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ZaxR
185 Stars 26 Forks GNU Lesser General Public License v3.0 136 Commits 22 Opened issues

Description

Bulwark is a package for convenient property-based testing of pandas dataframes.

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Bulwark is a package for convenient property-based testing of pandas dataframes.

Documentation: https://bulwark.readthedocs.io/en/latest/index.html

This project was heavily influenced by the no-longer-supported Engarde library by Tom Augspurger(thanks for the head start, Tom!), which itself was modeled after the R library assertr.

Why?

Data are messy, and pandas is one of the go-to libraries for analyzing tabular data. In the real world, data analysts and scientists often feel like they don't have the time or energy to think of and write tests for their data. Bulwark's goal is to let you check that your data meets your assumptions of what it should look like at any (and every) step in your code, without making you work too hard.

Installation

pip install bulwark

or

conda install -c conda-forge bulwark

Note that the latest version of Bulwark will only be compatible with newer version of Python, Numpy, and Pandas. This is to encourage upgrades that themselves can help minimize bugs, allow Bulwark to take advantage of the latest language/library features, reduce the technical debt of maintaining Bulwark, and to be consistent with Numpy's community version support recommendation in NEP 29. See the table below for officially supported versions:

| Bulwark | Python | Numpy | Pandas | |:-------:|:------:|:------:|:--------:| | 0.6.0 | >=3.6 | >=1.15 | >=0.23.0 | | <=0.5.3 | >=3.5 | >=1.8 | >=0.16.2 | | | | | |

Usage

Bulwark comes with checks for many of the common assumptions you might want to validate for the functions that make up your ETL pipeline, and lets you toss those checks as decorators on the functions you're already writing:

    import bulwark.decorators as dc

@dc.IsShape((-1, 10))
@dc.IsMonotonic(strict=True)
@dc.HasNoNans()
def compute(df):
    # complex operations to determine result
    ...
return result_df

Still want to have more robust test files? Bulwark's got you covered there, too, with importable functions.

    import bulwark.checks as ck

df.pipe(ck.has_no_nans())

Won't I have to go clean up all those decorators when I'm ready to go to production? Nope - just toggle the built-in "enabled" flag available for every decorator.

    @dc.IsShape((3, 2), enabled=False)
    def compute(df):
        # complex operations to determine result
        ...
    return result_df

What if the test I want isn't part of the library? Use the built-in

CustomCheck
to use your own custom function!
    import bulwark.checks as ck
    import bulwark.decorators as dc
    import numpy as np
    import pandas as pd

def len_longer_than(df, l):
    if len(df) &lt;= l:
        raise AssertionError("df is not as long as expected.")
    return df

@dc.CustomCheck(len_longer_than, 10, enabled=False)
def append_a_df(df, df2):
    return df.append(df2, ignore_index=True)

df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
df2 = pd.DataFrame({"a": [1, np.nan, 3, 4], "b": [4, 5, 6, 7]})

append_a_df(df, df2)  # doesn't fail because the check is disabled

What if I want to run a lot of tests and want to see all the errors at once? You can use the built-in

MultiCheck
. It will collect all of the errors and either display a warning message of throw an exception based on the
warn
flag. You can even use custom functions with MultiCheck:
    def len_longer_than(df, l):
        if len(df) <= l:
            raise AssertionError("df is not as long as expected.")
        return df

# `checks` takes a dict of function: dict of params for that function.
# Note that those function params EXCLUDE df.
# Also note that when you use MultiCheck, there's no need to use CustomCheck - just feed in the function.
@dc.MultiCheck(checks={ck.has_no_nans: {"columns": None},
                       len_longer_than: {"l": 6}},
               warn=False)
def append_a_df(df, df2):
    return df.append(df2, ignore_index=True)

df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
df2 = pd.DataFrame({"a": [1, np.nan, 3, 4], "b": [4, 5, 6, 7]})

append_a_df(df, df2)

See examples to see more advanced usage.

Contributing

Bulwark is always looking for new contributors! We work hard to make contributing as easy as possible, and previous open source experience is not required! Please see contributing.md for how to get started.

Thank you to all our past contributors, especially these folks:

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