Numpy Exercises




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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]:
np.zeros(10)# CODE HERE


Out[2]:
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

In [3]:
np.zeros(10)


Out[3]:
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

Create an array of 10 ones


In [4]:
np.ones(10)


Out[4]:
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])

In [5]:
np.ones(10)


Out[5]:
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])

Create an array of 10 fives


In [12]:
np.full(10, 5)


Out[12]:
array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5])

In [13]:
np.full(10, 5.0)


Out[13]:
array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])

Create an array of the integers from 10 to 50


In [15]:
np.arange(10, 51, 1)


Out[15]:
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 [ ]:
# CODE HERE

In [16]:
np.arange(10, 51, 2)


Out[16]:
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 [18]:
np.arange(0, 9, 1).reshape(3, 3)


Out[18]:
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 [19]:
np.eye(3)


Out[19]:
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 [ ]:
# CODE HERE

In [20]:
np.random.random(1)


Out[20]:
array([0.50082736])

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


In [24]:
np.random.randn(25)


Out[24]:
array([ 0.04332842, -0.05098551,  1.45636821, -0.99417831, -0.61773137,
        0.15523144, -0.05143545,  0.46982571, -0.74769964, -0.08826588,
        1.44253401, -0.54903565,  0.80612954, -0.14239433, -1.32478705,
        0.16239655, -0.41474045, -1.34372289,  0.09029024,  1.45097265,
       -0.08096682, -0.68899642, -0.9975957 ,  0.24343583, -0.07277654])

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 [35]:
np.arange(0.01, 1.01, 0.01).reshape(10, 10)


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.  ]])

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 [37]:
# HERE IS THE GIVEN MATRIX CALLED MAT
# USE IT FOR THE FOLLOWING TASKS
mat = np.arange(1,26).reshape(5,5)
mat


Out[37]:
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 [53]:
mat[:4,1:2]


Out[53]:
array([[ 2],
       [ 7],
       [12],
       [17]])

In [52]:



Out[52]:
array([[ 2],
       [ 7],
       [12],
       [17]])

In [55]:
mat[4,:]


Out[55]:
array([21, 22, 23, 24, 25])

In [46]:



Out[46]:
array([21, 22, 23, 24, 25])

In [57]:
mat[3:5,:]


Out[57]:
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 [59]:
mat.sum()


Out[59]:
325

In [50]:



Out[50]:
325

Get the standard deviation of the values in mat


In [61]:
mat.std()


Out[61]:
7.211102550927978

In [51]:



Out[51]:
7.2111025509279782

Get the sum of all the columns in mat


In [62]:
sum(mat)


Out[62]:
array([55, 60, 65, 70, 75])

In [53]:



Out[53]:
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 [66]:
np.random.seed(seed=None)

Great Job!