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

Need a 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

Need a 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

Need a 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

Need a hint?

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