# 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

<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/80x15.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.