Why performance matters with big datasets 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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Aseaborn

Bmatplotlib.pyplot

Cpandas

Dnumpy

Attempts:

3 left

💡 Hint

Common Mistakes

Importing the whole matplotlib instead of pyplot.

Using unrelated libraries like pandas or numpy.

We import matplotlib.pyplot as plt to create plots.

2fill in blank

medium

Complete the code to create a scatter plot of x and y data.

Matplotlib

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

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Ascatter

Bbar

Cplot

Dhist

Attempts:

3 left

💡 Hint

Common Mistakes

Using plot which connects points with lines.

Using bar or hist which are for different chart types.

The scatter function creates a scatter plot, which is good for showing many points.

3fill in blank

hard

Fix the error in the code to plot a large dataset efficiently by reducing point size.

Matplotlib

plt.scatter(x, y, s=[1])
plt.show()

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A1

B0

C100

D-5

Attempts:

3 left

💡 Hint

Common Mistakes

Using zero or negative sizes which cause errors or no points.

Using large sizes that slow down rendering.

Setting s=1 makes points very small, improving performance and clarity with big data.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters words longer than 5 letters and stores their lengths.

Matplotlib

{word: [1] for word in words if len(word) [2] 5}

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Alen(word)

Bword

C>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using wrong comparison operators.

The dictionary stores each word's length if the word is longer than 5 letters.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that stores uppercase words as keys, their counts as values, only if count is greater than 1.

Matplotlib

{ [1]: [2] for word, count in word_counts.items() if count [3] 1 }

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Aword.upper()

Bcount

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using original words instead of uppercase.

Using wrong comparison operators or values.

This comprehension creates a dictionary with uppercase words as keys and their counts as values, filtering counts greater than 1.