Never get in battle of bits without ammunitions

Title Credits: Gentle reference to the homonymous talk 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.