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.