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Welcome to Numpy exercise tutorial

By @alesanchezr and other contributors at 4Geeks Academy.

NumPy (and Pandas) are the #1 libraries for Machine Learning, there is no way you can do anything without them.

This interactive tutorial will help you become familiar with it, master the most used functionalities, and help you clean up your first datasets.

  • NumPy documentation.
  • Vectors.
  • Matrixes.
  • Random, Mean Values.

Note: The entire tutorial is 👆 interactive, ✅ auto-graded and with 📹 video tutorials.

These exercises were built in collaboration; we need you! If you find any bugs or misspellings, please contribute and report them.

One click installation (recommended):

You can open these exercises in just a few seconds by clicking: Open in Codespaces (recommended) or Open in Gitpod.

Once you have VSCode open the LearnPack exercises should start automatically. If exercises don't run automatically you can try typing on your terminal: $ learnpack start

Local Installation

  1. Clone or download this repository.

  2. Make sure you have LearnPack installed, node.js version 14+, and Python version 3+. This is the command to install LearnPack:

$ npm i @learnpack/learnpack -g && learnpack plugins:install learnpack-python
  1. Start the tutorial/exercises by running the following commands at the same level where your learn.json file is:
$ pip3 install pytest==4.6.0 pytest-testdox mock numpy==1.17.4 pandas==0.25.3
$ learnpack start

Note: The exercises have automatic grading, but it's very rigid and strict, my recommendation is to not take the tests too serious and use them only as a suggestion, or you may get frustrated.

Contributors

Thanks to these wonderful people (emoji key):

  1. Alejandro Sanchez (alesanchezr), contribution: (coder) 💻, (idea) 🤔, (build-tests) ⚠️, (pull-request-review) 👀, (build-tutorial) ✅, (documentation) 📖

  2. Paolo (plucodev), contribution: (bug reports) 🐛, (coder) 💻, (translation) 🌎

  3. Ricardo Rodriguez (RickRodriguez8080) contribution: (build-tutorial) ✅, (documentation) 📖

This project follows the all-contributors specifications.

Contributions of any kind are welcome!

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