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.

Import NumPy as np



In [1]:

    
import numpy as np

Create an array of 10 zeros



In [2]:

    
np.zeros(10)









    Out[2]:





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



In [2]:









    Out[2]:





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

Create an array of 10 ones



In [3]:

    
np.ones(10)









    Out[3]:





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



In [3]:









    Out[3]:





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

Create an array of 10 fives



In [8]:

    
np.ones(10) * 5









    Out[8]:





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



In [4]:









    Out[4]:





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

Create an array of the integers from 10 to 50



In [9]:

    
np.arange(10,51)









    Out[9]:





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])



In [5]:









    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 [11]:

    
np.arange(10,51,2)









    Out[11]:





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



In [6]:









    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 [16]:

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









    Out[16]:





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



In [7]:









    Out[7]:





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

Create a 3x3 identity matrix



In [17]:

    
np.eye(3)









    Out[17]:





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



In [8]:









    Out[8]:





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

Use NumPy to generate a random number between 0 and 1



In [27]:

    
np.random.rand(1)









    Out[27]:





array([ 0.47954395])



In [15]:









    Out[15]:





array([ 0.42829726])

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



In [35]:

    
np.random.randn(25)









    Out[35]:





array([-0.31907033, -1.00491984, -1.02068939,  0.08781748,  0.31882811,
        1.32579583,  1.13326008,  1.80520484,  1.65929981,  0.49531155,
       -0.33458763,  1.14882226,  0.51506638,  1.37138312,  0.60201169,
       -0.9178349 ,  1.57705732, -0.47141617,  0.38448079, -1.3639792 ,
       -0.25324191,  0.96868821,  0.24434625,  0.06067706, -0.5537109 ])



In [33]:









    Out[33]:





array([ 1.32031013,  1.6798602 , -0.42985892, -1.53116655,  0.85753232,
        0.87339938,  0.35668636, -1.47491157,  0.15349697,  0.99530727,
       -0.94865451, -1.69174783,  1.57525349, -0.70615234,  0.10991879,
       -0.49478947,  1.08279872,  0.76488333, -2.3039931 ,  0.35401124,
       -0.45454399, -0.64754649, -0.29391671,  0.02339861,  0.38272124])

Create the following matrix:



In [52]:

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









    Out[52]:





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.  ]])



In [35]:









    Out[35]:





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 [36]:

    
np.linspace(0,1,20)









    Out[36]:





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.        ])



In [36]:









    Out[36]:





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 [38]:

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









    Out[38]:





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 [40]:

    
mat[2:,1:]









    Out[40]:





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



In [40]:









    Out[40]:





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



In [41]:

    
mat[3,4]









    Out[41]:





20



In [41]:









    Out[41]:





20



In [46]:

    
mat[:3,1:2]









    Out[46]:





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



In [42]:









    Out[42]:





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



In [49]:

    
mat[4:5,]









    Out[49]:





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



In [46]:









    Out[46]:





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



In [50]:

    
mat[3:5]









    Out[50]:





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



In [49]:









    Out[49]:





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 [55]:

    
mat.sum()









    Out[55]:





325



In [50]:









    Out[50]:





325

Get the standard deviation of the values in mat



In [56]:

    
mat.std()









    Out[56]:





7.2111025509279782



In [51]:









    Out[51]:





7.2111025509279782

Get the sum of all the columns in mat



In [57]:

    
mat.sum(axis=0)









    Out[57]:





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



In [53]:









    Out[53]:





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