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

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

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

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

Hint

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

Practice

1. What does the yerr parameter do when creating a bar chart in matplotlib?

easy

A. It changes the color of the bars.

B. It sets the width of the bars.

C. It adds vertical error bars to each bar showing uncertainty.

D. It adds labels to the bars.

Solution

1. Step 1: Understand the role of yerr

   The yerr parameter in matplotlib bar charts specifies the size of vertical error bars to show uncertainty or variation.

2. Step 2: Compare with other options

   Other options like color, width, or labels are controlled by different parameters, not yerr.

3. Final Answer:

   It adds vertical error bars to each bar showing uncertainty. -> Option C

4. Quick Check:

   yerr = vertical error bars [OK]

Hint: Remember: yerr means vertical error bars [OK]

Common Mistakes:

• Confusing yerr with bar width
• Thinking yerr changes bar color
• Assuming yerr adds labels

2. Which of the following is the correct syntax to add error bars with size 0.2 to a bar chart using matplotlib?

easy

A. plt.bar(x, height, err=0.2)

B. plt.bar(x, height, error=0.2)

C. plt.bar(x, height, yerror=0.2)

D. plt.bar(x, height, yerr=0.2)

Solution

1. Step 1: Recall the correct parameter name

   The correct parameter to add vertical error bars in matplotlib bar charts is yerr.

2. Step 2: Check the syntax

   The syntax plt.bar(x, height, yerr=0.2) correctly adds error bars of size 0.2.

3. Final Answer:

   plt.bar(x, height, yerr=0.2) -> Option D

4. Quick Check:

   Correct parameter is yerr [OK]

Hint: Use exact parameter name yerr for error bars [OK]

Common Mistakes:

• Using wrong parameter names like error or err
• Misspelling yerr as yerror
• Forgetting to assign a value to yerr

3. What will be the output of this code snippet?

import matplotlib.pyplot as plt
x = [1, 2, 3]
heights = [3, 5, 2]
errors = [0.5, 0.2, 0.3]
plt.bar(x, heights, yerr=errors, capsize=5)
plt.show()

medium

A. A syntax error because capsize is not a valid parameter.

B. A bar chart with bars at x=1,2,3 with heights 3,5,2 and vertical error bars with caps of length 5.

C. A bar chart with bars but no error bars visible.

D. A bar chart with horizontal error bars.

Solution

1. Step 1: Understand the code

   The code creates bars at positions 1, 2, 3 with heights 3, 5, 2 respectively and adds vertical error bars sized by the list errors.

2. Step 2: Role of capsize

   The capsize=5 parameter adds horizontal caps at the ends of error bars, making them visible and 5 points long.

3. Final Answer:

   A bar chart with bars at x=1,2,3 with heights 3,5,2 and vertical error bars with caps of length 5. -> Option B

4. Quick Check:

   yerr + capsize = vertical error bars with caps [OK]

Hint: capsize adds visible ends to error bars [OK]

Common Mistakes:

• Thinking capsize disables error bars
• Confusing vertical and horizontal error bars
• Assuming capsize causes syntax error

4. Identify the error in this code that tries to add error bars to a bar chart:

import matplotlib.pyplot as plt
x = [1, 2, 3]
heights = [4, 6, 5]
errors = [0.3, 0.4, 0.2]
plt.bar(x, heights, yerror=errors)
plt.show()

medium

A. The parameter name should be yerr, not yerror.

B. The errors list length does not match heights length.

C. plt.bar does not support error bars.

D. The x values must be strings, not integers.

Solution

1. Step 1: Check parameter names

   The parameter to add vertical error bars is yerr, not yerror.

2. Step 2: Validate other parts

   The lengths of errors and heights match, and plt.bar supports error bars with yerr.

3. Final Answer:

   The parameter name should be yerr, not yerror. -> Option A

4. Quick Check:

   Correct parameter is yerr, not yerror [OK]

Hint: Use exact parameter yerr, spelling matters [OK]

Common Mistakes:

• Using yerror instead of yerr
• Ignoring parameter name case sensitivity
• Assuming plt.bar can't have error bars

5. You want to create a bar chart with 4 bars and different error sizes for each bar. Which code correctly achieves this with visible error bar caps?

hard

A. plt.bar([1,2,3,4], [5,7,3,4], yerr=[0.2,0.3,0.1,0.4], capsize=3)

B. plt.bar([1,2,3,4], [5,7,3,4], yerr=0.3, capsize=0)

C. plt.bar([1,2,3,4], [5,7,3,4], yerr=[0.2,0.3], capsize=3)

D. plt.bar([1,2,3,4], [5,7,3,4], error=[0.2,0.3,0.1,0.4], capsize=3)

Solution

1. Step 1: Check error bar sizes match bars

   There are 4 bars, so the yerr list must have 4 values. plt.bar([1,2,3,4], [5,7,3,4], yerr=[0.2,0.3,0.1,0.4], capsize=3) has 4 error sizes matching bars.

2. Step 2: Verify capsize for visible caps

   plt.bar([1,2,3,4], [5,7,3,4], yerr=[0.2,0.3,0.1,0.4], capsize=3) uses capsize=3 which makes error bar caps visible. Other options fail: one uses a single yerr value with capsize=0 (no caps), another provides only two error sizes (mismatch), and one uses wrong parameter error.

3. Final Answer:

   plt.bar([1,2,3,4], [5,7,3,4], yerr=[0.2,0.3,0.1,0.4], capsize=3) -> Option A

4. Quick Check:

   yerr list length = bars count + capsize > 0 [OK]

Hint: Match yerr length to bars and set capsize > 0 [OK]

Common Mistakes:

• Using single yerr value for multiple bars
• Setting capsize to zero hides caps
• Using wrong parameter name error instead of yerr