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Machine Learning

Data Manipulation

Web Development

What is a possible output of the following snippet:

import numpy as np

np.random.randn(4)

Generator(PCG64) at 0x16A64865AC0

array([[-0.18875073,  1.31687851],
       [ 0.02747433,  0.22661943]])

TypeError: 'float' object cannot be interpreted as an integer

array([-0.26110651,  2.17068264, -0.53384938, -1.93148138])

How do you add label to the vertical axis of a plot?

plt.label("feature")

plt.set_axis_label("feature")

plt.xlabel("feature")

plt.ylabel("feature")

import matplotlib.pyplot as plt

Which of the plots below corresponds to the following snippet:

import numpy as np

xdata = np.linspace(0, 10, 11)

ydata = [5, 11, 10, 9, 11, 13, 14, 9, 13, 15, 10]

plt.plot(xdata, ydata, '*g')

Image failed to load: Plot n.6

Image failed to load: plot n.7

Image failed to load: plot n.8

Image failed to load: Plot n. 5

100%

What does plt.savefig("figure.png") do?

Displays the plot.

Saves the plot as a JPG file.

Saves the plot as a PNG file.

Deletes the figure.

What is the output of the following code:

import numpy as np

A = np.array([[200, 300],

[100, 400]])

B = np.array([[150, 100],

[400, 100]])

result = A + B

print(result)

ValueError: operands could not be broadcast together with different shapes

[[ 350  400]
 [500 500]]

✅

[[ 300  700]
 [550 200]]

[[200 300]
 [100 400]
 [150 100]
 [400 100]]

How do you create a NumPy array from a list [1, 2, 3, 4]?

np.array([1,2,3,4])

np.array{1,2,3,4}

np.array(1,2,3,4)

np.list(1,2,3,4)

What is the output of the following snippet:

import numpy as np

import math

print(np.round(list(map(math.exp,[0,1,2])),2))

AttributeError: module 'math' has no attribute 'exp'

[1.   2.72 7.39]

[1.   2.7 7.4]

[1.0, 2.718281828459045, 7.38905609893065]

Which of the following functions is used to compute the square root in the math module?

math.power(x, 2)

math.sqr(x)

math.sqrt(x)

math.square(x)

What is the result of the following code:

import numpy as np

ar = np.array([0.5, 1, 7, 66, 234, 1000, 0.01])

print(ar[3:6])

[  66.  234. 1000.]

✅

[  66.  234. 1000. 0.01]

[  7.  66. 234.]

[   7.   66.  234. 1000.]