Operations In Numpy

Arrays in numpy are quite flexible in its dealing with another array or a scaler. Let us see some array to array operations. Let us begin with addition and subtraction of two arrays. 

import numpy as np  arr = np.arange(0,11)  print(arr+arr)  print(arr-arr)

Output : 
[ 0  2  4  6  8 10 12 14 16 18 20]  [0 0 0 0 0 0 0 0 0 0 0]

Numpy also allows product of two arrays .

import numpy as np  arr_1 = np.arange(0,11)  arr_2 = np.arange(10,21)  print(arr_1 * arr_2)

Output : 
[  0  11  24  39  56  75  96 119 144 171 200]

It is also possible to divide two arrays. Let us see how. 

import numpy as np  arr_1 = np.arange(0,11)  arr_2 = np.arange(10,21)  print( arr_1 / arr_2  )

Output : 
[0.         0.09090909 0.16666667 0.23076923 0.28571429 0.33333333   0.375      0.41176471 0.44444444 0.47368421 0.5       ]

All mathematical operations can also be performed between an array and an scaler.

import numpy as np  arr_1 = np.arange(0,11)  print( arr_1 + 5  )  print( arr_1 * 5  )  print( arr_1 - 5  )  print( arr_1 / 5  )

Output : 
[ 5  6  7  8  9 10 11 12 13 14 15]  [ 0  5 10 15 20 25 30 35 40 45 50]  [-5 -4 -3 -2 -1  0  1  2  3  4  5]  [0.  0.2 0.4 0.6 0.8 1.  1.2 1.4 1.6 1.8 2. ]
Functional operations on arrays

min and max :

import numpy as np  arr_1 = np.arange(0,11)    print(np.min(arr_1))  print(np.max(arr_1))

Output : 
0  10

sqrt : 

import numpy as np  arr_1 = np.arange(0,11)    print(np.sqrt(arr_1))
Output : 
[0.         1.         1.41421356 1.73205081 2.         2.23606798   2.44948974 2.64575131 2.82842712 3.         3.16227766]

trignometric functions like sin and tan are also built in numpy.

sin and tan : 

import numpy as np  arr_1 = np.arange(0,11)    print(np.sin(arr_1))  print()  print(np.tan(arr_1))

Output : 
[ 0.          0.84147098  0.90929743  0.14112001 -0.7568025  -0.95892427   -0.2794155   0.6569866   0.98935825  0.41211849 -0.54402111]    [ 0.          1.55740772 -2.18503986 -0.14254654  1.15782128 -3.38051501   -0.29100619  0.87144798 -6.79971146 -0.45231566  0.64836083]

exp :

The exponential value function . 

import numpy as np  arr_1 = np.arange(0,11)    print(np.exp(arr_1))

Output : 
[1.00000000e+00 2.71828183e+00 7.38905610e+00 2.00855369e+01   5.45981500e+01 1.48413159e+02 4.03428793e+02 1.09663316e+03   2.98095799e+03 8.10308393e+03 2.20264658e+04]

abs :

Absolute value function – returns the absolute value of a number 

import numpy as np  arr_1 = np.arange(-5,5)    print(np.abs(arr_1))

Output : 
[5 4 3 2 1 0 1 2 3 4]
PreviousNext