operations in numpy

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]

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