28.numpy exercise

import numpy as np

Create an array of 10 zeros

np.zeros(10)

array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

Create an array of 10 ones

np.ones(10)

array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])

Create an array of 10 fives

np.ones(10) * 5

array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])

Create an array of the integers from 10 to 50

np.arange(10,51)

array([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, 35, 36, 37, 38, 39, 40, 41, 42, 43,
       44, 45, 46, 47, 48, 49, 50])

Create an array of all the even integers from 10 to 50

print(np.arange(10,51,2))

[10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50]

x = np.arange(10,51)
y = (x%2 == 0)
z = x[y]
print(z)

[10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50]

Create a 3x3 matrix with values ranging from 0 to 8

np.arange(0,9).reshape((3,3))

array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])

Create a 3x3 identity matrix

np.eye(3)

array([[1., 0., 0.],
       [0., 1., 0.],
       [0., 0., 1.]])

Generate a random number between 0 and 1

np.random.randint(0,1)

Create an array of 20 linearly spaced points between 0 and 1

np.linspace(0,1,20)

array([0.        , 0.05263158, 0.10526316, 0.15789474, 0.21052632,
       0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,
       0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,
       0.78947368, 0.84210526, 0.89473684, 0.94736842, 1.        ])

Numpy Indexing and Selection

Now you will be given a few matrices, and be asked to replicate the resulting matrix outputs:

mat = np.arange(1,26).reshape(5,5)
mat

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]])

mat[2: ,1:]

array([[12, 13, 14, 15],
       [17, 18, 19, 20],
       [22, 23, 24, 25]])

mat[3,4]

mat[0:3,1:2]

array([[ 2],
       [ 7],
       [12]])

mat[4]

array([21, 22, 23, 24, 25])

mat[3:]

array([[16, 17, 18, 19, 20],
       [21, 22, 23, 24, 25]])

Get the sum of all the values in mat

mat.sum()

Get the standard deviation of the values in mat

mat.std()

7.211102550927978

Get the sum of all the columns in mat

mat.sum(axis=1)

array([ 15,  40,  65,  90, 115])

Get the sum of all the rows in mat

mat.sum(axis=0)

array([55, 60, 65, 70, 75])