Box plot vs violin plot comparison in Matplotlib - Practice Questions

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Box Plot Code

What will be the output of the following Python code that creates a box plot using matplotlib?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

data = np.random.normal(0, 1, 100)
plt.boxplot(data)
plt.savefig('boxplot.png')
plt.close()
print(type(data))

A SyntaxError

B <class 'list'>

C <class 'numpy.ndarray'>

D <class 'matplotlib.figure.Figure'>

💡 Hint

Check the type of the variable 'data' before plotting.

The variable 'data' is created using numpy's random.normal function, which returns a numpy ndarray. The print statement outputs its type.

❓ data_output

intermediate

Data Points in Violin Plot

Given the following code that creates a violin plot, how many data points are used to create the plot?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

data = np.concatenate([np.random.normal(0, 1, 50), np.random.normal(5, 1, 50)])
plt.violinplot(data)
plt.close()
print(len(data))

A 100

B 50

C 150

D 200

💡 Hint

Count the number of elements in each normal distribution and sum them.

The data is created by concatenating two arrays of 50 elements each, so total 100 points.

❓ visualization

advanced

Identify the Plot Type from Description

Which plot type shows the full distribution shape of the data along with median and quartiles?

A Box plot

B Violin plot

C Scatter plot

D Histogram

💡 Hint

One plot uses kernel density estimation to show distribution shape.

Violin plots show the full distribution shape using kernel density estimation, while box plots show summary statistics only.

🧠 Conceptual

advanced

Difference in Outlier Representation

Which statement correctly describes how outliers are represented in box plots and violin plots?

A Neither box plots nor violin plots show outliers explicitly.

B Violin plots show outliers as individual points; box plots do not explicitly mark outliers.

C Both box plots and violin plots show outliers as individual points.

D Box plots show outliers as individual points; violin plots do not explicitly mark outliers.

💡 Hint

Think about how each plot visualizes data extremes.

Box plots mark outliers as points beyond whiskers; violin plots show density but do not mark outliers explicitly.

🔧 Debug

expert

Error in Combined Box and Violin Plot Code

What error will this code produce when trying to plot both box and violin plots on the same axes?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

data = np.random.normal(size=100)
fig, ax = plt.subplots()
ax.boxplot(data)
ax.violinplot(data)
plt.show()

A No error; both plots display correctly.

B RuntimeWarning: Attempting to plot incompatible plot types

C ValueError: data must be 2D array for violinplot

D TypeError: violinplot() got an unexpected keyword argument

💡 Hint

Check if matplotlib supports plotting boxplot and violinplot on same axes.

Matplotlib allows plotting boxplot and violinplot on the same axes without error.

Practice

1. Which of the following best describes the main difference between a box plot and a violin plot?

easy

A. Box plots show the full data distribution shape, while violin plots only show median and quartiles.

B. Box plots show summary statistics like median and quartiles, while violin plots show the full data distribution shape.

C. Both plots show only the mean and standard deviation of the data.

D. Violin plots are used only for categorical data, box plots only for numerical data.

Solution

1. Step 1: Understand box plot features

   Box plots display median, quartiles, and outliers, summarizing key statistics.

2. Step 2: Understand violin plot features

   Violin plots show the full distribution shape using a density curve, revealing data spread and modality.

3. Final Answer:

   Box plots show summary statistics like median and quartiles, while violin plots show the full data distribution shape. -> Option B

4. Quick Check:

   Box = summary stats, Violin = distribution shape [OK]

Hint: Box = stats summary, Violin = full distribution shape [OK]

Common Mistakes:

• Confusing violin plots as only showing summary stats
• Thinking box plots show full distribution shape
• Assuming violin plots are for categorical data only

2. Which of the following is the correct way to create a violin plot using matplotlib for a list of values data?

easy

A. plt.plot.violin(data)

B. plt.boxplot(data)

C. plt.violin(data)

D. plt.violinplot(data)

Solution

1. Step 1: Recall matplotlib violin plot syntax

   The correct function to create a violin plot is plt.violinplot().

2. Step 2: Check other options for correctness

   plt.boxplot() creates box plots, plt.violin() and plt.plot.violin() are invalid functions.

3. Final Answer:

   plt.violinplot(data) -> Option D

4. Quick Check:

   Use plt.violinplot() for violin plots [OK]

Hint: Use plt.violinplot() to create violin plots [OK]

Common Mistakes:

• Using plt.boxplot() instead of plt.violinplot()
• Trying plt.violin() which does not exist
• Using plt.plot.violin() which is invalid

3. Given the following code, what will the output plot show?

import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
data = np.random.normal(0, 1, 100)
plt.boxplot(data)
plt.show()

medium

A. A plot showing the median, quartiles, and outliers of the data.

B. A plot showing the full distribution shape with density curves.

C. A scatter plot of all data points.

D. A line plot of the sorted data values.

Solution

1. Step 1: Identify the plot function used

   The code uses plt.boxplot(data), which creates a box plot.

2. Step 2: Understand box plot output

   Box plots display median, quartiles, and outliers, summarizing data distribution.

3. Final Answer:

   A plot showing the median, quartiles, and outliers of the data. -> Option A

4. Quick Check:

   plt.boxplot() = median, quartiles, outliers [OK]

Hint: plt.boxplot() shows median and quartiles [OK]

Common Mistakes:

• Confusing boxplot with violin plot output
• Expecting density curves from boxplot
• Thinking it shows scatter or line plot

4. The following code is intended to plot a violin plot but produces an error. What is the error?

import matplotlib.pyplot as plt
data = [1, 2, 3, 4, 5]
plt.violin(data)
plt.show()

medium

A. The function plt.violin() does not exist in matplotlib.

B. The data list must be converted to a numpy array first.

C. plt.show() must be called before plt.violin().

D. The data list is too short to plot a violin plot.

Solution

1. Step 1: Check the function name used

   The code calls plt.violin(), which is not a valid matplotlib function.

2. Step 2: Identify correct function for violin plots

   The correct function is plt.violinplot(), not plt.violin().

3. Final Answer:

   The function plt.violin() does not exist in matplotlib. -> Option A

4. Quick Check:

   Use plt.violinplot(), plt.violin() causes error [OK]

Hint: Use plt.violinplot(), not plt.violin() [OK]

Common Mistakes:

• Using plt.violin() instead of plt.violinplot()
• Thinking data type causes error
• Calling plt.show() too early

5. You have two groups of data and want to compare their distributions clearly. Which approach best uses both box and violin plots together in matplotlib?

hard

A. Plot box plots side by side for each group only.

B. Plot violin plots stacked vertically without box plots.

C. Overlay violin plots with box plots for each group side by side.

D. Plot only histograms for each group.

Solution

1. Step 1: Understand the goal of comparing distributions

   Using both box and violin plots together helps see summary stats and full distribution shape.

2. Step 2: Identify best plotting method

   Overlaying violin plots with box plots side by side for each group shows both views clearly for comparison.

3. Final Answer:

   Overlay violin plots with box plots for each group side by side. -> Option C

4. Quick Check:

   Combine box + violin side by side for best comparison [OK]

Hint: Overlay box and violin plots side by side for clear comparison [OK]

Common Mistakes:

• Using only box or only violin plots alone
• Stacking plots vertically without overlay
• Using histograms which show less detail