Why Small multiples (facet grid) in Matplotlib? - Purpose & Use Cases

The Big Idea

What if you could instantly see all your data groups side by side without endless clicking and copying?

The Scenario

Imagine you have a big table of sales data for different products across many regions and months. You want to compare sales trends side by side for each product. Doing this by drawing one big messy chart or making many separate charts by hand is confusing and slow.

The Problem

Manually creating separate charts for each product means repeating the same steps many times. It's easy to make mistakes, lose track of which chart shows what, and waste time adjusting each plot. The result is cluttered and hard to compare.

The Solution

Small multiples (facet grid) let you automatically split your data by categories and create a grid of similar charts. Each chart shows one subset, arranged neatly so you can easily compare patterns across groups without extra work.

Before vs After

✗ Before

plt.figure()
plt.plot(data_productA)
plt.figure()
plt.plot(data_productB)
# Repeat for each product

✓ After

import seaborn as sns
sns.FacetGrid(data, col='product').map(plt.plot, 'month', 'sales')

What It Enables

It makes comparing many groups side by side simple, clear, and fast, revealing insights that are hard to see in one big chart.

Real Life Example

A marketing analyst can quickly see how different campaigns perform across regions by plotting small multiples of sales trends, spotting which areas need attention.

Key Takeaways

Manual plotting for many groups is slow and error-prone.

Small multiples automate creating many similar charts in a grid.

This helps compare patterns clearly and efficiently.

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