Install python and an IDE

Install python and an IDE#

The code in this book is tested using python 3.12 and a list of dependencies. These can be found in the main repository in binder/environment.

Local installation#

For beginners it is is recommended that users first install Miniconda and install the packages you need using the provided conda environment. The environment bundles Python 3.12 along with a data science centric IDEs called Jupyter-Lab.

See also

Miniconda includes ‘conda’ (a package manager). Its good practice to have an environment per data science project. It allows you to create a “reproducible software box” on your computer that contains the version of Python you used along with the right versions of the other software (e.g. numpy and pandas).Instructions to setup the virtual environment are provided below, and we will cover reproducible environments in more detail later in the book.

An alternative to conda

Alternatively you can install substantially faster and more Open Source friendly Mamba via miniforge and install the packages you need using the provided conda environment or by selecting them yourself. By default mamba will install packages from conda-forge community channel. It is a slot in replacement for conda so simply replace all text that says conda with mamba below. Its my personal perference and what I use in all my contemporary research.

Install the dependencies#

  1. Clone this repository:

    git clone https://github.com/health-data-science-OR/coding-for-ml.git
cd coding-for-ml

  2. Install the virtual environment:

    conda env create -f binder/environment.yml
conda activate hds_code

  3. Launch JupyterLab:

    jupyter-lab

For reference the latest version of the book uses these packages:

name: hds_code
channels:
  - conda-forge
dependencies:
  - black
  - flake8
  - hatch
  - jupyterlab=4.4.6
  - jupyterlab-spellchecker=0.8.4
  - matplotlib=3.10.6
  - nbqa
  - nodejs=24.4.1
  - numpy=2.3.2
  - pandas=2.3.2
  - pip=25.2
  - python=3.12
  - pytest=8.4.1
  - py7zr=1.0.0
  - rich=14.1.0
  - scikit-learn=1.7.1
  - scipy==1.16.1
  - statsmodels=0.14.5

Run our code via Binderhub or Google Colab#

Note

You have the option of running our code in the cloud without the need to install on your local machine.

When you navigate to books pages and exercises that contain code cells you will see a image of a ‘rocketship’ in the top right hand corner of the screen. Move you mouse over the image and you can choose to either open the notebook in BinderHub (will take ~1 minute to open) or Google Colab. You will need a Google account to use Google Colab.

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