pandera 0.1.4

A light-weight and flexible validation package for pandas data structures.

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A light-weight and flexible validation package for pandas data structures.

Why?

Because pandas data structures hide a lot of information, and explicitly validating them at runtime in production-critical or reproducible research settings is a good idea. It also makes it easier to review pandas code :)

Documentation

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

Install

Using pip:

pip install pandera

Using conda:

conda install -c cosmicbboy pandera

Example Usage

DataFrameSchema

import pandas as pd

from pandera import Column, DataFrameSchema, Float, Int, String, Check


# validate columns
schema = DataFrameSchema({
    # the check function expects a series argument and should output a boolean
    # or a boolean Series.
    "column1": Column(Int, Check(lambda s: s <= 10)),
    "column2": Column(Float, Check(lambda s: s < -1.2)),
    # you can provide a list of validators
    "column3": Column(String, [
        Check(lambda s: s.str.startswith("value_")),
        Check(lambda s: s.str.split("_", expand=True).shape[1] == 2)
    ]),
})

# alternatively, you can pass strings representing the legal pandas datatypes:
# http://pandas.pydata.org/pandas-docs/stable/basics.html#dtypes
schema = DataFrameSchema({
    "column1": Column("int64", Check(lambda s: s <= 10)),
    ...
})

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"]
})

validated_df = schema.validate(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

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.