Dive into NumPy! Start organizing, rearranging, and sampling your data.

Python Data Analysis and Visualization

From Python to Numpy

## Solution 1: NumPy Implementation
The trick is to search at each iteration values that have not yet diverged and update relevant information for these values and only these values. Because we start from Z = 0, we know that each value will be updated at least once (when they're equal to 0, they have not yet diverged) and will stop being updated as soon as they've diverged. To do that, we'll use NumPy fancy indexing with the less(x1,x2) function that return the truth value of (x1 < x2) element-wise.



# Solution 1: NumPy Implementation
The trick is to search at each iteration values that have not yet diverged and update relevant information for these values and only these values. Because we start from Z = 0, we know that each value will be updated at least once (when they're equal to 0, they have not yet diverged) and will stop being updated as soon as they've diverged. To do that, we'll use NumPy fancy indexing with the less(x1,x2) function that return the truth value of (x1 < x2) element-wise.



In this lesson, we are going to look at two NumPy approaches to solve this case study!

Introduction

Anatomy of an Array

Code Vectorization

Problem Vectorization

Custom Vectorization

Beyond NumPy

Coding Example: The Mandelbrot Set (NumPy approach)

Solution 1: NumPy Implementation