Posts - Page 2 of 5
Pandas Exercises Part 1
Welcome back, folks! In this series of 3 blog post, we will be discussing pandas which one of my favorite python libraries. We will go through 74 exercises to solidify your skills with pandas and as usual, I will explain the WHY behind every single exercise.
NumPy Exercises Part 3
Welcome back, folks! This post is the last in the series of NumPy exercises. In this post, we will see intermediate and advanced level exercises. Remember, the more you practice, the more you will understand NumPy and will use it with ease in your ML projects.
NumPy Exercises Part 2
For this second post of NumPy exercises series, we will be doing intermediate level exercises in NumPy and will go through the solution together as we did in the first part. Try to solve the exercises on your own then compare your answer with mine. Let’s get started.
NumPy Exercises Part 1
In this post, we will be solving 20 exercises in NumPy to sharpen what you have learnt from the NumPy introduction post. If you have not read the NumPy post, I highly encourage to go first through that post on this link and then come back to try out the exercises.
NumPy Crash Course
One of the most used scientific computing library for python is without a doubt NumPy, Numpy, which is an abbreviation of Numerical Python, is very fast at computing arrays since it is mostly written in C programming. NumPy adds support for large, multi-dimensional arrays and matrices, along with an extensive collection of high-level mathematical functions (for linear algebra) to operate on these arrays.