Arrays are one of the vital parts of the higher-order architecture in artificial intelligence and machine learning. They can represent images, text, and many types of data.

In Python, which is one of the most favored programming languages for data scientists and AI engineers, ‘NumPy’ is the principal array programming library. Here we review the use of NumPy in the context of array programming.

NumPy: An Introduction

Prior to NumPy, two array packages existed in Python. One, numeric package, which was developed around the 1990s and offered array objects and array-aware functions. However, to handle huge astronomical images (taken from Hubble telescope), the Numeric package was reimplemented in the form of Numarray. It provided added support of:

Structured Array,

Flexible Indexing,

Byte-Order Variants,

Memory Mapping, and more.

15 years later, NumPy came to underline about every Python library – from SciPy, Matplotlib, sci-kit-learn, and others. So much so, it represents more than a typical library. It has become an open-source community capable of all functionalities provided by the past packages. Due to its simplicity, NumPy has become a de facto exchange for array data in Python.

NumPy in Python Ecosystem

NumPy is the base on which Python’s ecosystem is built. From among 137,000+ libraries of Python, it plays an essential role in many projects. It has applicability in deep learning and artificial intelligence programming, as well find use in a variety of fields like Physics, Chemistry, Biology, even Economics.

Did you know that NumPy is not a part of Python’s standard libraries?

https://datafloq.com/read/why-use-pythons-numpy-array-programming/10092