NumPy - Introduction

⚡️ What is NumPy

NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.

So basically its an array

⚡️ Advantages of NumPy over lists

NumPy arrays have a fixed size at creation, unlike Python lists (which can grow dynamically). Changing the size of an ndarray will create a new array and delete the original.

NumPy arrays facilitate advanced mathematical and other types of operations on large numbers of data. Typically, such operations are executed more efficiently and with less code than is possible using Python’s built-in sequences.

Linear algebra operations, Fourier transform, and random number generation

Beyond the fast array-processing capabilities that NumPy adds to Python, one of its primary uses in data analysis is as a container for data to be passed between algorithms and libraries

⚡️ Installing NumPy

Open terminal and simply type

pip install numpy

If it doesn't work for you use

pip3 install numpy

⚡️ How fast is NumPy over lists

Lets take an example

Using Lists

import time

start = time.time()

a = range(1000000)

for _ in range(10):
    [x * 2 for x in a]


end = time.time()

print(f"Code executed in {(end-start)*1000} ms")

Output -

Code executed in 950.7663249969482 ms

Using NumPy

import numpy as np
import time

start = time.time()

arr = np.arange(1000000)

for _ in range(10):
    arr * 2

end = time.time()

print(f"Code executed in {(end-start)*1000} ms")