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What is faster than Python list?

NumPy Arrays are faster than Python Lists because of the following reasons: An array is a collection of homogeneous data-types that are stored in contiguous memory locations. On the other hand, a list in Python is a collection of heterogeneous data types stored in non-contiguous memory locations.

geeksforgeeks.org - Why is Numpy faster in Python? - GeeksforGeeks

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NumPy is a Python fundamental package used for efficient manipulations and operations on High-level mathematical functions, Multi-dimensional arrays, Linear algebra, Fourier Transformations, Random Number Capabilities, etc. It provides tools for integrating C, C++, and Fortran code in Python. NumPy is mostly used in Python for scientific computing. Let us look at the below program which compares NumPy Arrays and Lists in Python in terms of execution time.

Python3

import numpy import time size = 1000000 list1 = range (size) list2 = range (size) array1 = numpy.arange(size) array2 = numpy.arange(size) initialTime = time.time() resultantList = [(a * b) for a, b in zip (list1, list2)] print ( "Time taken by Lists :" , (time.time() - initialTime), "seconds" ) initialTime = time.time() resultantArray = array1 * array2 print ( "Time taken by NumPy Arrays :" , (time.time() - initialTime), "seconds" )

Output:

Time taken by Lists : 1.1984527111053467 seconds Time taken by NumPy Arrays : 0.13434123992919922 seconds From the output of the above program, we see that the NumPy Arrays execute very much faster than the Lists in Python. There is a big difference between the execution time of arrays and lists.

NumPy Arrays are faster than Python Lists because of the following reasons:

An array is a collection of homogeneous data-types that are stored in contiguous memory locations. On the other hand, a list in Python is a collection of heterogeneous data types stored in non-contiguous memory locations. The NumPy package breaks down a task into multiple fragments and then processes all the fragments parallelly. The NumPy package integrates C, C++, and Fortran codes in Python. These programming languages have very little execution time compared to Python. Below is a program that compares the execution time of different operations on NumPy arrays and Python Lists:

Python3

import numpy import time size = 1000000 list1 = [i for i in range (size)] list2 = [i for i in range (size)] array1 = numpy.arange(size) array2 = numpy.arange(size) print ( " Concatenation:" ) initialTime = time.time() list1 = list1 + list2 print ( "Time taken by Lists :" , (time.time() - initialTime), "seconds" ) initialTime = time.time() array = numpy.concatenate((array1, array2), axis = 0 ) print ( "Time taken by NumPy Arrays :" , (time.time() - initialTime), "seconds" ) dot = 0 print ( "

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Dot Product:" ) initialTime = time.time() for a, b in zip (list1, list2): dot = dot + (a * b) print ( "Time taken by Lists :" , (time.time() - initialTime), "seconds" ) initialTime = time.time() array = numpy.dot(array1, array2) print ( "Time taken by NumPy Arrays :" , (time.time() - initialTime), "seconds" ) print ( " Scalar Addition:" ) initialTime = time.time() list1 = [i + 2 for i in range (size)] print ( "Time taken by Lists :" , (time.time() - initialTime), "seconds" ) initialTime = time.time() array1 = array1 + 2 print ( "Time taken by NumPy Arrays :" , (time.time() - initialTime), "seconds" ) print ( " Deletion: " ) initialTime = time.time() del (list1) print ( "Time taken by Lists :" , (time.time() - initialTime), "seconds" ) initialTime = time.time() del (array1) print ( "Time taken by NumPy Arrays :" , (time.time() - initialTime), "seconds" )

Output:

Concatenation: Time taken by Lists : 0.02946329116821289 seconds Time taken by NumPy Arrays : 0.011709213256835938 seconds Dot Product: Time taken by Lists : 0.179551362991333 seconds Time taken by NumPy Arrays : 0.004144191741943359 seconds Scalar Addition: Time taken by Lists : 0.09385180473327637 seconds Time taken by NumPy Arrays : 0.005884408950805664 seconds Deletion: Time taken by Lists : 0.01268625259399414 seconds Time taken by NumPy Arrays : 3.814697265625e-06 seconds From the above program, we conclude that operations on NumPy arrays are executed faster than Python lists. Moreover, the Deletion operation has the highest difference in execution time between an array and a list compared to other operations in the program.

geeksforgeeks.org - Why is Numpy faster in Python? - GeeksforGeeks