Lollipop charts in Matplotlib - Mini Project: Build & Apply

Create a Lollipop Chart with Matplotlib

📖 Scenario: You work in a small bakery and want to show how many lollipops were sold each day last week. You want to create a simple chart to share with your team.

🎯 Goal: Build a lollipop chart using matplotlib to display the number of lollipops sold each day of the week.

📋 What You'll Learn

Create a dictionary with days of the week as keys and lollipop sales as values.

Create a list of days from the dictionary keys.

Create a list of sales from the dictionary values.

Use matplotlib to plot a lollipop chart with vertical lines and dots.

Label the x-axis with days and y-axis with sales.

Display the chart.

💡 Why This Matters

🌍 Real World

Lollipop charts are simple and clear ways to show comparisons between categories, like daily sales or survey results.

💼 Career

Data analysts and scientists use lollipop charts to present data trends clearly to teams and stakeholders.

1

DATA SETUP: Create the sales data dictionary

Create a dictionary called sales with these exact entries: 'Monday': 10, 'Tuesday': 15, 'Wednesday': 7, 'Thursday': 12, 'Friday': 20, 'Saturday': 25, 'Sunday': 18.

Matplotlib

# Create the sales dictionary with days and lollipop sales
# Your code here

Hint

Use curly braces {} to create a dictionary. Put days as keys and sales numbers as values.

2

CONFIGURATION: Extract days and sales lists

Create a list called days from the keys of sales. Create a list called values from the values of sales.

Matplotlib

sales = {'Monday': 10, 'Tuesday': 15, 'Wednesday': 7, 'Thursday': 12, 'Friday': 20, 'Saturday': 25, 'Sunday': 18}
# Create the list days from sales keys
# Create the list values from sales values
# Your code here

Hint

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

3

CORE LOGIC: Plot the lollipop chart

Import matplotlib.pyplot as plt. Use plt.stem() with days and values to create the lollipop chart. Set the marker format to 'o' and line format to '-' for dots and lines.

Matplotlib

sales = {'Monday': 10, 'Tuesday': 15, 'Wednesday': 7, 'Thursday': 12, 'Friday': 20, 'Saturday': 25, 'Sunday': 18}
days = list(sales.keys())
values = list(sales.values())
# Import matplotlib.pyplot as plt
# Use plt.stem() with days and values, set markerfmt='o' and linefmt='-'
# Your code here

Hint

Use import matplotlib.pyplot as plt to import. Then call plt.stem() with the correct arguments.

4

OUTPUT: Label and show the chart

Use plt.xlabel() to label the x-axis as 'Days'. Use plt.ylabel() to label the y-axis as 'Lollipops Sold'. Use plt.title() to add the title 'Lollipop Sales Last Week'. Finally, use plt.show() to display the chart.

Matplotlib

import matplotlib.pyplot as plt

sales = {'Monday': 10, 'Tuesday': 15, 'Wednesday': 7, 'Thursday': 12, 'Friday': 20, 'Saturday': 25, 'Sunday': 18}
days = list(sales.keys())
values = list(sales.values())

plt.stem(days, values, markerfmt='o', linefmt='-')
# Label x-axis as 'Days'
# Label y-axis as 'Lollipops Sold'
# Add title 'Lollipop Sales Last Week'
# Show the plot
# Your code here

Hint

Use plt.xlabel(), plt.ylabel(), and plt.title() to add labels and title. Then call plt.show() to display the chart.

Practice

1. What is the main purpose of a lollipop chart in data visualization?

easy

A. To compare values using lines and dots for clear differences

B. To show the distribution of data using bars

C. To display data points on a map

D. To create 3D surface plots

Solution

1. Step 1: Understand lollipop chart basics

   A lollipop chart uses lines and dots to compare values clearly, making differences easy to see.

2. Step 2: Compare with other chart types

   Bar charts show distribution, maps show locations, and 3D plots show surfaces, which are different from lollipop charts.

3. Final Answer:

   To compare values using lines and dots for clear differences -> Option A

4. Quick Check:

   Lollipop chart = lines + dots comparison [OK]

Hint: Remember: lollipop = line + dot comparison [OK]

Common Mistakes:

• Confusing lollipop charts with bar charts
• Thinking lollipop charts show geographic data
• Assuming lollipop charts are 3D plots

2. Which matplotlib function is used to create a basic lollipop chart?

easy

A. plt.bar()

B. plt.scatter()

C. plt.plot()

D. plt.stem()

Solution

1. Step 1: Identify the function for lollipop charts

   Matplotlib's plt.stem() creates lollipop charts by drawing lines with markers at the ends.

2. Step 2: Differentiate from other plotting functions

   plt.bar() makes bar charts, plt.scatter() makes scatter plots, and plt.plot() makes line plots, not lollipop charts.

3. Final Answer:

   plt.stem() -> Option D

4. Quick Check:

   Use plt.stem() for lollipop charts [OK]

Hint: Use plt.stem() to combine lines and dots [OK]

Common Mistakes:

• Using plt.bar() instead of plt.stem()
• Confusing scatter plots with lollipop charts
• Trying plt.plot() which lacks markers by default

3. What will the following code output?

import matplotlib.pyplot as plt
x = [1, 2, 3]
y = [4, 2, 5]
plt.stem(x, y)
plt.show()

medium

A. A bar chart with bars at x=1,2,3 and heights 4,2,5

B. A lollipop chart with stems at x=1,2,3 and heights 4,2,5

C. A scatter plot with points at (1,4), (2,2), (3,5)

D. An error because plt.stem() needs more arguments

Solution

1. Step 1: Understand plt.stem() behavior

   The function plt.stem(x, y) draws vertical lines (stems) from zero to each y value at positions x, with dots at the top.

2. Step 2: Analyze the given data points

   Here, x = [1,2,3] and y = [4,2,5], so stems appear at x=1 with height 4, x=2 with height 2, and x=3 with height 5.

3. Final Answer:

   A lollipop chart with stems at x=1,2,3 and heights 4,2,5 -> Option B

4. Quick Check:

   plt.stem(x,y) = lollipop chart [OK]

Hint: plt.stem(x,y) draws stems at x with heights y [OK]

Common Mistakes:

• Thinking plt.stem() creates bar charts
• Confusing scatter plot with lollipop chart
• Assuming plt.stem() requires extra arguments

4. Identify the error in this code for creating a lollipop chart:

import matplotlib.pyplot as plt
x = [1, 2, 3]
y = [4, 5]
plt.stem(x, y)
plt.show()

medium

A. plt.stem() is not imported correctly

B. plt.show() is missing parentheses

C. x and y have different lengths causing a ValueError

D. No error, code runs fine

Solution

1. Step 1: Check lengths of x and y

   x has 3 elements, y has 2 elements. They must be the same length for plotting.

2. Step 2: Understand matplotlib error

   Matplotlib raises a ValueError when x and y lengths differ because it cannot pair points correctly.

3. Final Answer:

   x and y have different lengths causing a ValueError -> Option C

4. Quick Check:

   Equal length x,y needed [OK]

Hint: Check x and y lengths match before plotting [OK]

Common Mistakes:

• Ignoring length mismatch between x and y
• Thinking plt.show() needs no parentheses
• Assuming plt.stem() import causes error

5. Which code snippet correctly changes the marker color to red?

import matplotlib.pyplot as plt
x = [1, 2, 3, 4]
y = [5, 3, 9, 6]
markerline, stemlines, baseline = plt.stem(x, y)
# Change marker color to red
# Which line below is correct?

hard

A. markerline.set_color('red')

B. markerline.get_markerfacecolor()[2].set_color('red')

C. markerline.get_markerfacecolor()[2] = 'red'

D. markerline.get_markerfacecolor()[2].set_facecolor('red')

Solution

1. Step 1: Understand plt.stem() return values

   It returns markerline, stemlines, baseline. markerline is a Line2D object representing all markers.

2. Step 2: Change marker color correctly

   Use markerline.set_color('red') to change the marker colors to red.

3. Final Answer:

   markerline.set_color('red') -> Option A

4. Quick Check:

   Use set_color() on markerline to change marker colors [OK]

Hint: Use set_color() on markerline to change marker colors [OK]

Common Mistakes:

• Trying to assign color directly to a list element
• Using get_markerfacecolor() incorrectly
• Confusing set_color() with set_facecolor()