Categorical plots (boxplot, violinplot) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of boxplot with grouped categories

What will be the output of the following code snippet that creates a boxplot grouped by category?

Data Analysis Python

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

data = pd.DataFrame({
    'Category': ['A', 'A', 'B', 'B', 'C', 'C'],
    'Value': [10, 15, 10, 20, 15, 25]
})
sns.boxplot(x='Category', y='Value', data=data)
plt.show()

A A boxplot with three boxes labeled A, B, C showing value distributions: A (10,15), B (10,20), C (15,25)

B A line plot connecting points (A,10), (A,15), (B,10), (B,20), (C,15), (C,25)

C A scatter plot with points at (A,10), (A,15), (B,10), (B,20), (C,15), (C,25)

D A bar chart showing sum of values for each category

💡 Hint

Boxplots show distribution of values grouped by categories.

The boxplot groups data by 'Category' and shows the distribution of 'Value' for each group as boxes.

❓ data_output

intermediate

Number of boxes in violinplot

Given the following data and code, how many violin shapes will appear in the plot?

Data Analysis Python

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

data = pd.DataFrame({
    'Group': ['X', 'X', 'Y', 'Y', 'Y', 'Z'],
    'Score': [5, 7, 6, 8, 7, 9]
})
sns.violinplot(x='Group', y='Score', data=data)
plt.show()

A 6

B 2

C 1

D 3

💡 Hint

Each unique category in 'Group' creates one violin shape.

There are three unique groups: X, Y, and Z, so the violinplot will have three violins.

❓ visualization

advanced

Identify the plot type from description

Which plot type best shows the distribution shape and individual data points for categories?

A Violinplot

B Boxplot

C Bar plot

D Scatter plot

💡 Hint

One plot type shows a smooth density shape and can include individual points.

Violinplots show the distribution shape as a smooth density and can overlay individual data points, unlike boxplots which show summary statistics only.

🔧 Debug

advanced

Error in boxplot code

What error will this code produce and why? import seaborn as sns import matplotlib.pyplot as plt sns.boxplot(x='Category', y='Value') plt.show()

Data Analysis Python

import seaborn as sns
import matplotlib.pyplot as plt

sns.boxplot(x='Category', y='Value')
plt.show()

A NameError: name 'Category' is not defined

B ValueError: x and y must be numeric

C TypeError: boxplot() missing 1 required positional argument: 'data'

D No error, plot shows empty figure

💡 Hint

Seaborn needs data argument to know where to find columns.

The code misses the 'data' argument, so seaborn cannot find 'Category' and 'Value' columns, causing a TypeError.

🚀 Application

expert

Choosing plot for comparing distributions with many categories

You have a dataset with 20 categories and want to compare the distribution of a numeric variable across them. Which plot is best to use and why?

A Bar plot, because it shows average values clearly for many categories

B Boxplot, because it summarizes distribution with quartiles and is compact for many categories

C Violinplot, because it shows detailed distribution shape for each category clearly even with many categories

D Scatter plot, because it shows all individual points for all categories

💡 Hint

Consider clarity and compactness when many categories exist.

Boxplots summarize distributions with quartiles and outliers, making them easier to read when many categories exist. Violinplots can become cluttered with many categories.

Practice

1. What does a boxplot primarily show about a dataset?

easy

A. The correlation between two variables

B. The median, quartiles, and outliers of the data

C. Only the mean and standard deviation

D. The exact values of all data points

Solution

1. Step 1: Understand boxplot components

   A boxplot displays the median line, the box showing the interquartile range (25th to 75th percentile), and whiskers for range excluding outliers.

2. Step 2: Identify what it does not show

   It does not show every data point's exact value or correlation between variables.

3. Final Answer:

   The median, quartiles, and outliers of the data -> Option B

4. Quick Check:

   Boxplot = median + quartiles + outliers [OK]

Hint: Boxplots summarize data spread with median and quartiles [OK]

Common Mistakes:

• Thinking boxplots show all individual data points
• Confusing boxplots with scatterplots
• Assuming boxplots show mean instead of median

2. Which of the following is the correct way to create a violin plot using seaborn for a DataFrame df with columns 'category' and 'value'?

easy

A. sns.violinplot(y='category', x='value', data=df)

B. sns.violinplot(data=df, x='value', y='category')

C. sns.violinplot(df['category'], df['value'])

D. sns.violinplot(x='category', y='value', data=df)

Solution

1. Step 1: Check seaborn violinplot syntax

   The correct syntax is sns.violinplot(x='category', y='value', data=df) where x is the categorical variable and y is the numeric variable.

2. Step 2: Identify incorrect options

   sns.violinplot(data=df, x='value', y='category') swaps x and y incorrectly. sns.violinplot(df['category'], df['value']) passes series without specifying data keyword. sns.violinplot(y='category', x='value', data=df) swaps x and y axes.

3. Final Answer:

   sns.violinplot(x='category', y='value', data=df) -> Option D

4. Quick Check:

   Violinplot syntax = sns.violinplot(x=cat, y=num, data=df) [OK]

Hint: Use x=category, y=value, data=DataFrame for seaborn violinplot [OK]

Common Mistakes:

• Swapping x and y parameters
• Passing columns without data= argument
• Using positional arguments instead of keywords

3. Given the following code, what will be the output type?

import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt

# Sample data
df = pd.DataFrame({
    'group': ['A', 'A', 'B', 'B', 'C', 'C'],
    'score': [10, 15, 10, 20, 15, 25]
})

plot = sns.boxplot(x='group', y='score', data=df)
plt.show()

medium

A. A boxplot showing score distribution per group

B. A scatter plot of scores vs groups

C. A line plot connecting scores in each group

D. An error because of missing hue parameter

Solution

1. Step 1: Understand sns.boxplot usage

   The code uses seaborn's boxplot with x as categorical 'group' and y as numeric 'score', which creates boxplots per group.

2. Step 2: Confirm plot type and parameters

   No hue is required here; the plot will show boxplots for each group category with score distribution.

3. Final Answer:

   A boxplot showing score distribution per group -> Option A

4. Quick Check:

   sns.boxplot(x=cat, y=num) = boxplot per category [OK]

Hint: sns.boxplot(x=cat, y=num) plots distribution per category [OK]

Common Mistakes:

• Expecting scatter or line plot instead of boxplot
• Thinking hue is mandatory
• Confusing boxplot with barplot

4. Identify the error in this code snippet for creating a violin plot:

import seaborn as sns
sns.violinplot(data=df, x='value', y='category')

medium

A. DataFrame df is not defined

B. Missing import for matplotlib.pyplot

C. The x and y parameters are swapped; x should be categorical

D. Violinplot does not accept x and y parameters

Solution

1. Step 1: Check parameter roles in violinplot

   In seaborn violinplot, x should be the categorical variable and y the numeric variable. Here, 'value' is numeric and 'category' is categorical, so x and y are swapped.

2. Step 2: Verify other possible errors

   Importing matplotlib is not mandatory for just creating the plot object. The DataFrame df is assumed defined. Violinplot accepts x and y parameters.

3. Final Answer:

   The x and y parameters are swapped; x should be categorical -> Option C

4. Quick Check:

   Violinplot x=category, y=numeric [OK]

Hint: Categorical variable must be x in violinplot [OK]

Common Mistakes:

• Swapping x and y axes
• Assuming matplotlib import is always needed
• Not defining DataFrame before plotting

5. You have a dataset with three groups: 'X', 'Y', and 'Z'. Group 'Z' has many outliers. You want to visualize the distribution shape and highlight the outliers clearly. Which plot should you choose and why?

hard

A. Use a violinplot to show distribution shape and a boxplot overlay to highlight outliers

B. Use only a boxplot because violinplots hide outliers

C. Use a scatter plot to show all points without summary

D. Use a line plot to connect group means

Solution

1. Step 1: Understand plot strengths

   Violinplots show the shape and density of data distribution well, but outliers can be less obvious. Boxplots clearly mark outliers.

2. Step 2: Combine plots for best insight

   Overlaying a boxplot on a violinplot combines both benefits: distribution shape and clear outlier marking.

3. Final Answer:

   Use a violinplot to show distribution shape and a boxplot overlay to highlight outliers -> Option A

4. Quick Check:

   Violin + boxplot = shape + outliers [OK]

Hint: Combine violin and boxplot for shape and outliers [OK]

Common Mistakes:

• Thinking violinplots alone highlight outliers well
• Using scatter plots without summary
• Choosing line plots for categorical distributions