relational plot (relplot)

Syntax

Key Parameters

Core Parameters

• data: Dataset (Pandas DataFrame) to visualize.
• x, y: Variables to be plotted on the x- and y-axes.
• kind: Type of relational plot ("scatter" or "line").

Aesthetic Mappings

• hue: Differentiates points or lines by color (categorical or numerical variable).
• size: Differentiates points by size (numerical or categorical variable).
• style: Differentiates points or lines by marker style or line dashes (categorical variable).
• palette: Specifies color palette for hue.
• size_order: Specifies order for size categories.
• sizes: Specifies range of marker sizes (e.g., (min_size, max_size)).

Faceting

• col, row: Create subplots based on categorical variables.
• col_wrap: Number of columns in the subplot grid.
• row_order, col_order: Specify order of categorical variables for faceting.

Figure Settings

• height: Height (in inches) of each subplot.
• aspect: Aspect ratio (width/height) of each subplot.

Examples with Inbuilt Datasets

Dataset Setup

We will use the inbuilt datasets provided by Seaborn, such as tips, fmri, and penguins.

import seaborn as sns
import matplotlib.pyplot as plt

# Load datasets
tips = sns.load_dataset('tips')
fmri = sns.load_dataset('fmri')
penguins = sns.load_dataset('penguins')

penguins = sns.load_dataset('penguins')
penguins.head()

tips = sns.load_dataset('tips')
tips.head()

fmri = sns.load_dataset('fmri')
fmri.head()

1. Basic Scatter Plot

sns.relplot(data=tips, 
            x='total_bill', 
            y='tip', 
            kind='scatter')
            
            
plt.title("Basic Scatter Plot")
plt.show()

Use Case: Visualize the relationship between total bill and tip.

2. Basic Line Plot

sns.relplot(data=fmri, 
            x='timepoint', 
            y='signal', 
            kind='line')
            
            
plt.title("Basic Line Plot")
plt.show()

Use Case: Visualize how the signal changes over timepoint in the fmri dataset.

The shaded area around the plot represents a 95% confidence interval, which can be adjusted to show standard deviation by modifying the code:

sns.relplot(data=fmri, x='timepoint', y='signal', kind='line', errorbar='sd')
plt.title("Basic Line Plot")
plt.show()

You can also remove the confidence interval line entirely by setting the errorbar parameter to none like this

sns.relplot(data=fmri, x='timepoint', y='signal', kind='line', errorbar=None)
plt.title("Basic Line Plot")
plt.show()

3. Scatter Plot with hue

sns.relplot(data=tips, 
            x='total_bill', 
            y='tip', 
            hue='sex', 
            kind='scatter')
            
            
plt.title("Scatter Plot with Hue")
plt.show()

Key Parameter: hue differentiates data points by gender.

4. Line Plot with hue

sns.relplot(data=fmri, 
            x='timepoint', 
            y='signal', 
            hue='region', 
            kind='line')
            
            
plt.title("Line Plot with Hue")
plt.show()

Key Parameter: hue colors the lines based on region.

5. Scatter Plot with size

sns.relplot(data=tips, 
            x='total_bill', 
            y='tip', 
            size='size', 
            sizes=(20, 200), 
            kind='scatter')
            
            
plt.title("Scatter Plot with Variable Sizes")
plt.show()

Key Parameter: size adjusts the marker sizes based on the party size.

6. Scatter Plot with style

sns.relplot(data=tips, 
            x='total_bill', 
            y='tip', 
            style='sex', 
            kind='scatter')
            
plt.title("Scatter Plot with Style")
plt.show()

Key Parameter: style differentiates points by marker based on gender.

7. Subplots in columns

sns.relplot(data = tips, 
            x='total_bill', 
            y='tip', 
            col='sex', 
            kind='scatter')
            
            
plt.suptitle("Faceted Scatter Plot", y=1.05)
plt.show()

Key Parameter: col creates subplots for each sex

The plt.suptitle("Faceted Scatter Plot", y=1.05) function is used to add a super title to the entire figure when using Seaborn's relplot (or other FacetGrid-based plots). This super title is placed above all the subplots (facets) created by sns.relplot.

• "Faceted Scatter Plot": The text of the super title.
• y=1.05: The vertical position of the super title. The default value is 1.0, which places the title at the top of the figure. By setting y=1.05, the title is moved slightly above the top of the figure to avoid overlapping with the subplots.

8. Subplots in rows

sns.relplot(data = tips, 
            x='total_bill', 
            y='tip', 
            row ='sex', 
            kind='scatter')
            
            
plt.suptitle("Faceted Scatter Plot", y=1.05)
plt.show()

9. Wrapping columns

sns.relplot(data=tips, 
            x="total_bill", 
            y="tip", 
            kind="scatter", 
            col="day", 
            col_wrap=2)

plt.show()

10. Ordering columns

sns.relplot(data=tips, 
						x="total_bill",
						y="tip", 
						kind="scatter", 
						col="day", 
						col_wrap=2, 
						col_order=["Thur", "Fri", "Sat", "Sun"])

plt.show()

8. Faceted Line Plot

sns.relplot(data=fmri, 
            x='timepoint', 
            y='signal', 
            hue='event', 
            kind='line', 
            col='region')
            
            
plt.suptitle("Faceted Line Plot", y=1.05)
plt.show()

Key Parameter: col creates subplots for each brain region.

9. Line Plot with Custom dashes

sns.relplot(data=fmri, 
            x='timepoint', 
            y='signal', 
            hue='event', 
            style='event', 
            kind='line', 
            dashes=False)
            
plt.title("Line Plot with Custom Dashes")
plt.show()

Key Parameter: dashes=False ensures solid lines.

10. Scatter Plot with Transparency

sns.relplot(data=tips, 
            x='total_bill', 
            y='tip', 
            alpha=0.6, 
            kind='scatter')
            
            
plt.title("Scatter Plot with Transparency")
plt.show()

Key Parameter: alpha adjusts point transparency.

11. Faceted Scatter Plot with Row and Column

sns.relplot(data=penguins, 
            x='bill_length_mm', 
            y='bill_depth_mm', 
            row='sex', 
            col='species', 
            kind='scatter')
            
plt.suptitle("Faceted Scatter Plot with Row and Column", y=1.05)
plt.show()

Key Parameters: row and col create a grid layout based on gender and species.

12. Scatter Plot with Custom Marker Sizes

sns.relplot(data=tips, 
            x='total_bill', 
            y='tip', 
            size='size', 
            sizes=(50, 300), 
            kind='scatter')
            
            
plt.title("Scatter Plot with Custom Marker Sizes")
plt.show()

Key Parameter: sizes=(50, 300) scales marker sizes.