Awesome

Wimsey 🔍

A lightweight, flexible and fully open-source data contract library.

• 🐋 Bring your own dataframe library: Built on top of Narwhals so your tests are carried out natively in your own dataframe library (including Pandas, Polars, Dask, CuDF, Rapids, Arrow and Modin)
• 🎍 Bring your own contract format: Write contracts in yaml, json or python - whichever you prefer!
• 🪶 Ultra Lightweight: Built for fast imports and minimal overwhead with only two dependencies (Narwhals and FSSpec)
• 🥔 Simple, easy API: Low mental overheads with two simple functions for testing dataframes, and a simple dataclass for results.

Check out the handy test catalogue and quick start guide

What is a data contract?

As well as being a good buzzword to mention at your next data event, data contracts are a good way of testing data values at boundary points. Ideally, all data would be usable when you recieve it, but you probably already have figured that's not always the case.

A data contract is an expression of what should be true of some data - we might want to check that the only columns that exist are first_name, last_name and rating, or we might want to check that rating is a number less than 10.

Wimsey let's you write contracts in json, yaml or python, here's how the above checks would look in yaml:

- test: columns_should
  be:
    - first_name
    - last_name
    - rating
- column: rating
  test: max_should
  be_less_than_or_equal_to: 10

Wimsey then can execute tests for you in a couple of ways, validate - which will throw an error if tests fail, and otherwise pass back your dataframe - and test, which will give you a detailed run down of individual test success and fails.

Validate is designed to work nicely with polars or pandas pipe methods as a handy guard:

import polars as pl
import wimsey

df = (
  pl.read_csv("hopefully_nice_data.csv")
  .pipe(wimsey.validate, "tests.json")
  .group_by("name").agg(pl.col("value").sum())
)

Test is a single function call, returning a FinalResult data-type:

import pandas as pd
import wimsey

df = pd.read_csv("hopefully_nice_data.csv")
results = wimsey.test(df, "tests.yaml")

if results.success:
  print("Yay we have good data! 🥳")
else:
  print(f"Oh nooo, something's up! 😭")
  print([i for i in results.results if not i.success])

Roadmap, Contributing & Feedback

Wimsey is very new! There's a lot more to come soon in the form of additional available data tests, better test coverage, performance improvements and friendly error messages. Once the fundamentals are polished, next up is developing a handy API for "data profiling" (generate minimal tests from a sample of data).

Wimsey is ready to mingle! If you have ideas or feedback, including additional tests you'd want to see, please feel free to raise an issue or submit a pull request.