ENH: snapshot testing of dataframes

Is your feature request related to a problem?

When testing dataframe manipulating functions defining explicit expected dataframes takes a lot of time.

Describe the solution you’d like

I’ve used snapshottest to test GraphQl APIs. The same approach could be implemented in a pytest plugin for pandas as well.

API breaking implications

[this should provide a description of how this feature will affect the API]

Describe alternatives you’ve considered

[this should provide a description of any alternative solutions or features you’ve considered]

Additional context

[add any other context, code examples, or references to existing implementations about the feature request here]

# Your code here, if applicable

[clintmod]

It’s worth noting that this works pretty well with pytest and snapshottest for a dataframe:

def test_something_works(snapshot): # snapshot is a pytest fixture from snapshottest
    data_frame = calc_something_and_return_pandas_dataframe()
    snapshot.assert_match(data_frame.to_csv(index=False), 'some_module_level_unique_name_for_the_snapshot')

[fkromer]

@clintmod Yepp. The problem is that .csv does not save the df meta data (types of rows/columns, etc.) and that multi rows/columnns are tricky. Instead I usually store the data into .h5 instead. In any case you have to work with files which are not easy to read and diff. But right now I’m totally fine with implementing it this way.