Gain insights into utilizing NumPy for data manipulation and analytics. Learn about implementing concepts in both Python and NumPy through coding challenges and quizzes.

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If you're looking to grow your career in machine learning or data science in this day and age, adding a powerful library to your skill set is an important place to start. In that vein, Python has become one of the most widely used tools in the industry for serious data analytics, and NumPy is probably the most widely used data analytics library. With NumPy, you can manipulate data involving multi-dimensional arrays and matrices (think linear algebra).

Join us as we venture into the vast world of NumPy in this comprehensive course. Each lesson dive into the actual implementation of concepts in both pure Python and then NumPy, exploring how NumPy vectorization compares to traditional Python that uses a procedural and object-oriented approach.

Practice and test yourself along the way with in-browser coding challenges, quizzes, and more.

This course is intended for users who are already familiar with intermediate level Python.

From Python to Numpy

> *Temporal vectorization* is where elements share the same computation but necessitate a different number of iterations.


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# Problem Description

**The Mandelbrot** set is the set of complex numbers $c$ for which
the function $f_c(z) = z^2+ c$ does not diverge when iterated
from $z=0$, i.e., for which the sequence $f_c(0), f_c(f_c(0)) $, etc., remains bounded in an absolute value. It is very easy
to compute, but it can take a very long time because you need to
ensure a given number does not diverge. This is generally done by
iterating the computation up to a maximum number of iterations, after
which, if the number is still within some bounds, it is considered
non-divergent.


Of course, the more iterations you do, the more
precision you get.



This lesson explains temporal vectorization with an interesting case study called "Mandelbrot set".

Introduction

Anatomy of an Array

Code Vectorization

Problem Vectorization

Custom Vectorization

Beyond NumPy

Conclusion

Temporal Vectorization