How to Contribute

I would be very happy about any kind of contributions that help to improve and extend the functionality of mlxtend.

Quick Contributor Checklist

This is a quick checklist about the different steps of a typical contribution to mlxtend (and other open source projects). Consider copying this list to a local text file (or the issue tracker) and checking off items as you go.

1) Making and testing code changes:

  1. [ ] Open a new "issue" on GitHub to discuss the new feature / bug fix
  2. [ ] Fork the mlxtend repository from GitHub (if not already done earlier)
  3. [ ] Create and check out a new topic branch (please don't make modifications in the master branch)
  4. [ ] Implement the new feature or apply the bug-fix
  5. [ ] Add appropriate unit test functions in mlxtend/*/tests
  6. [ ] Run PYTHONPATH='.' pytest ./mlxtend -sv and make sure that all unit tests pass

  7. [ ] Make sure the newly implemented feature has good test coverage:

python -m pip install coverage
# test all: 
# coverage run --source=mlxtend --branch -m pytest .
coverage run --source=mlxtend --branch -m pytest mlxtend/<insert_path>
coverage html
  1. [ ] Modify documentation in the appropriate location under mlxtend/docs/sources/

  2. [ ] Add a note about the modification/contribution to the ./docs/sources/ file

2) Checking code style:

When you check in a PR, mlxtend will run code style checks via flak8 and black. To make the contributor experience easier, we recommend you check the code style locally before pushing it to the repository. This way it is less likely that the automated checkers will complain and prompt you to make fixes.

There are two ways you can do this:

Option A: Running the tools manually

  1. [ ] Check for style issues by running flake8 ./mlxtend (you may want to run pytest again after you made modifications to the code)
  2. [ ] We recommend using black to format the code automatically according to recommended style changes. After installing black, you can do this via
black [source_file_or_directory]
  1. [ ] Run isort which will sort the imports alphabetically. We recommend the following command:
isort -p mlxtend --line-length 88 --multi-line 3 --profile black

Option B: Using pre-commit hooks (recommended)

The pre-commit hooks for mlxtend will check your code via flake8, black, and isort automatically before you make a git commit. You can read more about pre-commit hooks here.

  1. [ ] Install the pre-commit package via pip install pre-commit.
  2. [ ] In the mlxtend folder, run pre-commit install (you only have to do it once).

3) Submitting your code

  1. [ ] Push the topic branch to the server and create a pull request.
  2. [ ] Check the automated tests passed.
  3. [ ] The automatic PEP8/black integrations may prompt you to modify the code stylistically. It would be nice if you could apply the suggested changes.

Tips for Contributors

Getting Started - Creating a New Issue and Forking the Repository

  • If you don't have a GitHub account, yet, please create one to contribute to this project.
  • Please submit a ticket for your issue to discuss the fix or new feature before too much time and effort is spent for the implementation.

  • Fork the mlxtend repository from the GitHub web interface.

  • Clone the mlxtend repository to your local machine by executing git clone<your_username>/mlxtend.git

Syncing an Existing Fork

If you already forked mlxtend earlier, you can bring you "Fork" up to date with the master branch as follows:

1. Configuring a remote that points to the upstream repository on GitHub

List the current configured remote repository of your fork by executing

$ git remote -v

If you see something like

origin<your username>/mlxtend.git (fetch)
origin<your username>/mlxtend.git (push)

you need to specify a new remote upstream repository via

$ git remote add upstream

Now, verify the new upstream repository you've specified for your fork by executing

$ git remote -v

You should see following output if everything is configured correctly:

origin<your username>/mlxtend.git (fetch)
origin<your username>/mlxtend.git (push)
upstream (fetch)
upstream (push)

2. Syncing your Fork

First, fetch the updates of the original project's master branch by executing:

$ git fetch upstream

You should see the following output

remote: Counting objects: xx, done.
remote: Compressing objects: 100% (xx/xx), done.
remote: Total xx (delta xx), reused xx (delta x)
Unpacking objects: 100% (xx/xx), done.
 * [new branch]      master     -> upstream/master

This means that the commits to the rasbt/mlxtend master branch are now stored in the local branch upstream/master.

If you are not already on your local project's master branch, execute

$ git checkout master

Finally, merge the changes in upstream/master to your local master branch by executing

$ git merge upstream/master

which will give you an output that looks similar to

SOME FILE1                    |    12 +++++++
SOME FILE2                    |    10 +++++++
2 files changed, 22 insertions(+),

*The Main Workflow - Making Changes in a New Topic Branch

Listed below are the 9 typical steps of a contribution.

1. Discussing the Feature or Modification

Before you start coding, please discuss the new feature, bugfix, or other modification to the project on the project's issue tracker. Before you open a "new issue," please do a quick search to see if a similar issue has been submitted already.

2. Creating a new feature branch

Please avoid working directly on the master branch but create a new feature branch:

$ git branch <new_feature>

Switch to the new feature branch by executing

$ git checkout <new_feature>

3. Developing the new feature / bug fix

Now it's time to modify existing code or to contribute new code to the project.

4. Testing your code

Add the respective unit tests and check if they pass:

$ PYTHONPATH='.' pytest ./mlxtend ---with-coverage

5. Documenting changes

Please add an entry to the mlxtend/docs/sources/ file. If it is a new feature, it would also be nice if you could update the documentation in appropriate location in mlxtend/sources.

6. Committing changes

When you are ready to commit the changes, please provide a meaningful commit message:

$ git add <modifies_files> # or `git add .`
$ git commit -m '<meaningful commit message>'

7. Optional: squashing commits

If you made multiple smaller commits, it would be nice if you could group them into a larger, summarizing commit. First, list your recent commit via

Due to the improved GitHub UI, this is no longer necessary/encouraged.

$ git log

which will list the commits from newest to oldest in the following format by default:

commit 046e3af8a9127df8eac879454f029937c8a31c41
Author: rasbt <>
Date:   Tue Nov 24 03:46:37 2015 -0500


commit c3c00f6ba0e8f48bbe1c9081b8ae3817e57ecc5c
Author: rasbt <>
Date:   Tue Nov 24 03:04:39 2015 -0500

        documented feature x

commit d87934fe8726c46f0b166d6290a3bf38915d6e75
Author: rasbt <>
Date:   Tue Nov 24 02:44:45 2015 -0500

        added support for feature x

Assuming that it would make sense to group these 3 commits into one, we can execute

$ git rebase -i HEAD~3

which will bring our default git editor with the following contents:

pick d87934f added support for feature x
pick c3c00f6 documented feature x
pick 046e3af fixed

Since c3c00f6 and 046e3af are related to the original commit of feature x, let's keep the d87934f and squash the 2 following commits into this initial one by changes the lines to

pick d87934f added support for feature x
squash c3c00f6 documented feature x
squash 046e3af fixed

Now, save the changes in your editor. Now, quitting the editor will apply the rebase changes, and the editor will open a second time, prompting you to enter a new commit message. In this case, we could enter support for feature x to summarize the contributions.

8. Uploading changes

Push your changes to a topic branch to the git server by executing:

$ git push origin <feature_branch>

9. Submitting a pull request

Go to your GitHub repository online, select the new feature branch, and submit a new pull request:

Notes for Developers

Building the documentation

The documentation is built via MkDocs; to ensure that the documentation is rendered correctly, you can view the documentation locally by executing mkdocs serve from the mlxtend/docs directory.

For example,

~/github/mlxtend/docs$ mkdocs serve

1. Building the API documentation

To build the API documentation, navigate to mlxtend/docs and execute the file from this directory via

~/github/mlxtend/docs$ python

This should place the API documentation into the correct directories into the two directories:

  • mlxtend/docs/sources/api_modules
  • mlxtend/docs/sources/api_subpackes

2. Editing the User Guide

The documents containing code examples for the "User Guide" are generated from IPython Notebook files. In order to convert a IPython notebook file to markdown after editing, please follow the following steps:

  1. Modify or edit the existing notebook.
  2. Execute all cells in the current notebook and make sure that no errors occur.
  3. Convert the notebook to markdown using the converter
~/github/mlxtend/docs$ python --ipynb ./sources/user_guide/subpackage/notebookname.ipynb


If you are adding a new document, please also include it in the pages section in the mlxtend/docs/mkdocs.yml file.

3. Building static HTML files of the documentation

First, please check the documenation via localhost (

~/github/mlxtend/docs$ mkdocs serve

Next, build the static HTML files of the mlxtend documentation via

~/github/mlxtend/docs$ mkdocs build --clean

To deploy the documentation, execute

~/github/mlxtend/docs$ mkdocs gh-deploy --clean

4. Generate a PDF of the documentation

To generate a PDF version of the documentation, simply cd into the mlxtend/docs directory and execute:


Uploading a new version to PyPI

1. Creating a new testing environment

Assuming we are using conda, create a new python environment via

$ conda create -n 'mlxtend-testing' python=3 numpy scipy pandas

Next, activate the environment by executing

$ source activate mlxtend-testing

2. Installing the package from local files

Test the installation by executing the following from within the mlxtend main directory:

$ pip install . -e

the --record files.txt flag will create a files.txt file listing the locations where these files will be installed.

Try to import the package to see if it works, for example, by executing

$ python -c 'import mlxtend; print(mlxtend.__file__)'

If everything seems to be fine, remove the installation via

$ cat files.txt | xargs rm -rf ; rm files.txt

Next, test if pip is able to install the packages. First, navigate to a different directory, and from there, install the package:

$ pip uninstall
$ pip install mlxtend

and uninstall it again

$ pip uninstall mlxtend

3. Deploying the package

Consider deploying the package to the PyPI test server first. The setup instructions can be found here.

First, install Twine if you don't have it already installed. E.g., use the following to install all recommended packages:

$ python -m pip install twine build

Second, create the distribution. This by default creates an sdist and wheel in the ./dist directory.

$ python -m build

Install the wheel and sdist to make sure they work. The distributions file names will change with each version.

python -m pip install ./dist/mlxtend-0.23.0.dev0.tar.gz --force-reinstall
python -m pip install ./dist/mlxtend-0.23.0.dev0-py3-none-any.whl --force-reinstall
python -m pip uninstall mlxtend

Third, upload the packages to the test server:

$ twine upload --repository-url dist/

Then, install it and see if it works:

$ pip install -i mlxtend

Next, uninstall it again as follows:

$ pip uninstall mlxtend

Fourth, after this dry-run succeeded, repeat this process using the "real" PyPI:

$ python -m twine upload  dist/*

4. Removing the virtual environment

Finally, to cleanup our local drive, remove the virtual testing environment via

$ conda remove --name 'mlxtend-testing' --all


if you get an error like

HTTPError: 403 Forbidden from

make sure you have an up to date version of twine installed (helped me to uninstall in conda and install via pip.)

5. Updating the conda-forge recipe

Once a new version of mlxtend has been uploaded to PyPI, update the conda-forge build recipe at by changing the version number in the recipe/meta.yaml file appropriately.