Small multiples (facet grid) in Matplotlib - Mini Project: Build & Apply

Create Small Multiples (Facet Grid) with Matplotlib

📖 Scenario: You are a data analyst at a fruit store. You have sales data for different fruits over several months. You want to compare the monthly sales of each fruit side by side to see trends easily.

🎯 Goal: Build a small multiples (facet grid) plot using matplotlib to show monthly sales for each fruit in separate subplots.

📋 What You'll Learn

Create a dictionary with fruit names as keys and lists of monthly sales as values.

Create a list of month names to use as x-axis labels.

Use a configuration variable to store the number of fruits.

Use a loop to create subplots for each fruit.

Plot monthly sales data for each fruit in its own subplot.

Add titles to each subplot with the fruit name.

Display the final figure with all subplots.

💡 Why This Matters

🌍 Real World

Small multiples help compare different groups or categories side by side, making it easier to spot patterns or differences in data over time or conditions.

💼 Career

Data analysts and scientists often use facet grids to visualize segmented data clearly, which helps in reporting and decision-making.

1

Create the sales data dictionary and months list

Create a dictionary called fruit_sales with these exact entries: 'Apple': [30, 35, 40, 45], 'Banana': [20, 25, 30, 35], 'Cherry': [15, 18, 20, 22]. Also create a list called months with these exact values: 'Jan', 'Feb', 'Mar', 'Apr'.

Matplotlib

# Create the fruit_sales dictionary and months list
# Your code here

Hint

Use curly braces {} to create the dictionary and square brackets [] for the lists.

2

Create a variable for the number of fruits

Create a variable called num_fruits and set it to the number of keys in the fruit_sales dictionary.

Matplotlib

fruit_sales = {
    'Apple': [30, 35, 40, 45],
    'Banana': [20, 25, 30, 35],
    'Cherry': [15, 18, 20, 22]
}
months = ['Jan', 'Feb', 'Mar', 'Apr']
# Create num_fruits variable
# Your code here

Hint

Use the len() function to count the keys in the dictionary.

3

Create subplots and plot each fruit's sales

Import matplotlib.pyplot as plt. Use plt.subplots() with num_fruits rows and 1 column to create subplots. Use a for loop with variables index and fruit from enumerate(fruit_sales) to plot each fruit's sales on its subplot. Use ax.plot(months, fruit_sales[fruit]) to plot. Set the subplot title with ax.set_title(fruit). Use plt.tight_layout() to adjust spacing.

Matplotlib

fruit_sales = {
    'Apple': [30, 35, 40, 45],
    'Banana': [20, 25, 30, 35],
    'Cherry': [15, 18, 20, 22]
}
months = ['Jan', 'Feb', 'Mar', 'Apr']
num_fruits = len(fruit_sales)

# Import matplotlib and create subplots
# Use a for loop to plot each fruit's sales
# Your code here

Hint

Remember to handle the case when there is only one subplot (axes is not a list).

4

Display the final figure

Use plt.show() to display the figure with all the subplots.

Matplotlib

import matplotlib.pyplot as plt

fruit_sales = {
    'Apple': [30, 35, 40, 45],
    'Banana': [20, 25, 30, 35],
    'Cherry': [15, 18, 20, 22]
}
months = ['Jan', 'Feb', 'Mar', 'Apr']
num_fruits = len(fruit_sales)

fig, axes = plt.subplots(num_fruits, 1, figsize=(6, 4 * num_fruits))

for index, fruit in enumerate(fruit_sales):
    ax = axes[index] if num_fruits > 1 else axes
    ax.plot(months, fruit_sales[fruit])
    ax.set_title(fruit)

plt.tight_layout()
# Display the plot
# Your code here

Hint

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

Practice

1. What is the main purpose of using small multiples (facet grid) in matplotlib?

easy

A. To display multiple related charts side by side for easy comparison

B. To create a single large plot with multiple lines

C. To animate a plot over time

D. To change the color scheme of a plot

Solution

1. Step 1: Understand the concept of small multiples

   Small multiples are multiple small charts arranged in a grid to compare different groups or categories easily.

2. Step 2: Identify the purpose in matplotlib

   Matplotlib uses small multiples to show many charts side by side, making it easier to compare data visually.

3. Final Answer:

   To display multiple related charts side by side for easy comparison -> Option A

4. Quick Check:

   Small multiples = multiple charts side by side [OK]

Hint: Small multiples = many small charts for comparison [OK]

Common Mistakes:

• Confusing small multiples with animations
• Thinking it changes colors only
• Assuming it creates one big plot

2. Which of the following is the correct way to create a 2x2 grid of subplots in matplotlib?

easy

A. fig, axes = plt.subplots(4)

B. fig, axes = plt.subplots(1, 4)

C. fig, axes = plt.subplots(2, 2)

D. fig, axes = plt.subplots(2)

Solution

1. Step 1: Recall plt.subplots() syntax

   plt.subplots(rows, columns) creates a grid of subplots with given rows and columns.

2. Step 2: Match the grid size

   To create a 2x2 grid, use plt.subplots(2, 2).

3. Final Answer:

   fig, axes = plt.subplots(2, 2) -> Option C

4. Quick Check:

   plt.subplots(2, 2) = 2 rows and 2 columns [OK]

Hint: Use plt.subplots(rows, columns) for grid size [OK]

Common Mistakes:

• Using single number for grid shape
• Confusing rows and columns
• Missing one dimension in arguments

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

import matplotlib.pyplot as plt
fig, axes = plt.subplots(1, 3)
data = [1, 2, 3]
for i, ax in enumerate(axes):
    ax.plot([data[i]] * 3)
plt.show()

medium

A. Three line plots each with three points of the same value 1, 2, and 3 respectively

B. A single plot with three lines of values 1, 2, and 3

C. Error because axes is not iterable

D. Three scatter plots with points 1, 2, and 3

Solution

1. Step 1: Understand plt.subplots(1, 3)

   This creates 1 row and 3 columns of subplots, so axes is an array of 3 axes objects.

2. Step 2: Loop plots each subplot

   For each axis, it plots a line with three points all equal to data[i] (1, then 2, then 3).

3. Final Answer:

   Three line plots each with three points of the same value 1, 2, and 3 respectively -> Option A

4. Quick Check:

   Loop over axes plots lines with repeated values [OK]

Hint: Loop over axes to plot each subplot separately [OK]

Common Mistakes:

• Thinking axes is a single plot
• Assuming error due to axes type
• Confusing line plot with scatter plot

4. Identify the error in this code for creating a 2x2 grid of plots:

fig, axes = plt.subplots(2, 2)
for i in range(4):
    axes[i].plot([1, 2, 3])
plt.show()

medium

A. The plot data list is invalid

B. plt.subplots(2, 2) creates only 2 plots, not 4

C. plt.show() is missing

D. axes is a 2D array, so axes[i] indexing causes an error

Solution

1. Step 1: Understand axes shape from plt.subplots(2, 2)

   axes is a 2x2 numpy array of Axes objects, not a flat list.

2. Step 2: Identify indexing error

   axes[i] tries to index a 2D array with one index, causing an error. Correct is axes[row, col] or flatten axes first.

3. Final Answer:

   axes is a 2D array, so axes[i] indexing causes an error -> Option D

4. Quick Check:

   2D axes need two indices or flatten before looping [OK]

Hint: 2D axes need two indices or flatten before indexing [OK]

Common Mistakes:

• Assuming axes is 1D array
• Ignoring error from wrong indexing
• Thinking plt.show() is missing

5. You have a dataset with sales data for 3 regions. How would you create a 1-row, 3-column grid of plots showing sales trends for each region separately using matplotlib?

hard

A. Use plt.plot() three times without subplots

B. Use plt.subplots(1, 3), loop over axes, and plot each region's data on each subplot

C. Use plt.subplots(3, 1) and plot all regions on each subplot

D. Use plt.subplots(1, 1) and plot all regions on the same plot

Solution

1. Step 1: Create a 1x3 grid for three regions

   Use plt.subplots(1, 3) to create one row and three columns of plots.

2. Step 2: Loop over axes and plot each region's data

   Loop through each axis and plot the sales data for each region separately to compare trends side by side.

3. Final Answer:

   Use plt.subplots(1, 3), loop over axes, and plot each region's data on each subplot -> Option B

4. Quick Check:

   One row, three columns, loop to plot each region [OK]

Hint: Create grid then loop axes to plot groups separately [OK]

Common Mistakes:

• Plotting all data on one plot
• Using wrong grid shape
• Not looping over axes