Within Python, NumPy is one of the most important libraries for data science and machine learning. Most machine learning experts agree that Python is the top programming language for machine learning. Numpy is among the most popular libraries in Python (e.g. What are matrices? In NumPy, they are arrays with homogeneous data of the same type – for example – float values. In machine learning, crucial algorithms and data structures rely on matrix computations. By learning NumPy, you equip yourself with a powerful tool for data analysis on numerical multi-dimensional data.īut you may ask (and rightly so): What Can NumPy Do for Me?įear of missing out on machine learning and data science? Learning NumPy now is a great first step into the field of machine learning and data science. NumPy implements these operations efficiently and in a rigorous consistent manner. These data type restrictions allow numpy to specialize in providing efficient linear algebra operations.Īmong those operations are maximum, minimum, average, standard deviation, variance, dot product, matrix product, and many more. This means that a numpy array contains either integer or float values, but not both at the same time. It has to be of homogeneous data values as well. NumPy is even more restrictive than focusing only on numerical data values. In this sense, NumPy arrays are different from Python lists that allow arbitrary data types. A NumPy array allows only for numerical data values.

A NumPy array is a multi-dimensional matrix of numerical data values (integers or floats). It’s about matrices and vectors – and doing operations on top of them.Īt the heart of NumPy is a basic data type, called a NumPy array. Think about linear algebra in school (or university) – NumPy is the Python library for it. NumPy is a Python library that allows you to perform numerical calculations. So without further introduction, let’s dive into the NumPy library in Python.

Python numpy tutorial