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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.

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.])

Create an array of 10 ones¶

In [3]:

np.ones(10)

Out[3]:

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

Create an array of 10 fives¶

In [4]:

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 [6]:

np.arange(10,51)

Out[6]:

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 [7]:

np.arange(10,51,2)

Out[7]:

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 [10]:

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

Out[10]:

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

Create a 3x3 identity matrix¶

In [11]:

np.eye(3,3)

Out[11]:

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

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

In [16]:

np.random.rand(1)

Out[16]:

array([0.21583671])

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

In [20]:

np.random.randn(25)

Out[20]:

array([-0.39344792,  1.52013611, -1.18768351, -0.9827006 , -1.46939512,
        1.32498892, -0.39663186,  0.85490399,  0.42723513, -1.51332761,
        0.39637203,  0.11058796, -1.01900223, -0.6089908 ,  0.47295978,
       -2.10033323, -1.49908175, -0.74565204,  0.24846951, -0.78763842,
       -0.65721393,  1.26161763, -0.63824932, -2.1883971 ,  1.73655173])

Create the following matrix:¶

In [55]:

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

Out[55]:

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 [23]:

np.linspace(0,1,20)

Out[23]:

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 [25]:

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

Out[25]:

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 [53]:

mat[2:,1:]

Out[53]:

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 [27]:

mat[3,4]

Out[27]:

In [41]:

Out[41]:

In [54]:

mat[:3,1:2]

Out[54]:

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

In [42]:

Out[42]:

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

In [51]:

mat[4,:]

Out[51]:

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

In [46]:

Out[46]:

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

In [35]:

mat[3:,0:]

Out[35]:

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 [48]:

mat.sum()

Out[48]:

Get the standard deviation of the values in mat¶