Introduction to NumPy library in Python

NumPy is a Python library that provides powerful and versatile N-dimensional arrays, mathematical functions, random number generators, linear algebra routines, and more. It is open source, distributed, and interoperable with various hardware and computing platforms. NumPy stands for Numerical Python and was created in 2005 by Travis Oliphant.

NumPy aims to provide an array object that is up to 50x faster than traditional Python lists. The array object in NumPy is called ndarray, which supports a lot of supporting functions that make working with ndarray very easy. Arrays are very frequently used in data science, where speed and resources are very important.

NumPy arrays are stored at one continuous place in memory unlike lists, so processes can access and manipulate them very efficiently. This behavior is called locality of reference in computer science. This is the main reason why NumPy is faster than lists. Also, NumPy is optimized to work with the latest CPU architectures.

NumPy also offers comprehensive mathematical functions, such as trigonometric, statistical, and algebraic functions, that can operate on arrays element-wise or along a specified axis. NumPy also has functions for generating random numbers from various distributions, such as uniform, normal, binomial, etc.

NumPy also has functions for performing linear algebra operations, such as matrix multiplication, inversion, decomposition, determinant, rank, etc. NumPy also supports complex numbers, which can be useful for some applications, such as signal processing or quantum computing.

NumPy also has functions for performing Fourier transforms, which can convert a signal from the time domain to the frequency domain, or vice versa. Fourier transforms are widely used in signal analysis, image processing, data compression, etc.

NumPy is the fundamental package for scientific computing with Python, and it is the basis for many other libraries, such as SciPy, Pandas, Matplotlib, Scikit-learn, etc. NumPy also plays well with distributed, GPU, and sparse array libraries, such as Dask, CuPy, and JAX.

To import the libray type the below code:

import numpy