Distribution plots (histplot, kdeplot) in Data Analysis Python - Mini Project: Build & Apply

Distribution plots (histplot, kdeplot)

📖 Scenario: You work as a data analyst for a small bakery. You have collected data on the number of cookies sold each day for a week. You want to understand how the sales are distributed to help plan baking quantities.

🎯 Goal: Build a simple Python program that creates a list of daily cookie sales, sets a threshold for high sales, uses distribution plots (histogram and KDE) to visualize the sales data, and prints the sales data.

📋 What You'll Learn

Create a list of daily cookie sales with exact values

Create a threshold variable for high sales

Use seaborn to plot a histogram and KDE plot of the sales data

Print the sales data list

💡 Why This Matters

🌍 Real World

Distribution plots help businesses understand how their data points, like daily sales, are spread out. This helps in planning and decision making.

💼 Career

Data analysts and scientists use distribution plots to explore data patterns and communicate insights clearly to teams and managers.

1

Create the daily sales data list

Create a list called daily_sales with these exact values: 12, 15, 14, 10, 18, 20, 16 representing cookies sold each day.

Data Analysis Python

# Create the daily_sales list with the exact values
# Your code here

Hint

Use square brackets to create a list and separate numbers with commas.

2

Set a threshold for high sales

Create a variable called high_sales_threshold and set it to 15 to mark days with high cookie sales.

Data Analysis Python

daily_sales = [12, 15, 14, 10, 18, 20, 16]
# Create high_sales_threshold variable and set it to 15
# Your code here

Hint

Just assign the number 15 to the variable named high_sales_threshold.

3

Plot the distribution of daily sales

Import seaborn as sns and matplotlib.pyplot as plt. Use sns.histplot to plot a histogram of daily_sales with kde=True to add a smooth curve. Then call plt.show() to display the plot.

Data Analysis Python

daily_sales = [12, 15, 14, 10, 18, 20, 16]
high_sales_threshold = 15
# Import seaborn as sns and matplotlib.pyplot as plt
# Plot histogram with KDE of daily_sales
# Show the plot
# Your code here

Hint

Use sns.histplot with kde=True to add the smooth curve on the histogram.

4

Print the daily sales data

Write a print statement to display the daily_sales list.

Data Analysis Python

import seaborn as sns
import matplotlib.pyplot as plt

daily_sales = [12, 15, 14, 10, 18, 20, 16]
high_sales_threshold = 15

sns.histplot(daily_sales, kde=True)
plt.show()
# Print the daily_sales list
# Your code here

Hint

Use print(daily_sales) to show the list.