Error bars on bar charts in Matplotlib - Mini Project: Build & Apply

Error bars on bar charts

📖 Scenario: You work in a small bakery that tracks daily sales of different types of bread. You want to show the average sales for each bread type along with how much the sales vary each day.

🎯 Goal: Create a bar chart showing average sales for each bread type with error bars representing the daily sales variation.

📋 What You'll Learn

Create a dictionary called daily_sales with bread types as keys and lists of daily sales numbers as values.

Create a variable called average_sales that stores the average sales for each bread type.

Create a variable called sales_std that stores the standard deviation of sales for each bread type.

Use matplotlib to plot a bar chart with error bars using average_sales and sales_std.

Display the bar chart with labels for bread types and a title.

💡 Why This Matters

🌍 Real World

Bakers and shop owners use sales charts with error bars to understand average sales and variability, helping with inventory and production planning.

💼 Career

Data analysts and business intelligence professionals often create charts with error bars to communicate data variability clearly to stakeholders.

1

Create the daily sales data

Create a dictionary called daily_sales with these exact entries: 'Sourdough': [20, 22, 19, 24, 21], 'Baguette': [15, 14, 16, 15, 17], 'Rye': [10, 9, 11, 10, 12].

Matplotlib

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

Need a hint?

Use a dictionary with bread names as keys and lists of numbers as values.

2

Calculate average sales and standard deviation

Create two dictionaries: average_sales and sales_std. Use a for loop with variables bread and sales to iterate over daily_sales.items(). Calculate the average sales using sum(sales) / len(sales) and the standard deviation using the formula for sample standard deviation.

Matplotlib

daily_sales = {
    'Sourdough': [20, 22, 19, 24, 21],
    'Baguette': [15, 14, 16, 15, 17],
    'Rye': [10, 9, 11, 10, 12]
}
# Calculate average_sales and sales_std dictionaries
# Your code here

Need a hint?

Use a loop to calculate average and standard deviation for each bread type.

3

Plot the bar chart with error bars

Import matplotlib.pyplot as plt. Create a list bread_types from the keys of average_sales. Create a list averages from the values of average_sales. Create a list errors from the values of sales_std. Use plt.bar with bread_types, averages, and yerr=errors to plot the bar chart with error bars.

Matplotlib

daily_sales = {
    'Sourdough': [20, 22, 19, 24, 21],
    'Baguette': [15, 14, 16, 15, 17],
    'Rye': [10, 9, 11, 10, 12]
}
average_sales = {}
sales_std = {}
for bread, sales in daily_sales.items():
    avg = sum(sales) / len(sales)
    variance = sum((x - avg) ** 2 for x in sales) / (len(sales) - 1)
    std_dev = variance ** 0.5
    average_sales[bread] = avg
    sales_std[bread] = std_dev

# Import matplotlib.pyplot as plt and plot bar chart with error bars
# Your code here

Need a hint?

Use plt.bar with yerr to add error bars.

4

Display the bar chart

Use plt.show() to display the bar chart with error bars.

Matplotlib

import matplotlib.pyplot as plt

daily_sales = {
    'Sourdough': [20, 22, 19, 24, 21],
    'Baguette': [15, 14, 16, 15, 17],
    'Rye': [10, 9, 11, 10, 12]
}
average_sales = {}
sales_std = {}
for bread, sales in daily_sales.items():
    avg = sum(sales) / len(sales)
    variance = sum((x - avg) ** 2 for x in sales) / (len(sales) - 1)
    std_dev = variance ** 0.5
    average_sales[bread] = avg
    sales_std[bread] = std_dev

bread_types = list(average_sales.keys())
averages = list(average_sales.values())
errors = list(sales_std.values())

plt.bar(bread_types, averages, yerr=errors, capsize=5)
plt.xlabel('Bread Type')
plt.ylabel('Average Daily Sales')
plt.title('Average Daily Bread Sales with Error Bars')

# Display the plot
# Your code here

Need a hint?

Use plt.show() to display the chart window.