Heatmaps for correlation in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of correlation heatmap data

What is the shape of the correlation matrix produced by this code?

Data Analysis Python

import pandas as pd
import numpy as np

data = pd.DataFrame(np.random.rand(10, 5), columns=list('ABCDE'))
corr_matrix = data.corr()
print(corr_matrix.shape)

A (5, 10)

B (10, 5)

C (5, 5)

D (10, 10)

💡 Hint

The correlation matrix compares each column with every other column.

The correlation matrix shape is (number_of_columns, number_of_columns). Here, 5 columns produce a 5x5 matrix.

❓ data_output

intermediate

Correlation values from heatmap data

What is the correlation value between columns 'A' and 'B' in this dataset?

Data Analysis Python

import pandas as pd
import numpy as np
np.random.seed(0)
data = pd.DataFrame({
    'A': np.arange(5),
    'B': np.arange(5) * 2,
    'C': np.random.rand(5)
})
corr = data.corr()
print(round(corr.loc['A', 'B'], 2))

A 1.00

B 0.50

C -1.00

D 0.00

💡 Hint

Columns 'A' and 'B' have a perfect linear relationship.

Since 'B' is exactly twice 'A', their correlation is 1.00.

❓ visualization

advanced

Identify the correct heatmap color scale

Which heatmap color scale correctly represents correlations from -1 (blue) to 1 (red) with 0 as white?

Data Analysis Python

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

np.random.seed(1)
data = pd.DataFrame(np.random.randn(10, 4), columns=list('WXYZ'))
corr = data.corr()

sns.heatmap(corr, cmap='coolwarm')
plt.show()

A 'viridis'

B 'Greens'

C 'Blues'

D 'coolwarm'

💡 Hint

The color scale should show blue for negative, white for zero, and red for positive.

'coolwarm' is a diverging color map that shows blue to red with white in the middle, ideal for correlation heatmaps.

🔧 Debug

advanced

Error in heatmap correlation calculation

What error does this code raise when trying to plot a heatmap of correlations?

Data Analysis Python

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

data = pd.DataFrame({'A': [1, 2, 3], 'B': ['x', 'y', 'z']})
corr = data.corr()
sns.heatmap(corr)
plt.show()

A No error, heatmap displays correctly

B KeyError: 'B'

C ValueError: No numeric types to aggregate

D TypeError: unsupported operand type(s) for -: 'str' and 'int'

💡 Hint

data.corr() ignores non-numeric columns.

No error is raised. Column 'B' is non-numeric, so data.corr() only uses numeric column 'A', producing a 1x1 correlation matrix that displays correctly.

🚀 Application

expert

Interpreting correlation heatmap for feature selection

Given this correlation heatmap matrix, which feature should be dropped to reduce multicollinearity?

Data Analysis Python

import pandas as pd
import numpy as np

np.random.seed(42)
data = pd.DataFrame({
    'X1': np.random.rand(100),
    'X2': np.random.rand(100),
    'X3': np.random.rand(100) * 0.5 + np.random.rand(100) * 0.5,
    'X4': np.random.rand(100) * 0.9 + np.random.rand(100) * 0.1
})
data['X3'] = data['X1'] * 0.95 + np.random.rand(100) * 0.05
corr = data.corr()
print(corr.round(2))

A Drop X2 because it has low correlation with others

B Drop X3 because it is highly correlated with X1

C Drop X4 because it has the highest variance

D Drop X1 because it has the lowest mean

💡 Hint

Look for pairs with correlation close to 1 or -1.

X3 is almost perfectly correlated with X1 (around 0.95), causing multicollinearity. Dropping X3 reduces redundancy.