NumPy - Vectorize Your Brain

Discover essential NumPy idioms for efficient scientific computing in Python through this EuroPython 2015 conference talk. Learn why Python loops can be slow and how vectorizing operations with NumPy can improve performance. Explore array creation, broadcasting, universal functions, aggregations, slicing, and indexing. Gain insights into the fundamental differences between Java and Python performance, and understand why NumPy is crucial for fast numerical computations. Apply these concepts to practical examples, such as K-Nearest Neighbors, and enhance your Python coding skills for scientific applications. Benefit from this knowledge even if you're not currently using NumPy in your projects.

Introduction
KNearest Neighbors
Python is slow
Java vs Python
What is NumPy
Universal Functions
Slicing Indexing
Broadcasting
Aggregations
Summary
Example
Questions