/dsr-teaching-setup

DSR Teaching: How to SetUp a Data Science Project

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Project Description

The purpose of this mini-project is to show how to set up a data science project

It follows these steps:

  1. Create a new repository in GitHub
  2. Clone the repository on your computer
  3. Set your Readme, .gitignore & requirements file ❗️Push your changes❗️
  4. Create a Conda environment
  5. Install needed packages
  6. Make the environment visible in Jupyter Notebook / Jupyter Lab
  7. Install a Code Formatter
  8. Start Jupyter Lab / Jupyter Notebook

Tech Stack

Detailed Instructions

1. Create a Repo in GutHub

  • You can create a repo in GutHub by following these instructions
  • Initialise the repository with both a readme and a .gitignore file
    • the README is what you are reading now 😉. It contains useful information about the project and how to set it up
    • the .gitignore file contains all files and folders that should NOT be pushed to the repository, i.e. they should remain hidden. Examples are: files containing passwords, folders containing raw data. Select the template for Python.
      readme_gitignore

2. Clone the New Repository

In your terminal:

  • Go to the folder, in which you keep your repositories. Use cd <YOUR FOLDER NAME>
  • Execute git clone https://github.com/<YOUR USERNAME>/dsr-setup.git
    • In general git clone https://github.com/<YOUR USERNAME>/<YOUR REPO NAME>.git
  • If you need any help, see this tutorial.

3. Set your README, .gitignore & requirements file

  • README

  • .gitignore

  • requirements.txt

    • Create a simple empty .txt file
    • Every time you install a new package, add it with its version to this file in the format `package==version
    • There are also automatic ways to create this file. However, they are some times either too detailed or do not include everything
      • create with pip: pip freeze > requirements.txt
      • create with PyCharm
    • For the exercise add the following:
      numpy==1.26.3
      pandas==2.1.4
      seaborn==0.13.1
      scikit-learn==1.3.2

  • After setting the files, push to repository by typing in your terminal

    • git add .
    • git commit -m '240108_repo_setup'
    • git push origin main

4. Create a Conda Environment

In your terminal:

  • conda create -n dsr-setup python=3.12
  • conda activate dsr-setup
  • conda cheat sheet

5. Install needed packages

Still in your terminal and in the root folder of your repository execute

  • pip install -r requirements.txt

6. Make The Environment Visible in Jupyter Lab / Jupyter Notebook

In your terminal make sure the environment is activated and execute:

  • pip install ipykernel
  • python -m ipykernel install --user --name dsr-setup --display-name "dsr-setup"
    • in general python -m ipykernel install --user --name <YOUR ENVIRONMENT> --display-name "<YOUR ENVIRONMENT DISPLAY NAME>"

7. Start Jupyter Lab / Jupyter Notebook

  • Make sure the environment is activated
  • Just type jupyter lab or jupyter notebook
  • NOTE: the folder, from which you started jupyter, will be your root folder.
  • NOTE: if your tool of choice does not start, try first installing it by running conda install jupyteror conda install jupyterlab

Bonus: Install a Code Formatter

  • It is best practice to format your Python code according to PEP 8
  • Especially while learning, try to pay attention to it and correct yourself manually
  • You can then automatically format your code by installing a code formatter
  • Make sure your environment is activated
  • pip install jupyterlab_code_formatter
  • jupyter server extension enable --py jupyterlab_code_formatter
  • pip install autopep8
  • documentation
  • Deactivate & activate the environment in order for the changes to take place:
    • conda deactivate
    • conda activate dsr-setup