Vertical bar chart with plt.bar in Matplotlib - Mini Project: Build & Apply

Vertical Bar Chart with plt.bar

📖 Scenario: You work in a small bakery. You want to show how many cakes you sold each day last week. A simple bar chart will help you see which days were busiest.

🎯 Goal: Create a vertical bar chart using plt.bar to display the number of cakes sold each day of the week.

📋 What You'll Learn

Create a list called days with the days: 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'.

Create a list called cakes_sold with the exact numbers: 20, 35, 30, 35, 27, 40, 45.

Use plt.bar to make a vertical bar chart with days on the x-axis and cakes_sold on the y-axis.

Add labels for the x-axis as 'Days' and y-axis as 'Number of Cakes Sold'.

Add a title 'Cakes Sold Last Week'.

Display the chart with plt.show().

💡 Why This Matters

🌍 Real World

Bar charts are a simple way to show comparisons, like sales over days or months, helping businesses see trends quickly.

💼 Career

Data analysts and scientists often use bar charts to present data clearly to teams and decision makers.

1

Create the data lists

Create a list called days with these exact values: 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'. Then create a list called cakes_sold with these exact numbers: 20, 35, 30, 35, 27, 40, 45.

Matplotlib

# Create the lists days and cakes_sold with the exact values
# Your code here

Hint

Use square brackets to create lists. Separate items with commas.

2

Import matplotlib and set up the plot

Import matplotlib.pyplot as plt. This will let you create the bar chart.

Matplotlib

days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
cakes_sold = [20, 35, 30, 35, 27, 40, 45]
# Import matplotlib.pyplot as plt
# Your code here

Hint

Use import matplotlib.pyplot as plt to import the plotting library.

3

Create the vertical bar chart

Use plt.bar with days as the x values and cakes_sold as the heights of the bars. Then add x-axis label 'Days', y-axis label 'Number of Cakes Sold', and the title 'Cakes Sold Last Week'.

Matplotlib

import matplotlib.pyplot as plt
days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
cakes_sold = [20, 35, 30, 35, 27, 40, 45]
# Create the bar chart and add labels and title
# Your code here

Hint

Use plt.bar(x, y) to create bars. Use plt.xlabel(), plt.ylabel(), and plt.title() to add labels and title.

4

Display the bar chart

Use plt.show() to display the bar chart you created.

Matplotlib

import matplotlib.pyplot as plt
days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
cakes_sold = [20, 35, 30, 35, 27, 40, 45]
plt.bar(days, cakes_sold)
plt.xlabel('Days')
plt.ylabel('Number of Cakes Sold')
plt.title('Cakes Sold Last Week')
# Display the plot
# Your code here

Hint

Use plt.show() to see the chart window pop up.

Practice

1. What does the plt.bar() function do in matplotlib?

easy

A. It creates a scatter plot with dots.

B. It creates a line chart connecting data points.

C. It creates a pie chart showing proportions.

D. It creates a vertical bar chart using categories and their values.

Solution

1. Step 1: Understand the purpose of plt.bar()

   The plt.bar() function is designed to draw vertical bars representing data values for different categories.

2. Step 2: Compare with other chart types

   Other functions like plt.plot() create line charts, plt.scatter() creates scatter plots, and plt.pie() creates pie charts, so they are different from plt.bar().

3. Final Answer:

   It creates a vertical bar chart using categories and their values. -> Option D

4. Quick Check:

   plt.bar() = vertical bar chart [OK]

Hint: Remember: bar = vertical bars for categories [OK]

Common Mistakes:

• Confusing bar chart with line or pie charts
• Thinking plt.bar() draws horizontal bars
• Mixing up plt.bar() with scatter plot functions

2. Which of the following is the correct syntax to create a vertical bar chart with categories ['A', 'B', 'C'] and values [5, 7, 3]?

easy

A. plt.bar(['A', 'B', 'C'], [5, 7, 3])

B. plt.bar([5, 7, 3], ['A', 'B', 'C'])

C. plt.bar('A', 5, 'B', 7, 'C', 3)

D. plt.bar([5, 7, 3])

Solution

1. Step 1: Identify correct parameter order for plt.bar()

   The first argument should be the categories (x-axis labels), and the second argument should be the heights (values).

2. Step 2: Check each option

   plt.bar(['A', 'B', 'C'], [5, 7, 3]) uses categories first and values second, which is correct. plt.bar([5, 7, 3], ['A', 'B', 'C']) reverses them, which is wrong. plt.bar('A', 5, 'B', 7, 'C', 3) uses multiple arguments incorrectly. plt.bar([5, 7, 3]) misses categories.

3. Final Answer:

   plt.bar(['A', 'B', 'C'], [5, 7, 3]) -> Option A

4. Quick Check:

   Categories first, values second in plt.bar() [OK]

Hint: Categories go first, values second in plt.bar() [OK]

Common Mistakes:

• Swapping categories and values order
• Passing multiple separate arguments instead of lists
• Omitting categories or values

3. What will be the height of the bar labeled 'B' in this code?

import matplotlib.pyplot as plt
categories = ['A', 'B', 'C']
values = [4, 9, 2]
plt.bar(categories, values)
plt.show()

medium

A. 9

B. Cannot determine

C. 2

D. 4

Solution

1. Step 1: Match categories with values

   The list categories is ['A', 'B', 'C'] and values is [4, 9, 2]. The bar for 'B' corresponds to the second value, 9.

2. Step 2: Confirm bar height for 'B'

   Since 'B' is the second category, its bar height is the second value, which is 9.

3. Final Answer:

   9 -> Option A

4. Quick Check:

   Bar 'B' height = 9 [OK]

Hint: Match category index to value index [OK]

Common Mistakes:

• Mixing up category and value positions
• Assuming bar height is category label
• Ignoring list order

4. Identify the error in this code that tries to plot a vertical bar chart:

import matplotlib.pyplot as plt
categories = ['X', 'Y', 'Z']
values = [3, 6]
plt.bar(categories, values)
plt.show()

medium

A. The categories list has fewer items than values list.

B. plt.bar() requires numeric categories, not strings.

C. The values list has fewer items than categories list.

D. plt.show() is missing.

Solution

1. Step 1: Compare lengths of categories and values

   The categories list has 3 items ['X', 'Y', 'Z'], but values list has only 2 items [3, 6]. This mismatch causes an error.

2. Step 2: Understand plt.bar() requirements

   Each category must have a corresponding value. Since values are fewer, matplotlib will raise a ValueError.

3. Final Answer:

   The values list has fewer items than categories list. -> Option C

4. Quick Check:

   Lengths must match: values shorter than categories [OK]

Hint: Check categories and values list lengths match [OK]

Common Mistakes:

• Ignoring length mismatch between categories and values
• Thinking categories must be numeric
• Forgetting plt.show()

5. You want to create a vertical bar chart showing sales for products ['Pen', 'Notebook', 'Eraser'] with sales [10, 15, 10]. You also want to highlight the product with the highest sales in red and others in blue. Which code correctly achieves this?

hard

A. plt.bar(['Pen', 'Notebook', 'Eraser'], [10, 15, 10], color='red')
plt.show()

B. colors = ['blue', 'red', 'blue']
plt.bar(['Pen', 'Notebook', 'Eraser'], [10, 15, 10], color=colors)
plt.show()

C. colors = ['red', 'blue', 'blue']
plt.bar(['Pen', 'Notebook', 'Eraser'], [10, 15, 10], color=colors)
plt.show()

D. plt.bar(['Pen', 'Notebook', 'Eraser'], [10, 15, 10])
plt.show()

Solution

1. Step 1: Identify highest sales product

   The sales are [10, 15, 10], so 'Notebook' has the highest sales (15).

2. Step 2: Assign colors to highlight highest sales

   We want 'Notebook' bar in red and others in blue. colors = ['blue', 'red', 'blue'] plt.bar(['Pen', 'Notebook', 'Eraser'], [10, 15, 10], color=colors) plt.show() sets colors as ['blue', 'red', 'blue'], matching the order of products.

3. Step 3: Check other options

   plt.bar(['Pen', 'Notebook', 'Eraser'], [10, 15, 10], color='red') plt.show() colors all bars red, not highlighting only highest. colors = ['red', 'blue', 'blue'] plt.bar(['Pen', 'Notebook', 'Eraser'], [10, 15, 10], color=colors) plt.show() colors 'Pen' red incorrectly. plt.bar(['Pen', 'Notebook', 'Eraser'], [10, 15, 10]) plt.show() uses default colors without highlight.

4. Final Answer:

   colors = ['blue', 'red', 'blue'] plt.bar(['Pen', 'Notebook', 'Eraser'], [10, 15, 10], color=colors) plt.show() -> Option B

5. Quick Check:

   Color list matches bars, red on highest sales [OK]

Hint: Use color list matching bars, red for highest [OK]

Common Mistakes:

• Coloring all bars same color
• Assigning wrong color order
• Not using color parameter