🤔Why is the NumPy library so popular in Python?
NumPy is a Python language library that adds support for large multi-dimensional arrays and matrices, as well as high-level (and very fast) math functions to operate on these arrays. This library has several important features that have made it a popular tool.
Firstly, you can find its source code on GitHub, which is why NumPy is called an open-source module for Python. https://github.com/numpy/numpy/tree/main/numpy
Second, NumPy is written in C. This language is compiled, that is, the constructions crated with the kind of this language standards and rules are converted into machine code - a set of instructions for a particular type of processor. The conversion takes place with the help of a special compiler program, due to which all calculations occur quickly enough.
Let’s compare the performance between NumPy arrays and standard Python lists by the code below:
import numpy
import time
list1 = range(1000000)
list2 = range(1000000)
array1 = numpy.arange(1000000)
array2 = numpy.arange(1000000)
initialTime = time.time()
resultantList = [(a * b) for a, b in zip(list1, list2)]
print("Time taken by Python lists :",(time.time() - initialTime),"secs")
initialTime = time.time()
resultantArray = array1 * array2
print("Time taken by NumPy :", (time.time() - initialTime),"secs")
As the result of the test we can guess that NumPy arrays (0.002 sec) are more faster that standard Python lists (0.11 sec)
Performance differs across platforms due to software and hardware differences. The default bit generator has been chosen to perform well on 64-bit platforms. Performance on 32-bit operating systems is very different. You can see the details here: https://numpy.org/doc/stable/reference/random/performance.html#performance-on-different-operating-systems