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Python + Github + GraphQL = PyGitGraph

  • Python + Github + GraphQL = PyGitGraph
  • "PyGitGraph" is a comprehensive suite of Jupyter notebooks for efficiently working with GitHub issues via GitHub's GraphQL API
  • the code in this repo allows you to analyze, visualize, and manage your Github issues like a pro!
  • future roadmap involves extending this functionality to PRs, Discussions and other Github functionalities

blog post on "PyGitGraph"

  • I have made a blog post on my website covering PyGitGraph. You can find it here. The blog post looks at the tool in a more non-technical way.
  • Alternatively, you can continue reading up more about the tool in the sections below.

purpose of "PyGitGraph"

  • GitHub's Issues feature provides robust project management tools, and its GraphQL API enables deep data access
  • this repository offers Jupyter notebooks that allow you to:
    • create new GitHub issues
    • retrieve and analyze existing issues
    • analyze trends and generate reports like these ones here

use cases of "PyGitGraph"

data analysis

  • export and analyze your issues to answer questions like:
    • what is the average issue open time?
    • which labels have the most associated issues?
  • create interactive graphs and rich visualizations from your data e.g.

time tracking

  • track tasks using github issues:
    • open an issue to start a task
    • close it when done
    • start new issues for new tasks
  • use the notebooks to extract data, analyze trends, and generate reports e.g.

data backup

  • github does not provide a direct way to back up issues
  • these notebooks aggregate and export issues to safeguard your data. e.g.

features of "PyGitGraph"

completed

  • fetch issues - retrieve issues by various parameters
  • filter issues - filter issues by labels, time, etc
  • create issues - create issues with specific titles, descriptions, and labels
  • close issues - close open issues
  • delete issues - delete issues
  • aggregate issues - combine issues into a single file/dataframe
  • visualize issues - create visualizations and export data for other tools

in progress

  • Pull Requests & Discussions - implement similar actions for pull requests github discussions
  • Bulk Actions - Use CSV/JSON input for batch operations on several issues at the same time

folder structure of this repository

  1. set parameters - configuration settings for queries and scripts. details.
  2. work with issues - fetch, filter, create, close, and delete issues
  3. aggregate data - append and aggregate data for backup and analysis. learn more
  4. visualize data - generate visualizations from aggregated data. explore
  5. clean up data - remove files created by notebooks. details

use "PyGitGraph"

  • 4 out-of-the-box options
    • you have four setup options to work with this code
      • using Binder (no installation or registration needed)
      • local python installation WITHOUT any virtual environment
      • local python installation WITH a virtual environment
      • VSCode with Devcontainers
    • you can find more info for each of this option in the next 4 sections
  • you do you
    • of course, you are not limited to these 4 options only
    • you can have your own setup approach as well
    • there is restriction set by the repo or the code

create github token

  • prerequisite
    • creating a github token is prerequisite for interacting with Github GraphQL
    • you would be asked for it when executing the code in any of the other files in this repo
    • dont worry, you will be asked just once - see more details here
    • find here details on how to create a new token
  • asked just once
    • you would not be asked to enter this token again and again...just once
    • you would asked for it once when you start with executing any of the jupyter notebooks in this repo
  • next steps
    • assuming you now have the token, you can use any of the below 4 options to use this repo

using Binder

  • just launch
    • simply click the badge below to launch this repository in Binder: Binder
    • this will launch a live environment where you can interact with the Jupyter notebooks and try out the code without any installation
  • all done
    • you are now free to execute any of the notebook in this repo

local python installation

  • create a github token

    • create a token and keep it secure
    • you would be asked for it when executing the code in any of the other files in this repo
    • dont worry, you will be asked just once - see more details here

  • clone the repository

    git clone https://github.com/kanad13/github_issues_explorer.git
cd PyGitGraph

  • install dependencies

    pip install -r ./requirements.txt

  • all done

    • you are now free to execute any of the notebook in this repo

python virtual environment

  • create a github token

    • create a token and keep it secure
    • you would be asked for it when executing the code in any of the other files in this repo
    • dont worry, you will be asked just once - see more details here

  • clone the repository

    git clone https://github.com/kanad13/PyGitGraph.git
cd PyGitGraph

  • create a virtual environment

    python3 -m venv PyGitGraph_env

  • activate the environment

    • windows -

      .\PyGitGraph_env\scripts\activate

    • macos/linux -

      source PyGitGraph_env/bin/activate

  • install dependencies

    pip install -r ./requirements.txt

  • all done

    • you are now free to execute any of the notebook in this repo

vscode devcontainer (docker)

  • create a github token
    • create a token and keep it secure
    • you would be asked for it when executing the code in any of the other files in this repo
    • dont worry, you will be asked just once - see more details here
  • open the repository in a container
    • open the command palette (ctrl+shift+p), and select "Remote-Containers: Open Folder in Container"
  • automatic setup
    • the environment will automatically install dependencies
  • customization
  • all done
    • you are now free to execute any of the notebook in this repo