Correlation matrix visualization in Matplotlib - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the matplotlib library for plotting.

Matplotlib

import [1] as plt

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Amatplotlib.pyplot

Bseaborn

Cpandas

Dnumpy

Attempts:

3 left

💡 Hint

Common Mistakes

Importing the whole matplotlib instead of pyplot.

Using seaborn or pandas instead of matplotlib.pyplot.

We import matplotlib.pyplot as plt to create plots.

2fill in blank

medium

Complete the code to calculate the correlation matrix from the DataFrame 'df'.

Matplotlib

corr_matrix = df.[1]()

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Acorr

Bcov

Cmean

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using cov() which calculates covariance, not correlation.

Using aggregation functions like mean() or sum().

The corr() method calculates the correlation matrix of a DataFrame.

3fill in blank

hard

Fix the error in the code to display the correlation matrix as a heatmap using matplotlib.

Matplotlib

plt.imshow(corr_matrix, cmap=[1])
plt.colorbar()
plt.show()

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Acoolwarm

B'coolwarm'

C'heatmap'

Dheatmap

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the colormap name without quotes causing a NameError.

Using an invalid colormap name like 'heatmap'.

The colormap name must be a string, so it needs quotes like 'coolwarm'.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each word to its length only if the length is greater than 3.

Matplotlib

lengths = {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 instead of its length as the value.

Comparing the word string directly to a number.

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3 using len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase keys to values only if the value is positive.

Matplotlib

result = { [1]: [2] for k, v in data.items() if [3] > 0 }

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Ak.upper()

Bv

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original key without uppercase conversion.

Using the wrong variable names for keys or values.

The dictionary comprehension uses k.upper() to convert keys to uppercase, maps them to values v, and filters only positive values with v > 0.