FacetGrid for multi-panel views in Data Analysis Python - Step-by-Step Execution

Concept Flow - FacetGrid for multi-panel views

Load Data

↓

Create FacetGrid Object

↓

Map Plot Function to Grid

↓

Adjust Layout

↓

Show Multi-panel Plot

The flow starts by loading data, then creating a FacetGrid object to define panels, mapping a plot function to each panel, adjusting layout, and finally displaying the multi-panel plot.

Execution Sample

Data Analysis Python

import seaborn as sns
import matplotlib.pyplot as plt

iris = sns.load_dataset('iris')
g = sns.FacetGrid(iris, col='species')
g.map(sns.histplot, 'sepal_length')
g.fig.tight_layout()
plt.show()

This code loads the iris dataset, creates a FacetGrid with one panel per species, maps a histogram of sepal_length to each panel, and shows the plot.

Execution Table

Step	Action	Data/Variable	Result/Output
1	Load iris dataset	iris	DataFrame with 150 rows, 5 columns
2	Create FacetGrid with col='species'	g = FacetGrid object	Grid with 3 panels (setosa, versicolor, virginica)
3	Map sns.histplot to 'sepal_length'	g.map(sns.histplot, 'sepal_length')	Each panel shows histogram of sepal_length for that species
4	Adjust layout	g.fig.tight_layout()	Panels arranged neatly without overlap
5	Show plot	plt.show()	Multi-panel histogram plot displayed
6	End	-	Execution complete, plot visible

💡 All steps completed, multi-panel plot displayed successfully

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	Final
iris	undefined	DataFrame(150x5)	DataFrame(150x5)	DataFrame(150x5)	DataFrame(150x5)	DataFrame(150x5)
g	undefined	undefined	FacetGrid object with 3 panels	FacetGrid with histograms mapped	FacetGrid layout adjusted	FacetGrid ready for display

Key Moments - 3 Insights

Why does FacetGrid create multiple panels instead of one plot?

Because we specify 'col="species"' when creating FacetGrid, it splits the data by species and creates one panel per species, as shown in execution_table step 2.

What does the map function do in FacetGrid?

The map function applies the plotting function (sns.histplot) to each subset of data in each panel, as seen in execution_table step 3.

Why do we call plt.show() at the end?

plt.show() displays the plot window with all panels rendered; without it, the plot may not appear, as in execution_table step 5.

Visual Quiz - 3 Questions

Look at the execution table, how many panels does the FacetGrid create after step 2?

A 3 panels, one for each species

B 1 panel with all species combined

C 5 panels, one for each column

D No panels created yet

💡 Hint

Check the 'Result/Output' column in step 2 of the execution_table.

At which step does the histogram plot get applied to each panel?

A Step 1

B Step 3

C Step 2

D Step 5

💡 Hint

Look for the action involving 'map' in the execution_table.

If we change col='species' to row='species', how would the panels be arranged?

A Panels arranged in columns

B Only one panel shown

C Panels arranged in rows

D Panels arranged randomly

💡 Hint

FacetGrid arranges panels in columns if 'col' is used, and in rows if 'row' is used.

Concept Snapshot

FacetGrid creates multiple plot panels by splitting data by a variable.
Syntax: g = sns.FacetGrid(data, col='var')
Map a plot: g.map(plot_func, 'column')
Use plt.show() to display.
Panels help compare subsets side-by-side.

Full Transcript

This example shows how to use seaborn's FacetGrid to create multiple panels for data visualization. First, the iris dataset is loaded. Then, a FacetGrid object is created with one panel per species by specifying col='species'. Next, a histogram plot of sepal_length is mapped to each panel. The layout is adjusted to avoid overlap, and finally, plt.show() displays the multi-panel plot. Variables iris and g change as the code runs, with g holding the grid object. Key points include how FacetGrid splits data into panels and how map applies plots to each panel. The visual quiz tests understanding of panel count, mapping step, and panel arrangement.