Seaborn figure-level vs axes-level in Matplotlib - Hands-On Comparison

Seaborn Figure-level vs Axes-level Plotting

📖 Scenario: You are analyzing a small dataset of daily temperatures and humidity levels. You want to visualize the data using Seaborn plots. Seaborn offers two types of plotting functions: figure-level and axes-level. Figure-level functions create their own figure and manage the layout, while axes-level functions draw on an existing matplotlib axes.Understanding the difference helps you control how your plots appear and combine multiple plots in one figure.

🎯 Goal: Build two simple plots using Seaborn: one figure-level plot and one axes-level plot. Learn how to create the data, configure a helper variable, apply the correct plotting function, and display the result.

📋 What You'll Learn

Create a pandas DataFrame with exact data for temperature and humidity

Create a matplotlib figure and axes for the axes-level plot

Use a Seaborn figure-level function to plot temperature distribution

Use a Seaborn axes-level function to plot humidity on the created axes

Display both plots correctly

💡 Why This Matters

🌍 Real World

Data scientists often need to visualize data clearly. Knowing when to use figure-level or axes-level functions helps create flexible and well-organized plots.

💼 Career

Understanding Seaborn's plotting levels is important for data analysts and scientists to produce clear reports and dashboards.

1

Create the DataFrame with temperature and humidity

Create a pandas DataFrame called weather_data with two columns: 'temperature' and 'humidity'. Use these exact values: temperature = [22, 25, 19, 23, 21], humidity = [30, 45, 50, 40, 35].

Matplotlib

# Create the DataFrame called weather_data with the exact temperature and humidity values
# Your code here

Hint

Use pd.DataFrame with a dictionary containing the two lists for temperature and humidity.

2

Create a matplotlib figure and axes for the axes-level plot

Import matplotlib.pyplot as plt. Create a figure and axes using plt.subplots() and assign them to variables fig and ax.

Matplotlib

import pandas as pd

weather_data = pd.DataFrame({
    'temperature': [22, 25, 19, 23, 21],
    'humidity': [30, 45, 50, 40, 35]
})

# Import matplotlib.pyplot as plt and create fig, ax using plt.subplots()
# Your code here

Hint

Use plt.subplots() to get both figure and axes objects.

3

Plot temperature with a Seaborn figure-level function and humidity with an axes-level function

Import seaborn as sns. Use the figure-level function sns.histplot to plot the 'temperature' column from weather_data. Then use the axes-level function sns.scatterplot to plot 'humidity' on the ax axes. Use weather_data.index for the x-axis and weather_data['humidity'] for the y-axis.

Matplotlib

import pandas as pd
import matplotlib.pyplot as plt

weather_data = pd.DataFrame({
    'temperature': [22, 25, 19, 23, 21],
    'humidity': [30, 45, 50, 40, 35]
})

fig, ax = plt.subplots()

# Import seaborn as sns
# Use sns.histplot to plot temperature
# Use sns.scatterplot to plot humidity on ax
# Your code here

Hint

Figure-level functions like sns.histplot create their own figure. Axes-level functions like sns.scatterplot need the ax parameter to draw on the existing axes.

4

Display the plots

Use plt.show() to display the plots.

Matplotlib

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

weather_data = pd.DataFrame({
    'temperature': [22, 25, 19, 23, 21],
    'humidity': [30, 45, 50, 40, 35]
})

fig, ax = plt.subplots()

sns.histplot(data=weather_data, x='temperature')
sns.scatterplot(x=weather_data.index, y=weather_data['humidity'], ax=ax)

# Display the plots
# Your code here

Hint

Call plt.show() to display all open figures.

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