Data Science exploration

Small personal playground for exploring data‑science tools, experiments and patterns.

Note

This README has been autogenerated with the library nbdev using the file nbs/index.ipynb.
To update this README modify nbs/index.ipynb

Quickstart (most common steps)

1. Create and activate a fresh environment:

   $ conda create -n dsenv python=3.13
   $ conda activate dsenv

2. Install or update nbdev:

   $ pip install -U nbdev

3. Install this project in development mode (editable):

   $ pip install -e .

4. When you change notebooks, compile the package:

   $ nbdev_prepare

5. Run tests locally:

   $ pytest

Note

1. The latest conda versions create empty environment (withouth python), so we have to indicate the python version to install.
   
2. You can also use conda, but Github uses pip in it’s Workflows. Flag -U for updgrade
3. This will install all the libraries in the requirements specified in settings.ini
4. nbdev_prepare: nbdev_export, nbdev_test and nbdev_clean and render README

Warning

1. nbdev_test runs all the notebooks in parallel and can freeze the computer

Installation of this repo

Install latest from the GitHub repository:

$ pip install git+https://github.com/Ramon-PR/DataScience_exploration.git

or from conda

$ conda install -c Ramon-PR DataScience_exploration

or from pypi

$ pip install DataScience_exploration

Documentation

Documentation can be found hosted on this GitHub repository’s pages. Additionally you can find package manager specific guidelines on conda and pypi respectively.

How to use

Fill me in please! Don’t forget code examples:

1+1

2

Annex — nbdev & development workflows

How to create a new nbdev project

1. Create a new conda environment with python<3.14 (Still not compatible, oct 2025) > conda create -n my_env python=3.12
2. Activate the environment > conda activate my_env
3. Install nbdev > pip install -U nbdev
4. Create a folder > mkdir Project_1
5. Change dir to a folder > cd Project_1
6. Create a new nbdev project > nbdev_new
7. It will ask for data (or read it automatically if it is a cloned github repo)
   • With that data nbdev creates the files and folders:
     • README.md
       
     • /nbs
       
     • pyproject.toml
       
     • settings.ini
       
     • setup.py
     • …
8. We should edit settings.ini with the packages that we need, for example: > requirements = fastcore pandas seaborn matplotlib
9. When we do > pip install -e .

   • pip looks first for pyproject.toml and finds: > [build-system]
     > requires = [“setuptools=64.0”]
     > build-backend = “setuptools.build_meta”

   • which says: > “Hey pip — before installing, create a temporary build environment and install setuptools>=64.0 in it. Then ask setuptools how to build this package.”

   • setuptools looks for project metadata in the form of

     • a [project] section in pyproject.toml (modern, PEP 621)
     • a setup.cfg
     • or a setup.py (which we have and reads our settings.ini)

   • setuptools reads and executes setup.py which contains a line:

     • install_requires = requirements (read from settings.ini)
10. Finally we have to maintain our repo updated with the packages that we use
    • Edit: settings.ini