Heatmaps for correlation in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the correlation matrix of the DataFrame df.

Data Analysis Python

corr_matrix = df.[1]()

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Asum

Bmean

Ccorr

Ddescribe

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean() or sum() instead of corr().

Trying to use describe() which gives summary statistics, not correlation.

The corr() method calculates the correlation matrix between numerical columns in a DataFrame.

2fill in blank

medium

Complete the code to import the seaborn library with its common alias.

Data Analysis Python

import [1] as sns

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Aseaborn

Bmatplotlib

Cpandas

Dnumpy

Attempts:

3 left

💡 Hint

Common Mistakes

Importing matplotlib or pandas instead of seaborn.

Not using the alias sns.

Seaborn is commonly imported as sns for creating statistical visualizations like heatmaps.

3fill in blank

hard

Fix the error in the code to create a heatmap of the correlation matrix corr_matrix.

Data Analysis Python

sns.heatmap([1], annot=True, cmap='coolwarm')

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Adf

Bcorr_matrix

Cdata

Dplt

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the original DataFrame instead of the correlation matrix.

Passing matplotlib or unrelated variables.

The heatmap function needs the correlation matrix as input to visualize it.

4fill in blank

hard

Fill both blanks to create a heatmap with a title and show the plot.

Data Analysis Python

sns.heatmap(corr_matrix, annot=True, cmap=[1])
plt.[2]()

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A'viridis'

Bshow

C'coolwarm'

Dplot

Attempts:

3 left

💡 Hint

Common Mistakes

Using an unrelated color map.

Forgetting to call plt.show().

The cmap parameter sets the color scheme; 'coolwarm' is common for correlations. plt.show() displays the plot.

5fill in blank

hard

Fill all three blanks to import matplotlib, set figure size, and create a heatmap with annotations.

Data Analysis Python

import [1] as plt
plt.figure(figsize=([2], 6))
sns.heatmap(corr_matrix, annot=[3], cmap='coolwarm')

Drag options to blanks, or click blank then click option'

Amatplotlib.pyplot

B10

CTrue

Dseaborn

Attempts:

3 left

💡 Hint

Common Mistakes

Importing seaborn as plt.

Setting figure size with wrong values.

Not enabling annotations.

We import matplotlib.pyplot as plt, set figure width to 10, and enable annotations with annot=True.

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