Contributing to datasist

Thanks for considering contributing to Datasist!
All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome here.
Have something in mind and not sure, chat with us **~~here**~~
For first time contributors, you can find/raise issues on our GitHub “issues” page. Once you’ve found an interesting issue, and have an improvement in mind, next thing is to set up your development environment.

Working with the code

Now that you have an issue you want to fix, enhancement to add, or documentation to improve, you need to learn how to work with GitHub and the datasist code base.

Version control, Git, and GitHub

The datasist code is hosted on GitHub. To contribute you will need to sign up for a free GitHub account. We use Git for version control to coordinate collaboration of contributors to this project. If you're new to Git and GitHub, the Official GitHub page is a great learning resource.

Forking the Datasist Repository

You will need your own fork to work on the code. Go to the datasist project page and hit the Fork button. After forking the repo,Click on the code drop-down and copy the link, you will use this later.

Create a development environment

It is advisable to create a development environment to test all code changes. This helps isolate datasist settings from other environments on your computer.
You can create a new virtual environment with conda. First, make sure you have either Anaconda or miniconda installed.
Confirm you have Anaconda installed:
conda --version
Create new virtual environment and install Python 3.5 or a later version:
$ conda create -n datasist-dev python=3.7
Activate your environment:
$ conda activate datasist-dev
Next, you will clone your forked repository to your local machine. Enter the following command:
git clone
then paste the url you copied earlier, next to the git clone command as shown below and press Enter
git clone
This creates the directory datasist and connects your repository to the upstream (main project) repository.
Next, change directory to datasist:
cd datasist
python build
and install:
pip install -e .
Test that datasist was successfully installed by starting a Python REPL:
$ python
Python 3.7.5 (default, Oct 25 2019, 15:51:11)
[GCC 7.3.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
then import datasist:
>>> import datasist as ds
If there you encounter no error after importing datasist, you're ready to start contributing. Now you can launch up your favorite IDE and start implementing your changes.
However, if you encounter a ModuleNotFound error while importing datasist package - as captured below, take note of the missing package name, exit the python interactive prompt and run on your command line.
pip install 'missing package name'
Proceed to import datasist after the missing package is successfully installed.

Docstrings Guidelines

Docstrings are important parts of coding and we encourage you to write clear and concise docstrings for your functions, methods and classes. Docstrings written for your code are automatically used to generate the datasist documentation.
Our guidelines for writing docstrings are:
  • Define what the function does.
  • Define all parameter types and what they do.
  • State the return values.
  • Use the correct spacing and indentation as this affects the documentation generated automatically.
  • Add Example usage.
Below is a sample docstrings for a function that adds two Pandas DataFrame together:
def add_df(df1=None, df2=None):
A function to add two dataframes together.
df1: DataFrame, Series
The first dataframe to add.
df1: DataFrame, Series
The second dataframe to add.
DataFrame: Concatenation of the two dataframe
>>> df1 = pd.DataFrame([1,2,3,4)
>>> df2 = pd.DataFrame(['a','b','c','d')
>>> add_df(df1,df2)
#check if dataframe is None
if df1 is None:
raise ValueError('df1: Expected a DataFrame, got None')
if df2 is None:
raise ValueError('df1: Expected a DataFrame, got None')
final_df = df1 + df2
return final_df

Writing tests

We strongly encourages contributors to write test for their code. Like many other python packages, datasist uses pytest to test its' code.
All tests should go into the tests sub-directory and placed in the corresponding script. The tests folder contains some current examples of tests, and we suggest looking through these for inspiration.
The easiest way to verify that your code is correct is to explicitly construct the result you expect (expected), then compare it to the actual result (output).

Using pytest

Here, we show an example of a test case written for the drop_redundant function in the feature_engineering module. This test is placed in the file inside the tests folder. The function is shown first below;
def drop_redundant(data):
Removes features with the same value in all cell. Drops feature If Nan is the second unique class as well.
data: DataFrame or named series.
DataFrame or named series.
if data is None:
raise ValueError("data: Expecting a DataFrame/ numpy2d array, got 'None'")
#get columns
cols_2_drop = _nan_in_class(data)
print("Dropped {}".format(cols_2_drop))
df = data.drop(cols_2_drop, axis=1)
return df
The corresponding test for the function above is:
import pytest
from datasist import feature_engineering
import pandas as pd
import numpy as np
df = pd.DataFrame({'country': ['Nigeria', 'Ghana', 'USA', 'Germany'],
'size': [280, 20, 60, np.NaN],
'language': ['En', 'En', 'En', np.NaN]})
def test_drop_redundant():
expected = ['country', 'size']
output = list(feature_engineering.drop_redundant(df).columns)
assert expected == output

Running the test case

To run the test case, navigate the tests/ subfolder and run the following command.
pytest tests
Learn more about pytest here.

Adding your changes to Datasist and Committing your code

Once you’ve made and saved your changes, you can check the changes by entering the underlisted command in our terminal:
git status
Next, Add the changes you've made by entering:
git add .
Next, commit your changes using:
git commit -m "Enter any commit message here"

Pushing your changes

Now, you want your changes to appear publicly on your GitHub page, you can push to your forked repo with:
git push

Review your code and finally make the pull request

If everything looks good, you are ready to make a pull request. A pull request is how code from a local repository becomes available to the Datasist community and can be reviewed and eventually merged into the master version. To submit a pull request:
  • Navigate to your updated repository of datasist on GitHub
  • Click on the Pull Request button
  • Write a description of your changes in the Preview Discussion tab
  • Click Send Pull Request.
This request will be reviewed by datasist maintainers and where it meets the requirements,it will be merged into the master branch.

Hooray! You're now a contributor to datasist. Now go bask in the euphoria!