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NumPy Exercises

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.

IMPORTANT NOTE! Make sure you don't run the cells directly above the example output shown, otherwise you will end up writing over the example output!

Import NumPy as np



In [1]:

    
import numpy as np

Create an array of 10 zeros



In [2]:

    
# CODE HERE
np.zeros(10)









    Out[2]:





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

Create an array of 10 ones



In [3]:

    
# CODE HERE
np.ones(10)









    Out[3]:





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

Create an array of 10 fives



In [4]:

    
# CODE HERE
np.ones(10) * 5









    Out[4]:





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

Create an array of the integers from 10 to 50



In [5]:

    
# CODE HERE
np.arange(10, 51)









    Out[5]:





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



In [6]:

    
# CODE HERE
np.arange(10, 51, 2)









    Out[6]:





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



In [7]:

    
# CODE HERE
np.arange(9).reshape(3,3)









    Out[7]:





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

Create a 3x3 identity matrix



In [8]:

    
# CODE HERE
np.eye(3)









    Out[8]:





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

Use NumPy to generate a random number between 0 and 1



In [9]:

    
# CODE HERE
np.random.randn(1)









    Out[9]:





array([ 1.91358801])

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



In [10]:

    
# CODE HERE
np.random.randn(25)









    Out[10]:





array([ 0.55183756, -1.3584953 ,  1.80606428,  1.23953382, -0.59855745,
       -1.00548875,  0.81657645, -1.50001345, -1.1707562 , -1.43071349,
       -1.07447439, -1.06407928, -0.81640882,  0.41278046, -0.11352749,
        1.31532805, -0.67066767,  1.59206672, -0.14437886,  1.14994271,
        1.42771432, -0.48792962,  0.78490582, -0.1964844 ,  0.86969164])

Create the following matrix:



In [11]:

    
np.arange(1, 101).reshape(10, 10) / 100









    Out[11]:





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:



In [12]:

    
np.linspace(0, 1, 20)









    Out[12]:





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:



In [13]:

    
# HERE IS THE GIVEN MATRIX CALLED MAT
# USE IT FOR THE FOLLOWING TASKS
mat = np.arange(1,26).reshape(5,5)
mat









    Out[13]:





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]])



In [14]:

    
# 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



In [15]:

    
mat[2:, ]









    Out[15]:





array([[11, 12, 13, 14, 15],
       [16, 17, 18, 19, 20],
       [21, 22, 23, 24, 25]])



In [16]:

    
# 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



In [17]:

    
mat[3, -1]









    Out[17]:





20



In [18]:

    
# 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



In [19]:

    
mat[:3, 1].reshape(3, 1)









    Out[19]:





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



In [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



In [21]:

    
mat[-1, :]









    Out[21]:





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



In [22]:

    
# 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



In [23]:

    
mat[-2:, :]









    Out[23]:





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

Now do the following

Get the sum of all the values in mat



In [24]:

    
# CODE HERE
np.sum(mat)









    Out[24]:





325

Get the standard deviation of the values in mat



In [25]:

    
# CODE HERE
np.std(mat)









    Out[25]:





7.2111025509279782

Get the sum of all the columns in mat



In [26]:

    
# CODE HERE
np.sum(mat, axis = 0)









    Out[26]:





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

Bonus Question

We worked a lot with random data with numpy, but is there a way we can insure that we always get the same random numbers? Click Here for a Hint



In [27]:

    
# My favourite number is 7
np.random.seed(7)