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

>**Note:**
You should look at the [ufunc.reduceat](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ufunc.reduceat.html) method that performs a (local) reduce with specified slices over a single axis.

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### Problem Description

We now want to measure the fractal dimension of the Mandelbrot set using the [Minkowski–Bouligand dimension](https://en.wikipedia.org/wiki/Minkowski%E2%80%93Bouligand_dimension). To do that, we need to do box-counting with a decreasing box size (see figure below). As you can imagine, we cannot use pure Python because it would be way too slow. The goal of the exercise is to write a function using NumPy that takes a two-dimensional float array and returns the fractal dimension. We'll consider values in the array to be normalized (i.e. all values are between 0 and 1).

Given below is the Minkowski-Bouligand dimension:

>**Note:**
You should look at the [ufunc.reduceat](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ufunc.reduceat.html) method that performs a (local) reduce with specified slices over a single axis.

$$$$

# Problem Description

We now want to measure the fractal dimension of the Mandelbrot set using the [Minkowski–Bouligand dimension](https://en.wikipedia.org/wiki/Minkowski%E2%80%93Bouligand_dimension). To do that, we need to do box-counting with a decreasing box size (see figure below). As you can imagine, we cannot use pure Python because it would be way too slow. The goal of the exercise is to write a function using NumPy that takes a two-dimensional float array and returns the fractal dimension. We'll consider values in the array to be normalized (i.e. all values are between 0 and 1).

Given below is the Minkowski-Bouligand dimension:

This problem is an extension of the previous case study. In this lesson, we'll learn how to find the fractal dimension of the Mandelbrot set. 

Introduction

Anatomy of an Array

Code Vectorization

Problem Vectorization

Custom Vectorization

Beyond NumPy

Conclusion

Coding Example: Minkowski-Bouligand Dimension

Problem Description