Distribution plots (histplot, kdeplot) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of histplot with bins parameter

What is the output of this code snippet that uses seaborn's histplot with bins=3 on a small dataset?

Data Analysis Python

import seaborn as sns
import matplotlib.pyplot as plt
data = [1, 2, 2, 3, 4, 5]
sns.histplot(data, bins=3)
plt.close()  # Prevent plot display in test environment
counts = [3, 1, 2]  # Expected counts per bin
print(counts)

A [3, 1, 2]

B [2, 2, 2]

C [1, 3, 2]

D [2, 3, 1]

💡 Hint

Bins split the data range into equal parts; count how many values fall into each bin.

The data values are split into 3 bins: [1-2], (2-3.666], (3.666-5]. The counts per bin are 3, 1, and 2 respectively.

❓ data_output

intermediate

KDE plot density estimate values

Given this code using seaborn kdeplot on a small dataset, what is the approximate density estimate at x=2.0?

Data Analysis Python

import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
data = [1, 2, 2, 3, 4]
kde = sns.kdeplot(data, bw_adjust=0.5)
plt.close()
# Approximate density at x=2.0 is printed below
from scipy.stats import gaussian_kde
kde_scipy = gaussian_kde(data, bw_method=0.5 * np.std(data, ddof=1))
density_at_2 = kde_scipy.evaluate(2.0)[0]
print(round(density_at_2, 2))

A 0.35

B 0.50

C 0.75

D 1.00

💡 Hint

KDE smooths data points; density is higher near repeated values.

The density estimate peaks near repeated values like 2, and with bw_adjust=0.5, the density at 2 is about 0.75.

🔧 Debug

advanced

Identify error in histplot usage

What error does this code raise when trying to plot a histogram with seaborn histplot?

Data Analysis Python

import seaborn as sns
data = [1, 2, 3, 4]
sns.histplot(data, bins='five')

A ValueError: bins must be a positive integer or sequence

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

C SyntaxError: invalid syntax

D No error, plot displays correctly

💡 Hint

Check the type and value of the bins parameter.

The bins parameter expects an integer or a sequence of bin edges, not a string like 'five'.

❓ visualization

advanced

Effect of bandwidth on KDE plot shape

Which option best describes the effect of increasing the bandwidth parameter in seaborn's kdeplot on the shape of the density curve?

A The density curve becomes a flat horizontal line.

B The density curve becomes sharper and more jagged, showing more peaks.

C The density curve shifts to the right without changing shape.

D The density curve becomes smoother and wider, with less detail.

💡 Hint

Bandwidth controls smoothing; higher means more smoothing.

Increasing bandwidth smooths the KDE curve more, making it wider and less detailed.

🧠 Conceptual

expert

Choosing between histplot and kdeplot for data analysis

Which statement best explains when to prefer seaborn's kdeplot over histplot for visualizing data distribution?

A Use kdeplot when you want to see exact counts of data points in fixed intervals.

B Use kdeplot when you want a smooth estimate of the distribution shape without binning artifacts.

C Use histplot only for categorical data visualization.

D Use histplot when you want to visualize relationships between two variables.

💡 Hint

Think about smoothness and binning in distribution plots.

KDE plots provide a smooth curve estimating the probability density function, avoiding bin edges and counts.