Practice Set on Numpy in Python for Data Science

Practice Set on Numpy in Python for Data Science

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.
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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 [5]:
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]:
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]:
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]:
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 [15]:
np.random.rand(1)



Out[15]:
array([ 0.42829726])

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

In [33]:
np.random.randn(25)



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


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. ])

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 [39]:
# 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 [40]:
mat[2:,1:]



Out[40]:
array([[12, 13, 14, 15], [17, 18, 19, 20], [22, 23, 24, 25]])



In [29]:

# 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 [41]:
mat[3,4]



Out[41]:
20



In [30]:

# 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 [42]:
mat[:3,1:2]



Out[42]:
array([[ 2], [ 7], [12]])



In [31]:

# 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 [46]:
mat[4,:]



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



In [32]:

# 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 [49]:
mat[3:5,:]



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 [50]:
mat.sum()



Out[50]:
325

Get the standard deviation of the values in mat

In [51]:
mat.std()



Out[51]:
7.2111025509279782

Get the sum of all the columns in mat

In [53]:
mat.sum(axis=0)



Out[53]:
array([55, 60, 65, 70, 75])



In [ ]:
#Result are based on lab test on radom data..