Grouped bar charts in Matplotlib - Mini Project: Build & Apply

Grouped Bar Charts

📖 Scenario: You work in a small store that sells two types of fruits: apples and oranges. You want to compare the sales of these fruits over three months to see which fruit sold better each month.

🎯 Goal: Create a grouped bar chart to visually compare the monthly sales of apples and oranges side by side.

📋 What You'll Learn

Create a dictionary called sales with keys as months and values as another dictionary with keys 'apples' and 'oranges' and their sales numbers.

Create a list called months containing the months in order.

Create a variable called bar_width to set the width of each bar.

Use matplotlib.pyplot to plot grouped bars for apples and oranges for each month.

Add labels for the x-axis, y-axis, and a title for the chart.

Add a legend to distinguish apples and oranges.

💡 Why This Matters

🌍 Real World

Grouped bar charts help compare multiple categories across different groups, like sales of different products over months.

💼 Career

Data analysts and scientists use grouped bar charts to visualize and communicate comparisons clearly to stakeholders.

1

Create the sales data dictionary

Create a dictionary called sales with these exact entries: 'January': {'apples': 30, 'oranges': 25}, 'February': {'apples': 40, 'oranges': 35}, and 'March': {'apples': 35, 'oranges': 30}.

Matplotlib

# Create the sales dictionary with exact values
# Your code here

Need a hint?

Use a dictionary with months as keys and another dictionary for fruit sales as values.

2

Set up months list and bar width

Create a list called months with the values 'January', 'February', and 'March'. Then create a variable called bar_width and set it to 0.35.

Matplotlib

sales = {
    'January': {'apples': 30, 'oranges': 25},
    'February': {'apples': 40, 'oranges': 35},
    'March': {'apples': 35, 'oranges': 30}
}
# Create the months list and bar_width variable
# Your code here

Need a hint?

Use a list for months and a float for bar_width.

3

Plot the grouped bar chart

Import matplotlib.pyplot as plt. Create two lists called apples_sales and oranges_sales that contain the sales numbers for apples and oranges for each month in order. Create a list called positions using range(len(months)). Plot grouped bars for apples and oranges using plt.bar with positions and positions + bar_width for x positions. Add x-axis labels using plt.xticks at the middle of the grouped bars with the month names. Add labels for x-axis as 'Months', y-axis as 'Sales', and a title 'Monthly Fruit Sales'. Add a legend for apples and oranges.

Matplotlib

sales = {
    'January': {'apples': 30, 'oranges': 25},
    'February': {'apples': 40, 'oranges': 35},
    'March': {'apples': 35, 'oranges': 30}
}

months = ['January', 'February', 'March']
bar_width = 0.35

# Import matplotlib.pyplot as plt
# Create apples_sales and oranges_sales lists
# Create positions list
# Plot grouped bars for apples and oranges
# Add x-axis labels, y-axis labels, title, and legend
# Your code here

Need a hint?

Use list comprehensions to get sales lists. Use plt.bar twice with shifted positions for grouped bars.

4

Display the grouped bar chart

Add a line to display the plot using plt.show().

Matplotlib

import matplotlib.pyplot as plt

sales = {
    'January': {'apples': 30, 'oranges': 25},
    'February': {'apples': 40, 'oranges': 35},
    'March': {'apples': 35, 'oranges': 30}
}

months = ['January', 'February', 'March']
bar_width = 0.35

apples_sales = [sales[month]['apples'] for month in months]
oranges_sales = [sales[month]['oranges'] for month in months]

positions = list(range(len(months)))

plt.bar(positions, apples_sales, width=bar_width, label='Apples')
plt.bar([p + bar_width for p in positions], oranges_sales, width=bar_width, label='Oranges')

plt.xticks([p + bar_width / 2 for p in positions], months)
plt.xlabel('Months')
plt.ylabel('Sales')
plt.title('Monthly Fruit Sales')
plt.legend()

# Display the plot
# Your code here

Need a hint?

Use plt.show() to display the chart.