Learning to Use Numpy Library in Python with Code Examples

NumPy – A Replacement for MatLab

NumPy is often used along with packages like SciPy (Scientific Python) and Matplotlib (plotting library). This combination is widely used as a replacement for Matlab, a popular platform for technical computing. However, Python alternative to Matlab is now seen as a more modern and complete programming language.

Installation

In the terminal, type:

>>>pip3 install numpy

Code Example | Creating 2D Numpy Array

np_2d = np.array([[1.73, 1.68, 1.71, 1.89, 1.79],

 [65.4, 59.2, 63.6, 88.4, 68.7]])

 print(np_2d.shape)

Code Example | Calculating BMI

import numpy as np

np_height = np.array([1.73, 1.68, 1.71, 1.89, 1.79])

np_weight = np.array([65.4, 59.2, 63.6, 88.4, 68.7])

bmi = np_weight / np_height ** 2

print(” BMI : “, bmi)

Basic Statistical Analysis

import numpy as np

np_city = np.array([[ 1.64, 71.78],

[ 1.37, 63.35],

[ 1.6 , 55.09],

[ 2.04, 74.85],

[ 2.04, 68.72],

[ 2.01, 73.57]])

print(np_city)

print(type(np_city))

print(“Mean Height : “,np.mean(np_city[:,0]))

print(“Median Height : “,np.median(np_city[:,0]))

np.corrcoef(np_city[:,0], np_city[:,1])

np.std(np_city[:,0])

fam = [1.73, 1.68, 1.71, 1.89]

tallest = max(fam)

print(“Tallest : “, tallest)

Data Generation and Statistics

Random.normal is used to draw random samples from a normal (Gaussian) distribution. For example, we can generate 1000 samples with mean mu and standard deviation sigma as follows:

mu, sigma = 0, 0.1 # mean and standard deviation

s = np.random.normal(mu, sigma, 1000)

height = np.round(np.random.normal(1.75,0.20,5000),2)

weight = np.round(np.random.normal(60.32,15,5000),2)

np_city = np.column_stack((height,weight))

print(np_city)