A library for defensive data analysis.
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Latest commit e7ea040 May 21, 2018



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A python package for defensive data analysis. Documentation is at readthedocs.


  • pandas

Supports python 2.7+ and 3.4+


Data are messy. But, our analysis often depends on 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.

This is especially important when working with flat files like CSV that aren't bound for a more structured destination (e.g. SQL or HDF5).


There are two main ways of using the library, which correspond to the two main ways I use pandas: writing small scripts or interactively at the interpreter.

First, as decorators, which are most useful in .py scripts The basic idea is to write each step of your ETL process as a function that takes and returns a DataFrame. These functions can be decorated with the invariants that should be true at that step in the process.

from engarde.decorators import none_missing, unique_index, is_shape

def f(df1, df2):
    return df1.add(df2)

@is_shape((1290, 10))
def make_design_matrix('data.csv'):
    out = ...
    return out

Second, interactively. The cleanest way to integrate this is through the pipe method, introduced in pandas 0.16.2 (June 2015).

>>> import engarde.checks as dc
>>> (df1.reindex_like(df2)
...     .pipe(dc.unique_index)
...     .cumsum()
...     .pipe(dc.within_range, (0, 100))
... )

See Also