If you’re interested in helping out, all open tasks are listed the GitHub Issues tab. The issues tagged with first issue are a good place to start if your new to the project or new to open source projects.

If you’re interested in a new major feature, please feel free to reach out to me

Bug reports

The best bug reports are Pull Requests. The second best bug reports are new issues on this repo.


This framework uses unittest for unit testing. Tests can be run by calling:

cd tests/

python -m unittest discover -s . -t .

Style guide

This codebase should follow Google’s Python Style Guide.


If you’ve changed any code, update the changelog on

Generating documentation

This codebase uses sphinx’s autodoc feature. To generate new documentation, to reflect updated documentation, run:

cd docs

make html

Adding new data types

If there’s a specific datatype you’d like to use that’s not built in (such as images, videos, or geospatial), you can include it by using Automater’s datatype_handlers parameter.

A template datatype can be found in keras_pandas/data_types/ Filling out this template will yield a new
datatype handler. If you’re happy with your work and want to share your new datatype handler, create a PR.

To create add a new datatype:

  • Create a new .py file in keras_pandas/data_types, based on keras_pandas/data_types/ (and perhaps referencing keras_pandas/data_types/
  • Fill out your new datatype’s .py file
  • Create a new test class for your new datatype (perhaps based on tests/ and / or tests/
  • Add the new datatype to keras_pandas/Automater.datatype_handlers, in keras_pandas/Automater.__init__()
  • Add the new datatype to docs/index.rst, in autosummary list

Adding new examples

To contribute a new example

  • Add data loader method to keras_pandas/ (perhaps in the style of load_titanic())
  • Add a new .py file under examples (perhaps by copying and pasting
  • Implement the required steps
  • Add the new file to tests/
  • Add the new example to examples/