Box plot vs violin plot comparison in Matplotlib - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the matplotlib plotting library.

Matplotlib

import [1] as plt

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

Anumpy

Bseaborn

Cpandas

Dmatplotlib.pyplot

Attempts:

3 left

💡 Hint

Common Mistakes

Importing the wrong library like seaborn or numpy.

Forgetting to use the alias plt.

We import matplotlib.pyplot as plt to create plots.

2fill in blank

medium

Complete the code to create a box plot of the data list.

Matplotlib

plt.[1](data)

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

Ascatter

Bviolinplot

Cboxplot

Dhist

Attempts:

3 left

💡 Hint

Common Mistakes

Using violinplot instead of boxplot.

Using hist or scatter which are different plot types.

The boxplot function draws a box plot to show data distribution.

3fill in blank

hard

Fix the error in the code to create a violin plot of the data list.

Matplotlib

plt.[1](data)

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

Aviolinplot

Bboxplot

Cplot

Dbar

Attempts:

3 left

💡 Hint

Common Mistakes

Using boxplot instead of violinplot.

Using plot or bar which do not create violin plots.

The violinplot function draws a violin plot which shows data distribution shape.

4fill in blank

hard

Fill both blanks to create side-by-side box and violin plots for the same data.

Matplotlib

fig, ax = plt.subplots()
ax.[1](data, positions=[1])
ax.[2](data, positions=[2])
plt.show()

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

Aboxplot

Bscatter

Cviolinplot

Dhist

Attempts:

3 left

💡 Hint

Common Mistakes

Using scatter or hist instead of violinplot for the second plot.

Not specifying positions to separate the plots.

Use boxplot and violinplot methods on the same axes to compare plots side-by-side.

5fill in blank

hard

Fill all three blanks to create labeled box and violin plots with titles and axis labels.

Matplotlib

fig, ax = plt.subplots()
ax.[1](data, positions=[1])
ax.[2](data, positions=[2])
ax.set_xticks([1, 2])
ax.set_xticklabels([[3]])
ax.set_title('Box vs Violin Plot')
plt.show()

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

Aboxplot

Bviolinplot

C['Box Plot', 'Violin Plot']

D['Violin', 'Box']

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping boxplot and violinplot order.

Using wrong labels or mismatched labels for ticks.

First create boxplot, then violinplot. Then label x-axis ticks with descriptive names.

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