4 minute read

Today i will completing Numpy Exercise. If you want to solve it all by yourself, you can download notebooks file here

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Now Lets get started

NumPy ExercisesPermalink

Now that we’ve learned about NumPy let’s test your knowledge. We’ll start off with a few simple tasks, and then you’ll be asked some more complicated questions.

Import NumPy as npPermalink

import numpy as np

Create an array of 10 zerosPermalink

np.zeros(10)
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

Create an array of 10 onesPermalink

np.ones(10)
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])

Create an array of 10 fivesPermalink

np.ones(10) * 5
array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])

Create an array of the integers from 10 to 50Permalink

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 50Permalink

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 8Permalink

np.arange(0,9).reshape(3,3)
array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])

Create a 3x3 identity matrixPermalink

np.identity(3)
array([[1., 0., 0.],
       [0., 1., 0.],
       [0., 0., 1.]])

Use NumPy to generate a random number between 0 and 1Permalink

np.random.rand()
0.676711335601249

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

np.random.standard_normal(25)
array([-1.94269319,  0.16413494,  0.12671602, -0.85521163,  0.07889969,
       -0.6964513 ,  0.459806  , -0.79168522, -0.85169067, -0.91152675,
       -0.46378128,  1.82498542, -0.51968832,  0.40439839, -0.09762801,
       -0.60365759, -0.75796157,  1.14922432, -0.47134756, -0.39654744,
        1.15194413, -1.76571928, -1.14328406, -2.40506812, -0.90691368])

Create the following matrix:Permalink

np.arange(1,100) / 100
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])

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

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 SelectionPermalink

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]])
# 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[2:,1:]
array([[12, 13, 14, 15],
       [17, 18, 19, 20],
       [22, 23, 24, 25]])

array([[12, 13, 14, 15],
       [17, 18, 19, 20],
       [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[3,4]
20

20
# 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, 1].reshape(3,1)
array([[ 2],
       [ 7],
       [12]])

array([[ 2],
       [ 7],
       [12]])
# 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[4,:]
array([21, 22, 23, 24, 25])

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]])

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

Now do the followingPermalink

Get the sum of all the values in matPermalink

mat.sum()
325

Get the standard deviation of the values in matPermalink

mat.std()
7.211102550927978

Get the sum of all the columns in matPermalink

mat.sum(axis=1)
array([ 15,  40,  65,  90, 115])

Great Job!Permalink

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