- 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_inputandcheck_outputdecorators enable seamless integration with existing code.Checks provide flexibility and performance by providing access topandasAPI by design and offers built-in checks for common data tests.Hypothesisclass provides a tidy-first interface for statistical hypothesis testing.Checks andHypothesisobjects 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.
Here are a few other alternatives for validating Python data structures.
Generic Python object data validation
pandas-specific data validation
- opulent-pandas
- PandasSchema
- pandas-validator
- table_enforcer
- dataenforce
- strictly typed pandas
- marshmallow-dataframe
Other tools for data validation
If you use pandera in the context of academic or industry research, please
consider citing the paper and/or software package.
@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 }
}
pandera is licensed under the MIT license and is written and
maintained by Niels Bantilan ([email protected])