Python for Data Science - Using NumPy to perform arithmetic operations on data

Chapter 5 - Basic Math and Statistics

Segment 1 - Using NumPy to perform arithmetic operations on data

import numpy as np
from numpy.random import randn

np.set_printoptions(precision=2)

Creating arrays

Creating arrays using a list

a= np.array([1,2,3,4,5,6])
a

array([1, 2, 3, 4, 5, 6])

b = np.array([[10,20,30],[40,50,60]])
b

array([[10, 20, 30],
       [40, 50, 60]])

Creating arrays via assignment

np.random.seed(25)
c = 36*np.random.randn(6)
c

array([  8.22,  36.97, -30.23, -21.28, -34.45,  -8.  ])

d = np.arange(1,35)
d

array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34])

Performing arthimetic on arrays

a*10

array([10, 20, 30, 40, 50, 60])

c + a

array([  9.22,  38.97, -27.23, -17.28, -29.45,  -2.  ])

c - a

array([  7.22,  34.97, -33.23, -25.28, -39.45, -14.  ])

c*a

array([   8.22,   73.94,  -90.68,  -85.13, -172.24,  -48.02])

c/a

array([  8.22,  18.48, -10.08,  -5.32,  -6.89,  -1.33])