Why line plots show trends in Matplotlib - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the matplotlib plotting library.

Matplotlib

import [1] as plt

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Amatplotlib.pyplot

Bseaborn

Cpandas

Dnumpy

Attempts:

3 left

💡 Hint

Common Mistakes

Importing the wrong library like numpy or pandas for plotting.

Forgetting to import pyplot specifically.

We import matplotlib.pyplot as plt to create line plots.

2fill in blank

medium

Complete the code to plot a line graph of y values.

Matplotlib

plt.[1]([1, 2, 3, 4], [10, 20, 25, 30])
plt.show()

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Ascatter

Bplot

Cline

Dbar

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'scatter' which only plots points without lines.

Using 'bar' which creates bar charts.

The plot function draws line plots connecting points.

3fill in blank

hard

Fix the error in the code to correctly label the x-axis.

Matplotlib

plt.plot([1, 2, 3], [4, 5, 6])
plt.xlabel([1])
plt.show()

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A'x label'

Bx label

Cx_label

Dxlabel

Attempts:

3 left

💡 Hint

Common Mistakes

Passing label without quotes causing a NameError.

Using wrong parameter names.

Axis labels must be strings, so use quotes around the label text.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

Matplotlib

{word: [1] for word in words if [2]

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Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using the wrong condition like word > 3 which compares strings to numbers.

The dictionary maps each word to its length, but only includes words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their counts only if count is positive.

Matplotlib

{ [1]: [2] for [3] in data.items() if [2] > 0 }

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Aword.upper()

Bcount

Cword

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using data instead of iterating over data.items().

Not converting words to uppercase for keys.

Using count without defining it in the loop.

This comprehension creates a dictionary with uppercase words as keys and their counts as values, including only positive counts.