NumPy is a popular Python library for data science focusing on arrays, vectors, and matrices. It’s at the core of data science and machine learning in Python. In today’s article, you’ll going to master NumPy’s impressive average() function that will be a loyal friend to you when fighting your upcoming data science battles. average(a, axis=None, … Read more
NumPy’s np.matmul() and the @ operator perform matrix multiplication. They compute the dot product of two arrays. For 2D arrays, it’s equivalent to matrix multiplication, while for higher dimensions, it’s a sum product over the last axis of the first array and the second-to-last of the second array. Have you ever tried to multiply two … Read more
Numpy is a popular Python library for data science focusing on arrays, vectors, and matrices. An important application of arrays, matrices, and vectors is the dot product. This article will teach you everything you need to know to get started! The dot product behaves differently for different input arrays. Dot Product 1D array and Scalar … Read more
To compare each element of a NumPy array arr against the scalar x using any of the greater (>), greater equal (>=), smaller (<), smaller equal (<=), or equal (==) operators, use the broadcasting feature with the array as one operand and the scalar as another operand. For example, the greater comparison arr > x … Read more
Anyone working with lists of data will encounter a need to combine them in a useful way. Often the best result is a dictionary consisting of keys and values. In this article, you’ll learn how to create a dictionary from two NumPy arrays. Problem Formulation: Given two NumPy arrays a and b. Create a dictionary … Read more
Given two 2D arrays a and b. You can perform standard matrix multiplication with the operation np.matmul(a, b) if the array a has shape (x, y) and array be has shape (y, z) for some integers x, y, and z. Problem Formulation: Given a two-dimensional NumPy array (=matrix) a with shape (x, y) and a … Read more
Problem: How to create a sequence of linearly increasing values? Solution: Use NumPy’s arange() function. The np.arange([start,] stop[, step]) function creates a new NumPy array with evenly-spaced integers between start (inclusive) and stop (exclusive). The step size defines the difference between subsequent values. For example, np.arange(1, 6, 2) creates the NumPy array [1, 3, 5]. … Read more
Numpy is a popular Python library for data science focusing on linear algebra. In this article, you’ll learn how to initialize your NumPy array. How to Initialize a NumPy Array with Zeros? To initialize your NumPy array with zeros, use the function np.zeros(shape) where shape is a tuple that defines the shape of your desired … Read more
This article explains how to insert a cursor to your plot, how to customize it and how to store the values that you selected on the plot window. In lots of situations we may want to select and store the coordinates of specific points in our graph; is it just for assessing their value or … Read more
NumPy is a popular Python library for data science. The focus of the library is computations on arrays, vectors, and matrices. If you work with data, there is no way that you can avoid NumPy. In this tutorial, you’ll learn more about the shape of a NumPy array. In particular, you’re going to learn the … Read more
Probability and Statistics play a very important role in the field of data science and machine learning. In this blog post you will learn the concept of proportional sampling and how can we implement it from scratch without using any library Proportional Sampling Let us take an example of tossing a die to better understand … Read more
Hey Finxters! I have another top 10 cheat sheets for you to consider and tape to your wall. In this article, I will be presenting the best Pytorch cheat sheets available. Keep them handy when you are working on a project and would like to spend less time searching for the correct function you are … Read more