Categorical plots (boxplot, violinplot) in Data Analysis Python - Mini Project: Build & Apply

Exploring Categorical Plots: Boxplot and Violinplot

📖 Scenario: You work as a data analyst for a small bakery. You have collected data on the number of pastries sold each day, categorized by pastry type. You want to understand the sales distribution for each pastry type to help the bakery decide which pastries are popular and how consistent their sales are.

🎯 Goal: Build a Python program that creates a dataset of pastry sales, sets up a configuration for plot style, then uses categorical plots (boxplot and violinplot) to visualize the sales distribution by pastry type.

📋 What You'll Learn

Create a pandas DataFrame with pastry sales data

Set a plot style configuration variable

Use seaborn to create a boxplot and a violinplot

Display the plots

💡 Why This Matters

🌍 Real World

Categorical plots like boxplots and violinplots help businesses understand how different categories perform and how consistent their data is, which supports better decision-making.

💼 Career

Data analysts and scientists use these plots to summarize and communicate insights about grouped data in reports and presentations.

1

Create the pastry sales data

Create a pandas DataFrame called sales_data with two columns: 'Pastry' and 'Sales'. The data should have these exact rows: 'Croissant', 30, 'Croissant', 45, 'Croissant', 40, 'Donut', 50, 'Donut', 55, 'Donut', 60, 'Muffin', 20, 'Muffin', 25, 'Muffin', 22.

Data Analysis Python

import pandas as pd
# Create the sales_data DataFrame with the exact pastry sales data
# Your code here

Hint

Use pd.DataFrame with a dictionary containing the two columns and their values.

2

Set the plot style configuration

Create a variable called plot_style and set it to the string 'whitegrid' to configure the seaborn plot style.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Pastry': ['Croissant', 'Croissant', 'Croissant', 'Donut', 'Donut', 'Donut', 'Muffin', 'Muffin', 'Muffin'],
    'Sales': [30, 45, 40, 50, 55, 60, 20, 25, 22]
})
# Set the plot_style variable to 'whitegrid'
# Your code here

Hint

Just assign the string 'whitegrid' to the variable plot_style.

3

Create boxplot and violinplot

Import seaborn as sns and matplotlib.pyplot as plt. Use sns.set_style(plot_style) to apply the style. Then create a boxplot with sns.boxplot using sales_data, setting x='Pastry' and y='Sales'. Next, create a violinplot with sns.violinplot using the same parameters. Use plt.show() to display the plots.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Pastry': ['Croissant', 'Croissant', 'Croissant', 'Donut', 'Donut', 'Donut', 'Muffin', 'Muffin', 'Muffin'],
    'Sales': [30, 45, 40, 50, 55, 60, 20, 25, 22]
})
plot_style = 'whitegrid'
# Import seaborn and matplotlib.pyplot
# Set the seaborn style using plot_style
# Create a boxplot and a violinplot for sales_data
# Your code here

Hint

Use sns.set_style(plot_style) before plotting. Create the boxplot and violinplot with x='Pastry' and y='Sales'. Use plt.show() to display each plot.

4

Display the plots

Run the program to display the boxplot and violinplot showing the sales distribution for each pastry type.

Data Analysis Python

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

sales_data = pd.DataFrame({
    'Pastry': ['Croissant', 'Croissant', 'Croissant', 'Donut', 'Donut', 'Donut', 'Muffin', 'Muffin', 'Muffin'],
    'Sales': [30, 45, 40, 50, 55, 60, 20, 25, 22]
})

plot_style = 'whitegrid'

sns.set_style(plot_style)
sns.boxplot(x='Pastry', y='Sales', data=sales_data)
plt.title('Boxplot of Pastry Sales')
plt.show()

sns.set_style(plot_style)
sns.violinplot(x='Pastry', y='Sales', data=sales_data)
plt.title('Violinplot of Pastry Sales')
plt.show()
# Run the program to display the plots

Hint

Make sure to run the entire program to see both plots displayed one after the other.

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