Correlation matrix visualization in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of correlation matrix heatmap code

What will be the output of the following Python code that uses matplotlib and pandas to visualize a correlation matrix?

Matplotlib

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

data = pd.DataFrame({
    'A': [1, 2, 3, 4, 5],
    'B': [5, 4, 3, 2, 1],
    'C': [2, 3, 2, 3, 2]
})

corr = data.corr()
plt.imshow(corr, cmap='coolwarm', interpolation='none')
plt.colorbar()
plt.xticks(range(len(corr)), corr.columns)
plt.yticks(range(len(corr)), corr.columns)
plt.title('Correlation Matrix Heatmap')
plt.show()

A A line plot of the correlation values over the index with labels A, B, C on x-axis.

B A scatter plot of columns A vs B with a colorbar showing correlation values.

C A heatmap image showing correlation values with colors from blue (negative) to red (positive), labeled axes A, B, C, and a colorbar.

D A bar chart showing the correlation coefficients of each column with column A.

💡 Hint

Think about what plt.imshow does with a correlation matrix and the color map used.

The code calculates the correlation matrix and uses plt.imshow to display it as a heatmap with colors representing correlation values. The axes are labeled with column names and a colorbar shows the scale.

❓ data_output

intermediate

Number of cells in correlation matrix heatmap

Given a DataFrame with 4 columns, how many cells will the correlation matrix heatmap display?

Matplotlib

import pandas as pd
import numpy as np

data = pd.DataFrame(np.random.rand(10,4), columns=['W', 'X', 'Y', 'Z'])
corr = data.corr()
print(corr.shape)

A 16

B 8

C 4

D 10

💡 Hint

Correlation matrix is square with size equal to number of columns squared.

The correlation matrix shape is (4,4), so it has 16 cells representing pairwise correlations.

🔧 Debug

advanced

Identify the error in correlation heatmap code

What error will this code raise when trying to plot a correlation matrix heatmap?

Matplotlib

import pandas as pd
import matplotlib.pyplot as plt

data = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})
corr = data.corr()
plt.imshow(corr, cmap='viridis')
plt.xticks(corr.columns)
plt.yticks(corr.columns)
plt.show()

A ValueError: shape mismatch between ticks and data

B TypeError: 'Index' object cannot be interpreted as an integer

C SyntaxError: invalid syntax in plt.xticks line

D No error, the plot will display correctly

💡 Hint

Check the arguments passed to plt.xticks and plt.yticks.

plt.xticks and plt.yticks expect positions (integers) and labels, but corr.columns is an Index object of strings without positions, causing a TypeError.

🚀 Application

advanced

Best way to add correlation values on heatmap cells

Which code snippet correctly adds correlation coefficient numbers on each cell of a matplotlib heatmap of a correlation matrix?

Matplotlib

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

data = pd.DataFrame(np.random.rand(5,3), columns=['A','B','C'])
corr = data.corr()
fig, ax = plt.subplots()
cax = ax.matshow(corr, cmap='coolwarm')
fig.colorbar(cax)
# Add correlation values here
plt.show()

A

for i in range(len(corr)):
    for j in range(len(corr)):
        ax.text(j, i, f'{corr.iloc[i,j]:.2f}', ha='center', va='center', color='white')

B

for i in range(len(corr)):
    for j in range(len(corr)):
        ax.text(i, j, f'{corr.iloc[i,j]:.2f}')

C

for i, j in corr:
    ax.text(i, j, f'{corr[i,j]:.2f}', ha='center', va='center')

D

for (i, j), val in np.ndenumerate(corr.values):
    ax.text(j, i, f'{val:.2f}', ha='center', va='center', color='black')

💡 Hint

Use np.ndenumerate to get row and column indices and values.

Option D correctly iterates over all matrix positions and places text at (j, i) with horizontal and vertical center alignment and visible color.

🧠 Conceptual

expert

Interpretation of correlation matrix heatmap colors

In a correlation matrix heatmap using the 'coolwarm' colormap, what does a deep blue color in a cell represent?

A Strong negative correlation close to -1

B Strong positive correlation close to +1

C No correlation close to 0

D Missing data or NaN value

💡 Hint

Recall the color scheme of 'coolwarm' where blue and red represent opposite ends.

In 'coolwarm', blue colors represent negative values, so deep blue means strong negative correlation near -1.