Trend lines on scatter plots in Matplotlib - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to plot a scatter plot using matplotlib.

Matplotlib

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]

plt.[1](x, y)
plt.show()

Drag options to blanks, or click blank then click option'

Ascatter

Bplot

Cbar

Dhist

Attempts:

3 left

💡 Hint

Common Mistakes

Using plt.plot instead of plt.scatter for scatter plots.

Using plt.bar which is for bar charts.

The scatter function creates a scatter plot, which is used to show points on a graph.

2fill in blank

medium

Complete the code to calculate the slope and intercept of a linear trend line using numpy.

Matplotlib

import numpy as np

x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 3, 5, 7, 11])

slope, intercept = np.polyfit(x, y, [1])

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A0

B2

C3

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using 2 or higher degree which fits curves, not lines.

Using 0 which is a constant.

The degree of the polynomial for a linear fit is 1.

3fill in blank

hard

Fix the error in the code to plot the trend line on the scatter plot.

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 3, 5, 7, 11])

slope, intercept = np.polyfit(x, y, 1)

plt.scatter(x, y)
plt.plot(x, slope * [1] + intercept, color='red')
plt.show()

Drag options to blanks, or click blank then click option'

Ay

Bx

Cslope

Dintercept

Attempts:

3 left

💡 Hint

Common Mistakes

Using y instead of x in the line equation.

Using slope or intercept alone without x.

The trend line is calculated as slope times x plus intercept, so x must be used in the plot.

4fill in blank

hard

Fill both blanks to create a dictionary of squared values for numbers greater than 3.

Matplotlib

numbers = [1, 2, 3, 4, 5]
squares = {num: num[1]2 for num in numbers if num [2] 3}

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A**

B>

C<

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using * instead of ** for power.

Using < instead of > in the condition.

Use ** for exponentiation and > to filter numbers greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase keys and values greater than 0.

Matplotlib

data = {'a': 1, 'b': -2, 'c': 3}
result = [1]: [2] for [3], [2] in data.items() if [2] > 0

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Ak.upper()

Bv

Ck

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'item' as loop variable without unpacking.

Not converting keys to uppercase.

Keys are converted to uppercase with k.upper(), values are v, and the loop variable is k and v unpacked from data.items().