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

• Python
• Jupyter Lab & Jupyter Notebook
• Visual Studio Code (download here) or PyCharm (installation instructions)
• Github
  • Example for file structure - pipeline and project workflow template of DSSG.
  • repo template

• 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.
    

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.

• README

  • Idea for structure
  • Help for the formatting
  • PyCharm comes in handy when creating the file

• .gitignore

  • documentation
  • Collection of .gitignore templates. Relevant for you is the Python one

• 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

In your terminal:

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

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

• pip install -r requirements.txt

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>"

• 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

• 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