Engarde!

Engarde is a package for defensive data analysis. Engarde supports python 2.7+ and python 3.4+.

Why?

The raison d’être for engarde is the fact of life that data are messy. To do our analysis, we often have certain assumptions about our data that should be invariant across updates to your dataset. Engarde is a lightweight way to explicitly state your assumptions and check that they’re actually true.

@is_shape(-1, 10)
@is_monotonic(strict=True)
@none_missing()
def compute(df):
    # complex operations to determine result
    ...
    return result

We state our assumptions as decorators, and verify that they are true upon the result of the function.

Usage

There are two main ways to use engarde, depending on whether you’re working interactively or not. For interactive use, I’d suggest using DataFrame.pipe to run the check. For non-interactive use, each of the checks are wrapped into a decorator. You can decorate the functions that makeup your ETL pipeline with the checks that should hold true at that stage in the pipeline. Checkout Example to see engarde in action.