Why statistical plots reveal data patterns in Matplotlib - See It in Action

Why statistical plots reveal data patterns

📖 Scenario: You work as a data analyst for a small online store. You have sales data for different products over a week. You want to understand how sales vary and spot any patterns or trends.

🎯 Goal: Build a simple Python program that creates a dictionary of sales data, sets a threshold for high sales, filters the data, and then plots the sales using a bar chart to visually reveal patterns.

📋 What You'll Learn

Create a dictionary called sales with product names as keys and sales numbers as values

Create a variable called high_sales_threshold set to 50

Use a dictionary comprehension to create a new dictionary high_sales with only products having sales greater than high_sales_threshold

Use matplotlib.pyplot to plot a bar chart of high_sales

Print the high_sales dictionary

💡 Why This Matters

🌍 Real World

Data analysts often use plots to quickly understand sales trends, customer behavior, or product popularity.

💼 Career

Knowing how to filter data and visualize it is a key skill for data science, business analysis, and reporting roles.

1

Create the sales data dictionary

Create a dictionary called sales with these exact entries: 'Apples': 40, 'Bananas': 55, 'Cherries': 30, 'Dates': 70, 'Elderberries': 20

Matplotlib

# Create the sales dictionary with exact product names and sales values
# Your code here

Need a hint?

Use curly braces {} to create a dictionary with keys and values separated by colons.

2

Set the high sales threshold

Create a variable called high_sales_threshold and set it to 50

Matplotlib

sales = {'Apples': 40, 'Bananas': 55, 'Cherries': 30, 'Dates': 70, 'Elderberries': 20}
# Set the high_sales_threshold variable to 50
# Your code here

Need a hint?

Assign the number 50 to the variable high_sales_threshold.

3

Filter high sales products

Use a dictionary comprehension to create a new dictionary called high_sales that includes only products from sales with sales greater than high_sales_threshold

Matplotlib

sales = {'Apples': 40, 'Bananas': 55, 'Cherries': 30, 'Dates': 70, 'Elderberries': 20}
high_sales_threshold = 50
# Create high_sales dictionary with products having sales > high_sales_threshold
# Your code here

Need a hint?

Use {product: count for product, count in sales.items() if count > high_sales_threshold} to filter the dictionary.

4

Plot the high sales data and print the dictionary

Import matplotlib.pyplot as plt. Use plt.bar() to create a bar chart of high_sales keys and values. Then use plt.show() to display the plot. Finally, print the high_sales dictionary.

Matplotlib

sales = {'Apples': 40, 'Bananas': 55, 'Cherries': 30, 'Dates': 70, 'Elderberries': 20}
high_sales_threshold = 50
high_sales = {product: count for product, count in sales.items() if count > high_sales_threshold}
# Import matplotlib.pyplot as plt
# Plot a bar chart of high_sales keys and values
# Show the plot
# Print the high_sales dictionary
# Your code here

Need a hint?

Use plt.bar() with high_sales.keys() and high_sales.values(). Then call plt.show(). Finally, print high_sales.