NumPy Exercises

Testing basic NumPy knowledge.

Import NumPy as np

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.array([5.0]*10)

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

np.arange(10,51,2)

array([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(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.]])

Use NumPy to generate a random number between 0 and 1

np.random.rand(1)

array([0.7903837])

Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution

np.random.randn(25)

array([ 0.86287048,  0.34043398,  0.97092782, -0.12990277,  0.38232832,
       -0.69926378, -0.31600325,  0.85971356,  1.10996902, -1.40665255,
       -0.7009659 ,  0.14205376, -0.14403028, -0.62144374, -0.62569855,
       -0.09088263,  1.8967801 ,  1.46763251,  0.78542142, -0.16063449,
       -0.69190568, -1.50980316,  0.70223806,  0.24406165, -0.65443729])

Create the following matrix:

np.arange(0.01,1.01,0.01).reshape(10,10)

array([[0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],
       [0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],
       [0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],
       [0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],
       [0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],
       [0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],
       [0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],
       [0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],
       [0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],
       [0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 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

Generate a 5 by 5 matrix:

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]])

Slice the matrix

mat[2:,1:]

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

Select value based on location in the matrix

mat[3,4]

20

First three elements in the 2nd column

mat[:3,1:2]

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

#### Fifth

mat[4]

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

# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW
# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T
# BE ABLE TO SEE THE OUTPUT ANY MORE

mat[3:]

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

Now do the following

Get the sum of all the values in mat

mat.sum()

325

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=0)

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

Great Job!