Matplotlib Stack Plot - Tutorial and Examples
There are many data visualization libraries in Python, yet Matplotlib is the most popular library out of all of them. Matplotlib’s popularity is due to its reliability and utility - it's able to create both simple and complex plots with little code. You can also customize the plots in...
Python: How to Flatten a List of Lists
A list is the most flexible data structure in Python. Whereas, a 2D list which is commonly known as a list of lists, is a list object where every item is a list itself - for example: [[1,2,3], [4,5,6], [7,8,9]]. Flattening a list of...
Generating Synthetic Data with Numpy and Scikit-Learn
In this tutorial, we'll discuss the details of generating different synthetic datasets using Numpy and Scikit-learn libraries. We'll see how different samples can be generated from various distributions with known parameters. We'll also discuss generating datasets for different purposes, such as regression, classification, and clustering. At the end we'll see...
Remove Element from an Array in Python
This tutorial will go through some common ways for removing elements from Python arrays. Here's a list of all the techniques and methods we'll cover in this article: remove() pop() del NumPy arrays Arrays in Python Arrays and lists are not the same thing in Python. Although lists are more...
Dimensionality Reduction in Python with Scikit-Learn
In machine learning, the performance of a model only benefits from more features up until a certain point. The more features are fed into a model, the more the dimensionality of the data increases. As the dimensionality increases, overfitting becomes more likely. There are multiple techniques that can be used...
Solving Systems of Linear Equations with Python's Numpy
The Numpy library can be used to perform a variety of mathematical/scientific operations such as matrix cross and dot products, finding sine and cosine values, Fourier transform and shape manipulation, etc. The word Numpy is short-hand notation for "Numerical Python". In this article, you will see how...