Bar charts in Data Analysis Python - Mini Project: Build & Apply

Bar charts

📖 Scenario: You work at a small bakery. You want to show how many cakes of each type you sold last week. A bar chart is a great way to show this information clearly.

🎯 Goal: Create a bar chart that shows the number of cakes sold for each cake type.

📋 What You'll Learn

Create a dictionary with cake types and their sales numbers

Create a list of cake types from the dictionary keys

Create a list of sales numbers from the dictionary values

Use matplotlib to create a bar chart with cake types on the x-axis and sales numbers on the y-axis

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

Display the bar chart

💡 Why This Matters

🌍 Real World

Bar charts are used in business to show sales, in schools to show test scores, and in many places to compare amounts clearly.

💼 Career

Data analysts and scientists use bar charts to communicate data insights visually to teams and decision makers.

1

Create the sales data dictionary

Create a dictionary called cake_sales with these exact entries: 'Chocolate': 30, 'Vanilla': 45, 'Strawberry': 25, 'Lemon': 20.

Data Analysis Python

# Create the cake_sales dictionary with the exact cake types and sales numbers
# Your code here

Hint

Use curly braces {} to create a dictionary. Separate keys and values with a colon : and items with commas.

2

Prepare lists for the bar chart

Create a list called cake_types containing the keys of cake_sales. Create another list called sales_numbers containing the values of cake_sales.

Data Analysis Python

cake_sales = {'Chocolate': 30, 'Vanilla': 45, 'Strawberry': 25, 'Lemon': 20}
# Create cake_types list from cake_sales keys
# Create sales_numbers list from cake_sales values
# Your code here

Hint

Use list() with cake_sales.keys() and cake_sales.values() to get lists.

3

Create the bar chart

Import matplotlib.pyplot as plt. Use plt.bar() with cake_types and sales_numbers to create the bar chart. Add x-axis label 'Cake Types', y-axis label 'Number Sold', and title 'Cake Sales Last Week' using plt.xlabel(), plt.ylabel(), and plt.title().

Data Analysis Python

cake_sales = {'Chocolate': 30, 'Vanilla': 45, 'Strawberry': 25, 'Lemon': 20}
cake_types = list(cake_sales.keys())
sales_numbers = list(cake_sales.values())
# Import matplotlib.pyplot as plt
# Create bar chart with cake_types and sales_numbers
# Add x-axis label 'Cake Types'
# Add y-axis label 'Number Sold'
# Add title 'Cake Sales Last Week'
# 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.

Data Analysis Python

import matplotlib.pyplot as plt

cake_sales = {'Chocolate': 30, 'Vanilla': 45, 'Strawberry': 25, 'Lemon': 20}
cake_types = list(cake_sales.keys())
sales_numbers = list(cake_sales.values())

plt.bar(cake_types, sales_numbers)
plt.xlabel('Cake Types')
plt.ylabel('Number Sold')
plt.title('Cake Sales Last Week')
# Display the bar chart
# Your code here

Hint

Call plt.show() to open a window with the bar chart.

Practice

1. What does a bar chart mainly help you do with data?

easy

A. See differences between groups using bars

B. Calculate exact averages of numbers

C. Show data as a continuous line

D. Display data in a table format

Solution

1. Step 1: Understand the purpose of bar charts

   Bar charts use bars to visually compare values across categories.

2. Step 2: Compare options to bar chart use

   Only See differences between groups using bars describes using bars to see differences between groups.

3. Final Answer:

   See differences between groups using bars -> Option A

4. Quick Check:

   Bar charts = Visual group comparison [OK]

Hint: Bar charts show group sizes as bars [OK]

Common Mistakes:

• Confusing bar charts with line charts
• Thinking bar charts show exact calculations
• Mixing bar charts with tables

2. Which of these is the correct way to create a vertical bar chart using matplotlib in Python?

easy

A. plt.plot(x, height)

B. plt.barh(x, height)

C. plt.bar(x, height)

D. plt.scatter(x, height)

Solution

1. Step 1: Recall matplotlib bar chart functions

   plt.bar() creates vertical bars; plt.barh() creates horizontal bars.

2. Step 2: Match function to vertical bars

   Only plt.bar(x, height) creates vertical bar charts.

3. Final Answer:

   plt.bar(x, height) -> Option C

4. Quick Check:

   Vertical bars use plt.bar() [OK]

Hint: Use plt.bar() for vertical bars [OK]

Common Mistakes:

• Using plt.barh() for vertical bars
• Confusing plt.plot() with bar charts
• Using plt.scatter() for bar charts

3. What will the following code display?

import matplotlib.pyplot as plt
categories = ['A', 'B', 'C']
values = [5, 3, 7]
plt.bar(categories, values)
plt.show()

medium

A. A vertical bar chart with bars for A=5, B=3, C=7

B. A horizontal bar chart with bars for A=5, B=3, C=7

C. A line chart connecting points A, B, C

D. An error because plt.bar() needs numeric x values

Solution

1. Step 1: Understand plt.bar() with categorical x

   plt.bar() accepts category labels as x and numeric heights as values.

2. Step 2: Analyze the code output

   It creates vertical bars labeled A, B, C with heights 5, 3, 7 respectively.

3. Final Answer:

   A vertical bar chart with bars for A=5, B=3, C=7 -> Option A

4. Quick Check:

   plt.bar(categories, values) = vertical bars [OK]

Hint: plt.bar() accepts category labels for x-axis [OK]

Common Mistakes:

• Thinking plt.bar() only accepts numbers for x
• Confusing vertical and horizontal bars
• Expecting a line chart from plt.bar()

4. The code below is meant to create a horizontal bar chart but it shows a vertical one instead. What is the error?

import matplotlib.pyplot as plt
labels = ['X', 'Y', 'Z']
values = [10, 20, 15]
plt.bar(labels, values)
plt.show()

medium

A. The labels list should be numeric, not strings

B. plt.show() must be called before plt.bar()

C. Values must be strings, not numbers

D. plt.bar() creates vertical bars; use plt.barh() for horizontal bars

Solution

1. Step 1: Identify the function used for bar charts

   plt.bar() creates vertical bars; plt.barh() creates horizontal bars.

2. Step 2: Find why chart is vertical

   Using plt.bar() causes vertical bars, so to get horizontal bars, plt.barh() is needed.

3. Final Answer:

   plt.bar() creates vertical bars; use plt.barh() for horizontal bars -> Option D

4. Quick Check:

   Horizontal bars need plt.barh() [OK]

Hint: Use plt.barh() for horizontal bars [OK]

Common Mistakes:

• Thinking labels must be numbers
• Swapping order of plt.show() and plt.bar()
• Using string values instead of numbers

5. You have sales data for 3 products: {'Product A': 30, 'Product B': 45, 'Product C': 25}. You want to create a bar chart showing these sales with product names on the x-axis and sales on the y-axis. Which code snippet correctly creates this chart with labels and a title?

hard

A. import matplotlib.pyplot as plt
sales = {'Product A': 30, 'Product B': 45, 'Product C': 25}
plt.barh(sales.values(), sales.keys())
plt.xlabel('Products')
plt.ylabel('Sales')
plt.title('Sales by Product')
plt.show()

B. import matplotlib.pyplot as plt
sales = {'Product A': 30, 'Product B': 45, 'Product C': 25}
plt.bar(sales.keys(), sales.values())
plt.xlabel('Products')
plt.ylabel('Sales')
plt.title('Sales by Product')
plt.show()

C. import matplotlib.pyplot as plt
sales = {'Product A': 30, 'Product B': 45, 'Product C': 25}
plt.plot(sales.keys(), sales.values())
plt.xlabel('Products')
plt.ylabel('Sales')
plt.title('Sales by Product')
plt.show()

D. import matplotlib.pyplot as plt
sales = {'Product A': 30, 'Product B': 45, 'Product C': 25}
plt.bar(sales.values(), sales.keys())
plt.xlabel('Products')
plt.ylabel('Sales')
plt.title('Sales by Product')
plt.show()

Solution

1. Step 1: Understand data and desired chart

   We want vertical bars with product names on x-axis and sales on y-axis.

2. Step 2: Check code snippets for correct bar chart

   import matplotlib.pyplot as plt sales = {'Product A': 30, 'Product B': 45, 'Product C': 25} plt.bar(sales.keys(), sales.values()) plt.xlabel('Products') plt.ylabel('Sales') plt.title('Sales by Product') plt.show() uses plt.bar() with keys as x and values as heights, plus labels and title correctly set.

3. Step 3: Identify errors in other options

   import matplotlib.pyplot as plt sales = {'Product A': 30, 'Product B': 45, 'Product C': 25} plt.barh(sales.values(), sales.keys()) plt.xlabel('Products') plt.ylabel('Sales') plt.title('Sales by Product') plt.show() uses plt.barh() but swaps x and y labels incorrectly. import matplotlib.pyplot as plt sales = {'Product A': 30, 'Product B': 45, 'Product C': 25} plt.plot(sales.keys(), sales.values()) plt.xlabel('Products') plt.ylabel('Sales') plt.title('Sales by Product') plt.show() uses plt.plot() which is a line chart. import matplotlib.pyplot as plt sales = {'Product A': 30, 'Product B': 45, 'Product C': 25} plt.bar(sales.values(), sales.keys()) plt.xlabel('Products') plt.ylabel('Sales') plt.title('Sales by Product') plt.show() swaps keys and values in plt.bar(), causing wrong axes.

4. Final Answer:

   Code that correctly creates the labeled vertical bar chart -> Option B

5. Quick Check:

   Keys on x, values on y with plt.bar() [OK]

Hint: Use plt.bar(keys, values) with labels and title [OK]

Common Mistakes:

• Swapping keys and values in plt.bar()
• Using plt.plot() instead of plt.bar()
• Mixing up plt.bar() and plt.barh() axes