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.

Practice

1. What does a heatmap of correlation typically show in data analysis?

easy

A. The total sum of all numerical columns

B. The frequency of each value in a dataset

C. The order of rows in a DataFrame

D. The strength and direction of relationships between variables

Solution

1. Step 1: Understand correlation meaning

   Correlation measures how two variables move together, either positively or negatively.

2. Step 2: Relate heatmap to correlation

   A heatmap uses colors to show these correlation strengths and directions between variables.

3. Final Answer:

   The strength and direction of relationships between variables -> Option D

4. Quick Check:

   Heatmap = Correlation strength/direction [OK]

Hint: Heatmaps color-code relationships between variables [OK]

Common Mistakes:

• Confusing correlation with frequency counts
• Thinking heatmaps show raw data values
• Assuming heatmaps show data order

2. Which of the following is the correct way to create a heatmap of correlations using seaborn in Python?

easy

A. sns.heatmap(data.corr())

B. sns.heatmap(data.corr)

C. sns.heatmap(corr(data))

D. sns.heatmap(data.correlation())

Solution

1. Step 1: Recall pandas correlation method

   In pandas, correlation is calculated by calling data.corr() with parentheses.

2. Step 2: Use seaborn heatmap correctly

   Seaborn's heatmap() accepts a matrix like the correlation matrix from data.corr().

3. Final Answer:

   sns.heatmap(data.corr()) -> Option A

4. Quick Check:

   Use parentheses for corr() method [OK]

Hint: Use data.corr() with parentheses inside sns.heatmap() [OK]

Common Mistakes:

• Omitting parentheses after corr
• Using non-existent corr() functions
• Passing raw data instead of correlation matrix

3. Given the following code, what will be the output of corr_matrix?

import pandas as pd
import numpy as np
data = pd.DataFrame({
  'A': [1, 2, 3, 4],
  'B': [4, 3, 2, 1],
  'C': [1, 3, 5, 7]
})
corr_matrix = data.corr()

medium

A. An error because data has different ranges

B. A matrix showing negative correlation between A and B, positive between A and C

C. A matrix with only positive correlations

D. A matrix showing all zeros because data is small

Solution

1. Step 1: Analyze data columns

   Column A increases, B decreases (inverse), C increases faster than A.

2. Step 2: Understand correlation signs

   A and B have negative correlation, A and C have positive correlation, B and C negative correlation.

3. Final Answer:

   A matrix showing negative correlation between A and B, positive between A and C -> Option B

4. Quick Check:

   Opposite trends = negative correlation [OK]

Hint: Opposite value trends mean negative correlation [OK]

Common Mistakes:

• Assuming small data means zero correlation
• Ignoring sign of correlation
• Expecting errors from different value ranges

4. You wrote this code to plot a heatmap but get an error:

import seaborn as sns
import pandas as pd
data = pd.DataFrame({'X':[1,2,3],'Y':[4,5,6]})
sns.heatmap(data.corr)

What is the error and how to fix it?

medium

A. DataFrame columns must be numeric for heatmap

B. Seaborn heatmap does not accept correlation matrices

C. Missing parentheses after corr; fix by using data.corr()

D. Missing import matplotlib.pyplot as plt

Solution

1. Step 1: Identify the error cause

   Calling data.corr without parentheses passes a method, not the correlation matrix.

2. Step 2: Correct the method call

   Add parentheses to call the method: data.corr() to get the matrix.

3. Final Answer:

   Missing parentheses after corr; fix by using data.corr() -> Option C

4. Quick Check:

   Method call needs parentheses [OK]

Hint: Always use parentheses to call methods like corr() [OK]

Common Mistakes:

• Passing method instead of result
• Assuming heatmap can't use correlation matrix
• Forgetting to import seaborn

5. You have a DataFrame with columns 'Age', 'Income', 'SpendingScore', and 'Savings'. You want to create a heatmap showing only correlations above 0.5 to focus on strong relationships. Which code snippet correctly creates this filtered heatmap?

hard

A. corr = data.corr(); sns.heatmap(corr.where(corr > 0.5))

B. corr = data.corr(); sns.heatmap(corr > 0.5)

C. corr = data.corr(); sns.heatmap(corr.filter(lambda x: x > 0.5))

D. corr = data.corr(); sns.heatmap(corr.dropna(thresh=0.5))

Solution

1. Step 1: Calculate correlation matrix

   Use data.corr() to get all correlations.

2. Step 2: Filter correlations above 0.5

   Use corr.where(corr > 0.5) to keep values above 0.5 and set others to NaN.

3. Step 3: Plot heatmap with filtered matrix

   Pass the filtered matrix to sns.heatmap() to visualize only strong correlations.

4. Final Answer:

   corr = data.corr(); sns.heatmap(corr.where(corr > 0.5)) -> Option A

5. Quick Check:

   Use where() to filter matrix values [OK]

Hint: Use corr.where(condition) to filter heatmap values [OK]

Common Mistakes:

• Using filter() which works on columns, not matrix values
• Trying to index matrix with boolean mask directly
• Dropping rows instead of masking values