https://github.com/leriomaggio/numpy-euroscipy

Introduction to NumPy - Tutorial @ EuroSciPy
https://github.com/leriomaggio/numpy-euroscipy

Last synced: 2 months ago
JSON representation

Introduction to NumPy - Tutorial @ EuroSciPy

• Host: GitHub
• URL: https://github.com/leriomaggio/numpy-euroscipy
• Owner: leriomaggio
• License: mit
• Created: 2015-08-26T01:34:29.000Z (about 10 years ago)
• Default Branch: master
• Last Pushed: 2023-07-06T21:45:59.000Z (over 2 years ago)
• Last Synced: 2025-04-05T16:36:18.451Z (6 months ago)
• Language: Jupyter Notebook
• Homepage: http://euroscipy.org
• Size: 2.97 MB
• Stars: 30
• Watchers: 4
• Forks: 21
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# Never get in battle of bits without ammunitions

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/leriomaggio/numpy-euroscipy/master)

**Title Credits**: Gentle reference to the homonymous [talk](https://pyvideo.org/europython-2013/never-get-in-a-battle-of-bits-without-ammunition.html) presented at 

**EuroPython 2013** in Florence by my friend **riko** (_a.k.a._ Enrico Franchi ).

## Abstract

The `numpy` package takes a central role in Python scientific ecosystem. 

This is mainly because `numpy` code has been designed with

high performance in mind. 

This tutorial will provide materials for the most essential concepts

to become confident with `numpy` and `ndarray` in (a matter of) `90 mins`.

# Outline

**Part I** Numpy Basics

- Introduction to NumPy Arrays

    - numpy internals schematics

    - Reshaping and Resizing

- Numerical Data Types

    - Record Array

    

    

**Part II** Indexing and Slicing

    

- Indexing numpy arrays

    - fancy indexing

    - array masking

- Slicing & Stacking

- Vectorization & Broadcasting

**Part III** "Advanced NumPy"

- Serialisation & I/O

    - `.mat` files

- Array and Matrix

    - Matlab compatibility

- Memmap 

- Bits of Data Science with NumPy

- NumPy beyond `numpy`

### Python version

The minimum recommended version of Python to use for this tutorial is **Python 3.5**, although 

Python 2.7 should be fine, as well as previous versions of Python 3. 

Py3.5+ is recommended due to a reference to the `@` operator in the linear algebra notebook.

## License and Sharing Material


This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.