How to Fix “TypeError: len() of unsized object”

Problem Formulation: How to fix the TypeError: len() of unsized object? TypeError: len() of unsized object There are many possible ways why this array may occur. One common pitfall is to use the len() function on a NumPy array with only one value. Example: Let’s consider the minimal example that creates this error message! Reason … Read more

NumPy Average

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 Matrix Multiplication — np.matmul() and @ [Ultimate Guide]

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 Dot Product

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

NumPy Reshape 1D to 2D

Problem Formulation: Given a one-dimensional NumPy array. How to create a new two-dimensional array by reshaping the original array so that the new array has x rows and y columns? Here’s an example of what you’re trying to do: # Given: [0 1 2 3 4 5] x = 2 # rows y = 3 … Read more

How to Create a Sequence of Linearly Increasing Values with Numpy Arange?

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

How to Initialize a NumPy Array with Zeros and Ones

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