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

# Bridson method
If the vectorization of the previous method poses no real difficulty, the speed improvement is not so good and the quality remains low and dependent on the k parameter. The higher, the better since it basically governs how hard to try to insert a new sample. But, when there is already a large number of accepted samples, only chance allows us to find a position to insert a new sample. We could increase the k value but this would make the method even slower without any guarantee in quality. It's time to think out-of-the-box and luckily enough, Robert Bridson did that for us and proposed a simple yet efficient method:

## Step 0:

Initialize an n-dimensional background grid for storing samples and accelerating spatial searches. We pick the cell size to be bounded by $\frac{r}{\sqrt{n}}$, so that each grid cell will contain at most one sample, and thus the grid can be implemented as a simple n-dimensional array of integers: the default `−1` indicates no sample, a non-negative integer gives the index of the sample located in a cell.

## Step 1:

Select the initial sample, `x_0`, randomly chosen uniformly from the domain. Insert it into the background grid, and initialize the “active list” (an array of sample indices) with this index (zero).

## Step 2:
While the active list is not empty, choose a random index from it (say $i$). Generate up to k points chosen uniformly from the spherical annulus between radius $r$ and $2r$ around $x_i$. For each point in turn, check if it is within distance r of existing samples (using the background grid to only test nearby samples). If a point is adequately far from existing samples, emit it as the next sample and add it to the active list. If after $k$ attempts no such point is found, instead remove $i$ from the active list.
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## Implementation
The implementation poses no real problem. Note that not only is this method fast, but it also offers a better quality (more samples) than the DART method even with a high $k$ parameter. Here's the complete `Bridson` implementation:



In this lesson, we will try to do the blue noise sampling using the Bridson method. First we will discuss the step-by-step approach and then implement it in code.

Introduction

Anatomy of an Array

Code Vectorization

Problem Vectorization

Custom Vectorization

Beyond NumPy

Conclusion

Coding Example: Blue Noise Sampling using Bridson method

Bridson method

Step 0: