Engarde is no longer under active developement. Take a look at the bulwark tool for similar functionality.

Engarde

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

Dependencies

• pandas

Supports python 2.7+ and 3.4+

Why?

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).

Examples

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

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

@is_shape((1290, 10))
@unique_index
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

• assertr
• Validada