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.

Practice

1. What does the histplot function primarily display in a distribution plot?

easy

A. Bars showing the frequency of data values in bins

B. A smooth curve estimating the data density

C. Scatter points representing individual data values

D. A line chart showing data over time

Solution

1. Step 1: Understand the purpose of histplot

   The histplot function creates a histogram, which groups data into bins and shows how many values fall into each bin using bars.

2. Step 2: Differentiate from kdeplot

   Unlike kdeplot which draws a smooth curve, histplot uses bars to represent frequency counts.

3. Final Answer:

   Bars showing the frequency of data values in bins -> Option A

4. Quick Check:

   histplot = bars frequency [OK]

Hint: Histplot uses bars for counts, kdeplot uses smooth curves [OK]

Common Mistakes:

• Confusing histplot with kdeplot
• Thinking histplot shows individual points
• Assuming histplot draws lines instead of bars

2. Which of the following is the correct way to import seaborn and plot a histogram of a list data using histplot?

easy

A. import seaborn as sb
sb.kdeplot(data)

B. import seaborn
seaborn.plot_hist(data)

C. from seaborn import histplot
kdeplot(data)

D. import seaborn as sns
sns.histplot(data)

Solution

1. Step 1: Check the standard import for seaborn

   The common alias for seaborn is sns, so import seaborn as sns is correct.

2. Step 2: Verify the function call for histogram

   The function to plot a histogram is histplot, so sns.histplot(data) is the correct syntax.

3. Final Answer:

   import seaborn as sns sns.histplot(data) -> Option D

4. Quick Check:

   Standard import + histplot call = import seaborn as sns sns.histplot(data) [OK]

Hint: Use 'import seaborn as sns' and call sns.histplot(data) [OK]

Common Mistakes:

• Using wrong alias like sb
• Calling non-existent seaborn.plot_hist
• Importing histplot directly from seaborn

3. What will the following code output?

import seaborn as sns
import matplotlib.pyplot as plt
data = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4]
sns.kdeplot(data)
plt.show()

medium

A. A smooth curve estimating the data density

B. A bar chart showing counts of each number

C. A scatter plot of the data points

D. An error because kdeplot needs a DataFrame

Solution

1. Step 1: Understand what sns.kdeplot does

   The kdeplot function draws a smooth curve estimating the probability density of the data.

2. Step 2: Check the input data type

   The input is a list of numbers, which is valid for kdeplot, so no error occurs.

3. Final Answer:

   A smooth curve estimating the data density -> Option A

4. Quick Check:

   kdeplot = smooth density curve [OK]

Hint: kdeplot draws smooth curves, not bars or points [OK]

Common Mistakes:

• Expecting bars instead of curve
• Thinking kdeplot requires DataFrame input
• Confusing kdeplot with scatter plot

4. The following code does not display a plot. What is the most likely cause?

import seaborn as sns
data = [1, 2, 3, 4, 5]
sns.histplot(data, kde=True)

medium

A. Data must be a DataFrame, not a list

B. The parameter 'kde' is not valid for histplot

C. Missing import of matplotlib.pyplot to show the plot

D. histplot cannot plot numeric data

Solution

1. Step 1: Check if plot display is handled

   The code calls sns.histplot but does not call plt.show() to display the plot.

2. Step 2: Identify missing import and display call

   Without importing matplotlib.pyplot and calling plt.show(), the plot won't appear, which may be mistaken as an error.

3. Final Answer:

   Missing import of matplotlib.pyplot to show the plot -> Option C

4. Quick Check:

   Need plt.show() to display plot [OK]

Hint: Always import matplotlib.pyplot and call plt.show() to display plots [OK]

Common Mistakes:

• Assuming 'kde' param is invalid for histplot
• Thinking list data causes error
• Believing histplot can't plot numeric data

5. You have two groups of data: group1 = [1,2,2,3,4] and group2 = [3,4,4,5,6]. You want to compare their distributions on the same plot using seaborn. Which code correctly overlays their KDE plots with different colors?

hard

A. import seaborn as sns
import matplotlib.pyplot as plt
data = group1 + group2
labels = ['group1', 'group2']
sns.kdeplot(data, hue=labels)
plt.show()

B. import seaborn as sns
import matplotlib.pyplot as plt
sns.kdeplot(group1, color='blue')
sns.kdeplot(group2, color='red')
plt.show()

C. import seaborn as sns
sns.histplot(group1, color='blue')
sns.histplot(group2, color='red')

D. import seaborn as sns
sns.kdeplot(group1)
sns.kdeplot(group2)
plt.show()

Solution

1. Step 1: Understand how to overlay KDE plots

   Calling sns.kdeplot twice with different data and colors overlays the KDE curves on the same plot.

2. Step 2: Check for plot display

   Importing matplotlib.pyplot and calling plt.show() ensures the plot appears.

3. Step 3: Evaluate other options

   import seaborn as sns
   import matplotlib.pyplot as plt
   data = group1 + group2
   labels = ['group1']*len(group1) + ['group2']*len(group2)
   sns.kdeplot(data, hue=labels)
   plt.show()

   uses hue incorrectly on a list without DataFrame;

   import seaborn as sns
   sns.histplot(group1, color='blue')
   sns.histplot(group2, color='red')

   uses histplot, not KDE;

   import seaborn as sns
   sns.kdeplot(group1)
   sns.kdeplot(group2)
   plt.show()

   misses import matplotlib.pyplot as plt.

4. Final Answer:

   import seaborn as sns import matplotlib.pyplot as plt sns.kdeplot(group1, color='blue') sns.kdeplot(group2, color='red') plt.show() -> Option B

5. Quick Check:

   Overlay KDE with sns.kdeplot twice + plt.show() [OK]

Hint: Call sns.kdeplot twice with colors, then plt.show() [OK]

Common Mistakes:

• Using hue on plain lists without DataFrame
• Forgetting plt.show() to display plot
• Mixing histplot with kdeplot for smooth curves