Pair plots for feature relationships in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Pair Plot with Seaborn

What will be the output of this code snippet that creates a pair plot for the Iris dataset?

Data Analysis Python

import seaborn as sns
import matplotlib.pyplot as plt
iris = sns.load_dataset('iris')
sns.pairplot(iris, hue='species')
plt.show()

A A grid of scatter plots and histograms showing relationships between Iris features, colored by species

B A bar chart showing average petal length per species

C A single scatter plot of sepal_length vs sepal_width only

D An error because 'species' column does not exist

💡 Hint

Think about what sns.pairplot does with the hue parameter.

The pairplot function creates a matrix of scatter plots for each pair of numerical features and histograms on the diagonal. The hue colors points by species.

❓ data_output

intermediate

Number of Plots in a Pair Plot Matrix

If a dataset has 4 numerical features, how many scatter plots (excluding diagonal histograms) will appear in the pair plot matrix?

A 8

B 16

C 6

D 12

💡 Hint

Count pairs of features without repetition and excluding self-pairs.

For 4 features, the number of unique pairs is 4 choose 2 = 6. Each pair appears twice (x vs y and y vs x), so total scatter plots = 6 * 2 = 12.

🔧 Debug

advanced

Identify the Error in Pair Plot Code

What error will this code produce and why?

Data Analysis Python

import seaborn as sns
import matplotlib.pyplot as plt
df = sns.load_dataset('tips')
sns.pairplot(df, hue='nonexistent_column')
plt.show()

A TypeError because hue must be a boolean

B KeyError because 'nonexistent_column' is not in the DataFrame

C ValueError because tips dataset has no numerical columns

D No error, the plot will show without coloring

💡 Hint

Check if the hue column exists in the dataset.

The hue parameter expects a column name present in the DataFrame. Using a non-existent column causes a KeyError.

❓ visualization

advanced

Interpreting Pair Plot Patterns

In a pair plot of a dataset with features A, B, and C, you see a strong diagonal line in the scatter plot of A vs B. What does this indicate?

A Features A and B have a strong positive linear relationship

B Features A and B are independent

C Features A and B have a strong negative linear relationship

D Features A and B have no correlation

💡 Hint

A diagonal line means one feature increases as the other increases.

A strong diagonal line from bottom-left to top-right shows a positive linear correlation between features A and B.

🚀 Application

expert

Choosing Features for Pair Plot to Detect Clusters

You have a dataset with 10 numerical features and want to use a pair plot to detect natural clusters. Which approach is best to select features for the pair plot?

A Select features randomly to avoid bias

B Select features with the lowest variance to reduce noise

C Select features with highest variance and low correlation among them

D Select features that are highly correlated to each other

💡 Hint

Features that vary a lot and are not redundant help reveal clusters.

Choosing features with high variance and low correlation helps show distinct clusters without redundant information.