Numpy exercises

import numpy as np

1: Given the following matrix:

arr_2d = np.arange(0,9).reshape(3,3)
arr_2d

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

Obtain the following elements (one line for each):

arr_2d[:2, 1:]

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

arr_2d[-1]

array([6, 7, 8])

arr_2d[2]

array([6, 7, 8])

arr_2d[2, ]

array([6, 7, 8])

arr_2d[:, :2]

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

arr_2d[1, :2]

array([3, 4])

2: Create a matrix of size n x m of all zeros, surrounded by 1s.

n=6
m=8

zeros = np.zeros((n,m)) 
ones = np.ones((n+2, m+2))

ones[1:-1, 1:-1] = zeros
ones

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

3 Create a matrix with a checkerboard pattern. Black squares will be 0, white squares 1.

np.arange(0, 9)[::2]

array([0, 2, 4, 6, 8])

chess = np.zeros((8,8))

chess[::2, ::2] = 1
chess[1::2, 1::2] = 1
chess

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

import matplotlib.pyplot as plt

plt.imshow(chess, cmap="gray")

<matplotlib.image.AxesImage at 0x126452640>

4 Given the following matrix

np.random.seed(101)
arr_2drandom = np.random.randn(4,3)
arr_2drandom

array([[ 2.70684984,  0.62813271,  0.90796945],
       [ 0.50382575,  0.65111795, -0.31931804],
       [-0.84807698,  0.60596535, -2.01816824],
       [ 0.74012206,  0.52881349, -0.58900053]])

a) Subtract from each column the average value of each column

arr_2drandom - arr_2drandom.mean(axis=0)

array([[ 1.93116967,  0.02462533,  1.41259879],
       [-0.27185441,  0.04761057,  0.1853113 ],
       [-1.62375715,  0.00245797, -1.5135389 ],
       [-0.03555811, -0.07469388, -0.08437119]])

b) Substract from each row the average value of each row

arr_2drandom - arr_2drandom.mean(axis=1).reshape(4, 1)

array([[ 1.29253251, -0.78618462, -0.50634789],
       [ 0.22528387,  0.37257606, -0.59785993],
       [-0.09465036,  1.35939198, -1.26474162],
       [ 0.51347705,  0.30216849, -0.81564554]])

arr_2drandom.shape

(4, 3)

arr_2drandom.mean(axis=1).shape

(4,)

arr_2drandom.mean(axis=1).reshape(4, 1).shape

(4, 1)