Seaborn figure-level vs axes-level in Matplotlib - Performance Comparison

Time Complexity: Seaborn figure-level vs axes-level

O(n)

Understanding Time Complexity

When using Seaborn with matplotlib, it is important to understand how the time to create plots grows as data size increases.

We want to see how figure-level and axes-level plotting functions differ in their time cost as data grows.

Scenario Under Consideration

Analyze the time complexity of these two Seaborn plotting approaches.

import seaborn as sns
import matplotlib.pyplot as plt

# Figure-level plot
sns.relplot(data=large_data, x="x", y="y", kind="scatter")

# Axes-level plot
fig, ax = plt.subplots()
sns.scatterplot(data=large_data, x="x", y="y", ax=ax)
plt.show()

The first creates a figure-level plot managing figure and axes internally. The second creates an axes-level plot on an existing axes.

Identify Repeating Operations

Look at what repeats as data size grows.

• Primary operation: Plotting each data point as a marker on the axes.
• How many times: Once per data point in the dataset.

How Execution Grows With Input

As the number of data points increases, the number of markers to draw grows the same way.

Input Size (n)	Approx. Operations
10	10 markers drawn
100	100 markers drawn
1000	1000 markers drawn

Pattern observation: The work grows directly with the number of points; doubling points doubles work.

Final Time Complexity

Time Complexity: O(n)

This means the time to create the plot grows linearly with the number of data points.

Common Mistake

[X] Wrong: "Figure-level plots are always slower because they manage more things."

[OK] Correct: Both figure-level and axes-level plots spend most time drawing points, so their time grows similarly with data size.

Interview Connect

Understanding how plotting time grows helps you explain performance in data visualization tasks clearly and confidently.

Self-Check

What if we added a loop to create multiple subplots each with n data points? How would the time complexity change?

Practice

1. Which of the following best describes a figure-level function in Seaborn?

easy

A. It creates a complete plot including figure and axes automatically.

B. It only modifies existing axes without creating a new figure.

C. It is used to customize axis labels after plotting.

D. It requires manual creation of figure and axes before plotting.

Solution

1. Step 1: Understand figure-level function role

   Figure-level functions in Seaborn create the entire plot including figure and axes automatically.

2. Step 2: Compare with axes-level functions

   Axes-level functions only draw on existing axes and do not create the figure.

3. Final Answer:

   It creates a complete plot including figure and axes automatically. -> Option A

4. Quick Check:

   Figure-level = creates full plot [OK]

Hint: Figure-level functions create whole plots; axes-level modify existing axes [OK]

Common Mistakes:

• Confusing figure-level with axes-level functions
• Thinking figure-level functions require manual axes creation
• Assuming axes-level functions create figures automatically

2. Which of the following is the correct way to use an axes-level function in Seaborn on existing axes?

easy

A. sns.scatterplot(data=df, x='age', y='height')

B. sns.scatterplot(ax=ax, data=df, x='age', y='height')

C. sns.relplot(data=df, x='age', y='height')

D. sns.relplot(ax=ax, data=df, x='age', y='height')

Solution

1. Step 1: Identify axes-level function usage

   Axes-level functions like scatterplot can accept an ax parameter to plot on existing axes.

2. Step 2: Check options for correct syntax

   sns.scatterplot(ax=ax, data=df, x='age', y='height') correctly passes ax=ax to scatterplot, an axes-level function.

3. Final Answer:

   sns.scatterplot(ax=ax, data=df, x='age', y='height') -> Option B

4. Quick Check:

   Axes-level functions use ax= parameter [OK]

Hint: Axes-level functions accept ax= parameter to plot on existing axes [OK]

Common Mistakes:

• Using figure-level functions with ax= parameter (not supported)
• Confusing relplot (figure-level) with scatterplot (axes-level)
• Omitting ax= when plotting on existing axes

3. What will the following code output?

import seaborn as sns
import matplotlib.pyplot as plt

df = sns.load_dataset('tips')
fig, ax = plt.subplots()
sns.scatterplot(data=df, x='total_bill', y='tip', ax=ax)
plt.title('Scatterplot on existing axes')
plt.show()

medium

A. A scatterplot of total_bill vs tip on a new figure with default title

B. An empty plot with no points

C. A scatterplot of total_bill vs tip on the existing axes with custom title

D. An error because scatterplot cannot accept ax parameter

Solution

1. Step 1: Analyze code creating figure and axes

   The code creates a figure and axes with plt.subplots() and stores axes in ax.

2. Step 2: Understand scatterplot with ax parameter

   scatterplot is axes-level and plots on the given ax. Title is set on the figure.

3. Final Answer:

   A scatterplot of total_bill vs tip on the existing axes with custom title -> Option C

4. Quick Check:

   Axes-level plot on existing axes = scatterplot with ax= [OK]

Hint: Axes-level plots use ax= to draw on existing axes [OK]

Common Mistakes:

• Expecting scatterplot to create new figure automatically
• Thinking ax= causes error with scatterplot
• Assuming title applies only to figure-level plots

4. Identify the error in this code snippet:

import seaborn as sns
import matplotlib.pyplot as plt

df = sns.load_dataset('tips')
fig, ax = plt.subplots()
sns.relplot(data=df, x='total_bill', y='tip', ax=ax)
plt.show()

medium

A. plt.show() must be called before relplot

B. plt.subplots() is missing required arguments

C. DataFrame 'df' is not loaded correctly

D. relplot does not accept ax parameter; it creates its own figure

Solution

1. Step 1: Check relplot function parameters

   relplot is a figure-level function and does not accept an ax parameter.

2. Step 2: Understand error cause

   Passing ax=ax to relplot causes an error because it manages figure creation internally.

3. Final Answer:

   relplot does not accept ax parameter; it creates its own figure -> Option D

4. Quick Check:

   Figure-level functions ignore ax= and raise error if given [OK]

Hint: Figure-level functions like relplot do NOT accept ax= [OK]

Common Mistakes:

• Passing ax= to figure-level functions
• Confusing relplot with scatterplot usage
• Assuming plt.subplots() is incorrect here

5. You want to create a figure with two different plots side by side: a histogram and a scatterplot. Which approach correctly uses Seaborn's figure-level and axes-level functions together?

hard

A. Use sns.histplot() on one axes and sns.scatterplot() on another axes created by plt.subplots().

B. Use sns.relplot() twice, each creating its own figure, then combine figures manually.

C. Use sns.histplot() with ax= parameter and sns.relplot() without ax= on the same axes.

D. Use sns.relplot() with ax= parameter for both plots on shared axes.

Solution

1. Step 1: Understand figure-level vs axes-level functions

   relplot is figure-level and creates its own figure; histplot and scatterplot are axes-level and can plot on existing axes.

2. Step 2: Plan side-by-side plots

   Creating subplots with plt.subplots() and plotting axes-level functions on each axes allows side-by-side plots in one figure.

3. Step 3: Evaluate options

   Use sns.histplot() on one axes and sns.scatterplot() on another axes created by plt.subplots(). correctly uses axes-level functions on subplots. Options A, B, and D misuse figure-level functions or ax= parameter.

4. Final Answer:

   Use sns.histplot() on one axes and sns.scatterplot() on another axes created by plt.subplots(). -> Option A

5. Quick Check:

   Axes-level functions + subplots = combined figure [OK]

Hint: Use axes-level functions with plt.subplots() for combined plots [OK]

Common Mistakes:

• Trying to use relplot with ax= parameter
• Using multiple figure-level functions expecting one figure
• Mixing figure-level and axes-level functions on same axes