Relational Plots: Scatter Plots & Line Plots

Relational plots let you explore how two or more numeric variables interact. Seaborn provides scatterplot() and lineplot() (as well as figure-level relplot()) with deep semantic encoding via colors, marker sizes, and grouping styles.

1. Scatter Plots: Spotting Relationships

A scatter plot places one variable along the x-axis and another along the y-axis, rendering a point for every observation in your dataset.

Basic Scatter Plot

Let’s see if restaurant diners who receive larger bills also leave larger tips:

import seaborn as sns
import matplotlib.pyplot as plt
 
tips = sns.load_dataset('tips')
sns.scatterplot(data=tips, x='total_bill', y='tip')
plt.show()

Adding Categories with hue

Pass hue='category_column' to color points based on qualitative dimensions—such as smoking vs. non-smoking tables:

import seaborn as sns
import matplotlib.pyplot as plt
 
tips = sns.load_dataset('tips')
sns.scatterplot(data=tips, x='total_bill', y='tip', hue='smoker')
plt.show()

Figure 2.1: Each dot represents one meal, colored by whether the table was smoking.

Sizing Points by a Numeric Column

You can encode a third continuous variable using size:

import seaborn as sns
import matplotlib.pyplot as plt
 
tips = sns.load_dataset('tips')
sns.scatterplot(data=tips, x='total_bill', y='tip', size='size')
plt.show()

2. Line Plots: Trends Over Time & Confidence Bands

Line plots connect data points in order along the x-axis, making them ideal for time-series and continuous progressions.

Basic Line Plot

Using the classic flights dataset to examine air passenger counts:

import seaborn as sns
import matplotlib.pyplot as plt
 
flights = sns.load_dataset('flights')
sns.lineplot(data=flights, x='year', y='passengers')
plt.show()

Multiple Lines for Different Categories

Split lines across categories using hue:

import seaborn as sns
import matplotlib.pyplot as plt
 
flights = sns.load_dataset('flights')
sns.lineplot(data=flights, x='year', y='passengers', hue='month')
plt.show()

Understanding the Shaded Confidence Band

When multiple measurements exist for each x-value, Seaborn calculates the mean and draws a shaded confidence band (by default, 95% bootstrap confidence interval):

import seaborn as sns
import matplotlib.pyplot as plt
 
fmri = sns.load_dataset('fmri')
sns.lineplot(data=fmri, x='timepoint', y='signal')
plt.show()

Figure 2.2: Line plot showing mean fMRI signal with shaded confidence interval band.

Interactive Demonstration

Explore dataset attributes and inspect observations directly:

Try It Yourself

Problem 1: Scatter Plot with Time Encoding

Using the tips dataset, generate a scatter plot of total_bill vs tip, colored by time (Lunch vs Dinner).

Problem 2: Multi-line Flight Trends

Using the flights dataset, plot passengers over year with a separate line for each month.