Storytelling with visualization sequence in Matplotlib - Mini Project: Build & Apply

Storytelling with visualization sequence

📖 Scenario: You are a data analyst working for a small bakery. You have sales data for three types of pastries over a week. Your manager wants to see a clear story of how each pastry sold each day, so they can decide what to bake more of next week.

🎯 Goal: Create a sequence of visualizations using matplotlib to show daily sales of three pastries. Start by setting up the data, then configure the days, plot the sales for each pastry, and finally display the combined chart to tell the sales story clearly.

📋 What You'll Learn

Use a dictionary to store sales data for three pastries: Croissant, Muffin, and Danish.

Create a list of days representing a week.

Plot a line chart for each pastry's sales over the days using matplotlib.

Display the final combined plot with a legend and labels.

💡 Why This Matters

🌍 Real World

Visualizing sales data helps businesses understand trends and make better decisions about inventory and marketing.

💼 Career

Data analysts and business intelligence professionals often create clear visual stories from data to communicate insights to managers and stakeholders.

1

DATA SETUP: Create sales data dictionary

Create a dictionary called sales with these exact entries: 'Croissant': [20, 22, 18, 25, 30, 28, 35], 'Muffin': [15, 18, 14, 20, 22, 25, 27], and 'Danish': [10, 12, 9, 15, 18, 20, 22].

Matplotlib

# Create the sales dictionary with exact pastry names and sales lists
# Your code here

Hint

Use a dictionary with pastry names as keys and lists of daily sales as values.

2

CONFIGURATION: Create list of days

Create a list called days with these exact values: ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'].

Matplotlib

sales = {
    'Croissant': [20, 22, 18, 25, 30, 28, 35],
    'Muffin': [15, 18, 14, 20, 22, 25, 27],
    'Danish': [10, 12, 9, 15, 18, 20, 22]
}
# Create the list of days for the week
# Your code here

Hint

Use a list with the exact day abbreviations as strings.

3

CORE LOGIC: Plot sales lines for each pastry

Import matplotlib.pyplot as plt. Use a for loop with variables pastry and sales_list to iterate over sales.items(). Inside the loop, plot days on the x-axis and sales_list on the y-axis with label=pastry.

Matplotlib

sales = {
    'Croissant': [20, 22, 18, 25, 30, 28, 35],
    'Muffin': [15, 18, 14, 20, 22, 25, 27],
    'Danish': [10, 12, 9, 15, 18, 20, 22]
}
days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']

# Import matplotlib.pyplot as plt
# Use a for loop to plot each pastry's sales over days
# Your code here

Hint

Remember to import matplotlib.pyplot as plt before plotting.

Use a for loop to plot each pastry's sales with labels.

4

OUTPUT: Display the final plot with labels and legend

Add x-axis label 'Day' and y-axis label 'Number Sold' using plt.xlabel() and plt.ylabel(). Add a title 'Pastry Sales Over a Week' with plt.title(). Show the legend with plt.legend(). Finally, display the plot with plt.show().

Matplotlib

import matplotlib.pyplot as plt

sales = {
    'Croissant': [20, 22, 18, 25, 30, 28, 35],
    'Muffin': [15, 18, 14, 20, 22, 25, 27],
    'Danish': [10, 12, 9, 15, 18, 20, 22]
}
days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']

for pastry, sales_list in sales.items():
    plt.plot(days, sales_list, label=pastry)

# Add labels, title, legend, and show the plot
# Your code here

Hint

Use plt.xlabel(), plt.ylabel(), plt.title(), plt.legend(), and plt.show() to complete the plot.

Practice

1. What is the main purpose of using multiple plots in a storytelling visualization sequence?

easy

A. To make the plot colors more vibrant

B. To reduce the size of the data

C. To break data into parts and explain it step-by-step

D. To avoid using titles and labels

Solution

1. Step 1: Understand storytelling with visualization

   Storytelling with visualization means showing data in parts to explain it clearly.

2. Step 2: Purpose of multiple plots

   Using multiple plots helps break the data into smaller pieces to tell a clear story step-by-step.

3. Final Answer:

   To break data into parts and explain it step-by-step -> Option C

4. Quick Check:

   Storytelling = breaking data into parts [OK]

Hint: Multiple plots show data in steps for clear explanation [OK]

Common Mistakes:

• Thinking colors are the main reason for multiple plots
• Believing multiple plots reduce data size
• Ignoring the importance of titles and labels

2. Which of the following is the correct way to create two plots side by side using matplotlib?

easy

A. plt.subplot(2, 1, 1) and plt.subplot(2, 1, 3)

B. plt.subplot(1, 2, 1) and plt.subplot(1, 2, 2)

C. plt.subplot(1, 1, 1) and plt.subplot(1, 1, 2)

D. plt.subplot(3, 1, 1) and plt.subplot(3, 1, 2)

Solution

1. Step 1: Understand plt.subplot parameters

   plt.subplot(rows, columns, plot_number) arranges plots in a grid.

2. Step 2: Create two side-by-side plots

   One row and two columns means plt.subplot(1, 2, 1) and plt.subplot(1, 2, 2) for two plots side by side.

3. Final Answer:

   plt.subplot(1, 2, 1) and plt.subplot(1, 2, 2) -> Option B

4. Quick Check:

   One row, two columns = plt.subplot(1, 2, x) [OK]

Hint: Use plt.subplot(1, 2, x) for two side-by-side plots [OK]

Common Mistakes:

• Using wrong plot numbers like 3 in a 2-plot layout
• Mixing rows and columns incorrectly
• Trying to create more plots than grid allows

3. What will be the output arrangement of the following code?

import matplotlib.pyplot as plt
plt.subplot(2, 1, 1)
plt.title('Top Plot')
plt.subplot(2, 1, 2)
plt.title('Bottom Plot')
plt.show()

medium

A. Error because plt.title() is used twice

B. Two plots side by side with titles 'Top Plot' and 'Bottom Plot'

C. One plot with both titles overlapping

D. Two plots stacked vertically with titles 'Top Plot' and 'Bottom Plot'

Solution

1. Step 1: Understand plt.subplot(2, 1, x)

   This creates 2 rows and 1 column, stacking plots vertically.

2. Step 2: Titles assigned to each subplot

   First plot gets 'Top Plot', second gets 'Bottom Plot', shown stacked vertically.

3. Final Answer:

   Two plots stacked vertically with titles 'Top Plot' and 'Bottom Plot' -> Option D

4. Quick Check:

   2 rows, 1 column = vertical stack [OK]

Hint: Rows first, columns second in plt.subplot for layout [OK]

Common Mistakes:

• Thinking plots are side by side with (2,1,x)
• Assuming plt.title() causes error if used twice
• Expecting one plot instead of two

4. Identify the error in this code that tries to create a 2x2 grid of plots:

import matplotlib.pyplot as plt
plt.subplot(2, 2, 1)
plt.plot([1,2,3])
plt.subplot(2, 2, 5)
plt.plot([3,2,1])
plt.show()

medium

A. Using subplot number 5 in a 2x2 grid causes an error

B. plt.plot() cannot be used inside subplot

C. Missing plt.figure() before subplots

D. No error, code runs fine

Solution

1. Step 1: Understand subplot numbering in 2x2 grid

   2 rows and 2 columns means subplot numbers 1 to 4 only.

2. Step 2: Check subplot number 5 usage

   Using subplot(2, 2, 5) is invalid and causes an error.

3. Final Answer:

   Using subplot number 5 in a 2x2 grid causes an error -> Option A

4. Quick Check:

   Max subplot number = rows*columns = 4 [OK]

Hint: Subplot number must be ≤ rowsxcolumns [OK]

Common Mistakes:

• Thinking plt.plot() can't be inside subplot
• Believing plt.figure() is mandatory before subplots
• Ignoring subplot numbering limits

5. You want to tell a story showing sales growth over 3 years with separate plots for each year. Which approach best helps your audience understand the story clearly?

hard

A. Create 3 subplots in one column using plt.subplot(3, 1, x) with clear titles and labels

B. Plot all years on one plot without labels

C. Create 1 subplot and plot only the last year's data

D. Use plt.subplot(1, 3, x) but skip titles and labels

Solution

1. Step 1: Choose subplot layout for storytelling

   Using 3 rows and 1 column (plt.subplot(3, 1, x)) stacks plots vertically, showing each year clearly.

2. Step 2: Importance of titles and labels

   Clear titles and labels help the audience understand each year's data easily.

3. Final Answer:

   Create 3 subplots in one column using plt.subplot(3, 1, x) with clear titles and labels -> Option A

4. Quick Check:

   Separate plots + clear labels = better storytelling [OK]

Hint: Stack plots vertically with titles for clear story [OK]

Common Mistakes:

• Plotting all data in one plot without labels
• Skipping titles and labels reduces clarity
• Showing only last year's data misses story