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Contributing Guidelines

We value your participation in this open source project. This page will give you a quick overview of how to get involved.

Important Note

  • Do not raise issues or send PRs for changing issue template, adding header-footer, badges or any buttons.
  • Do not raise issues or send PRs for for any website changes.
  • Do not choose an entire topic or a whole bunch of sections. Just choose a single small section for which you will be contributing when you raise a new issue.

How can I contribute?

You can contribute to this project by adding content on a new topic or improving existing content (in the README.md file).

The list of topics for which we are looking for content are provided below along with the location where the content has to be added:

  • Advanced Python - Link
  • Pandas - Link
  • NumPy - Link
  • SciPy - Link
  • Data Science & Machine Learning - Link
  • Plotting & Visualization - Link
  • Interacting with Databases - Link
  • Web Scrapping - Link
  • API Development - Link
  • Data Structures & Algorithms - Link
  • Python Mini Projects - Link
  • Python Question Bank - Link

You can check out some content ideas below.

Process

Step 1: Raise a new issue that you want to "Add content". We will assign the issue to you and label it.
Do not choose an entire topic or a whole bunch of sections. Just choose a single small section for which you will be contributing when you raise a new issue.
Step 2: Star and fork THIS repository.
Step 3: Now in your fork, go the correct topic folder as provided in the links above.
Step 4: Edit index.md file to add the title of the content and the corresponding file name where the content will be available in this folder.
Step 5: Add the content in markdown format in the file (extension .md).
Step 6: Raise a PR with your changes. Accept the pledge that the content is original and not stolen from any other source.
Step 7: Wait for review and PR merge.

Some Content Ideas

  • NumPy: Introduction, Arrays, Indexing and Slicing, Operations on Arrays, Concatenating Arrays, Reshaping Arrays, Splitting Arrays, Statistical Operations on Arrays, Loading Arrays from Files, Saving NumPy Arrays in Files, etc.
  • Pandas: Introduction, Importing and Exporting Data, DataFrames, Pandas Series Vs NumPy ndarray, Descriptive Statistics, Data Aggregations, Sorting a DataFrame, Group by Functions, Altering the Index, Other DataFrame Operations, Handling Missing Values, Import and Export of Data between Pandas and MySQL, Pandas plotting, etc.
  • Plotting & Visualization: Matplotlib, Seaborn, Customisation of Plots (Marker, colour, Line width and Line Style), Line chart, Bar Chart, Histogram, Scatter Chart, Plotting Quartiles and Box plot, Pie Chart, etc.
  • Data Science & Machine Learning: Scikit-learn, TensorFlow, PyTorch, regression, classification, clustering, ensemble model, deep learning, etc.
  • API Development: Building APIs using FastAPI, CRUD API Development, etc.
  • Web Scrapping: beautifulsoup, requests, etc.
  • Advanced Python: OOP In Python, Generators, List Comprehensions, Lambda functions, In-depth Function Arguments, Regular Expressions, Exception Handling, Partial functions, Code Introspection, Closures, Decorators, Map, Filter, Reduce, etc.

Any doubt?

In case you are new to the open source ecosystem, we would be more than happy to guide you through the entire process. Just join our Discord server and drop a message in relevant channel.