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
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 .
This codebase should follow Google's Python Style Guide.
If you've changed any code, update the changelog on README.md
This codebase uses sphinx's autodoc feature. To generate new documentation, to reflect updated documentation, run:
cd docs
make html
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/Abstract.py
. 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 inkeras_pandas/data_types
, based onkeras_pandas/data_types/Abstract.py
(and perhaps referencingkeras_pandas/data_types/Numerical.py
) - Fill out your new datatype's
.py
file - Create a new test class for your new datatype (perhaps based on
tests/testDatatypeTemplate.py
and / ortests/testNumerical.py
) - Add the new datatype to
keras_pandas/Automater.datatype_handlers
, inkeras_pandas/Automater.__init__()
- Add the new datatype to
docs/index.rst
, inautosummary list
To contribute a new example
- Add data loader method to
keras_pandas/lib.py
(perhaps in the style ofload_titanic()
) - Add a new
.py
file underexamples
(perhaps by copying and pastingexample_interface.py
) - Implement the required steps
- Add the new file to
tests/testExamples.py
- Add the new example to
examples/README.md