Facets¶

Faceting creates small multiples - the same plot repeated for subsets of your data.

In [1]:

import pandas as pd
import numpy as np
from ggplotly import *

# Sample data
np.random.seed(42)
df = pd.DataFrame({
    'x': np.random.randn(200),
    'y': np.random.randn(200),
    'category': np.random.choice(['A', 'B', 'C', 'D'], 200),
    'group': np.random.choice(['Group 1', 'Group 2'], 200),
    'value': np.random.rand(200) * 10
})

import pandas as pd import numpy as np from ggplotly import * # Sample data np.random.seed(42) df = pd.DataFrame({ 'x': np.random.randn(200), 'y': np.random.randn(200), 'category': np.random.choice(['A', 'B', 'C', 'D'], 200), 'group': np.random.choice(['Group 1', 'Group 2'], 200), 'value': np.random.rand(200) * 10 })

facet_wrap¶

Wraps a 1D sequence of panels into 2D.

In [2]:

# Basic faceting by one variable
ggplot(df, aes(x='x', y='y')) + geom_point() + facet_wrap('category')

# Basic faceting by one variable ggplot(df, aes(x='x', y='y')) + geom_point() + facet_wrap('category')

Out[2]:

Controlling Layout¶

In [3]:

# Specify number of columns
ggplot(df, aes(x='x', y='y')) + geom_point() + facet_wrap('category', ncol=2)

# Specify number of columns ggplot(df, aes(x='x', y='y')) + geom_point() + facet_wrap('category', ncol=2)

Out[3]:

Free Scales¶

By default, all panels share the same axis scales. Allow them to vary:

In [4]:

# Free both axes
ggplot(df, aes(x='x', y='y')) + geom_point() + facet_wrap('category', scales='free')

# Free both axes ggplot(df, aes(x='x', y='y')) + geom_point() + facet_wrap('category', scales='free')

Out[4]:

In [5]:

# Free only y-axis
ggplot(df, aes(x='x', y='y')) + geom_point() + facet_wrap('category', scales='free_y')

# Free only y-axis ggplot(df, aes(x='x', y='y')) + geom_point() + facet_wrap('category', scales='free_y')

Out[5]:

facet_grid¶

Creates a 2D grid based on two variables.

In [6]:

# Rows by one variable, columns by another
ggplot(df, aes(x='x', y='y')) + geom_point() + facet_grid(rows='category', cols='group')

# Rows by one variable, columns by another ggplot(df, aes(x='x', y='y')) + geom_point() + facet_grid(rows='category', cols='group')

Out[6]:

Grid Layout¶

Use '.' as a placeholder when you only want rows or columns:

In [7]:

# Only rows
ggplot(df, aes(x='x', y='y')) + geom_point() + facet_grid(rows='category', cols='.')

# Only rows ggplot(df, aes(x='x', y='y')) + geom_point() + facet_grid(rows='category', cols='.')

Out[7]:

In [8]:

# Only columns
ggplot(df, aes(x='x', y='y')) + geom_point() + facet_grid(rows='.', cols='group')

# Only columns ggplot(df, aes(x='x', y='y')) + geom_point() + facet_grid(rows='.', cols='group')

Out[8]:

Free Scales in Grid¶

In [9]:

ggplot(df, aes(x='x', y='y')) + geom_point() + facet_grid(rows='category', cols='group', scales='free')

ggplot(df, aes(x='x', y='y')) + geom_point() + facet_grid(rows='category', cols='group', scales='free')

Out[9]:

Examples¶

Comparing Distributions¶

In [10]:

ggplot(df, aes(x='value')) + geom_histogram(bins=20) + facet_wrap('group')

ggplot(df, aes(x='value')) + geom_histogram(bins=20) + facet_wrap('group')

Out[10]:

Scatter by Category¶

In [11]:

ggplot(df, aes(x='x', y='y', color='group')) + geom_point() + facet_wrap('category', ncol=2)

ggplot(df, aes(x='x', y='y', color='group')) + geom_point() + facet_wrap('category', ncol=2)

Out[11]:

Facet Reference¶

Function	Description
facet_wrap(facets)	Wrap 1D into 2D grid
facet_grid(rows, cols)	2D grid of panels

facet_wrap Parameters¶

Parameter	Description
facets	Column name to facet by
ncol	Number of columns
nrow	Number of rows
scales	'fixed', 'free', 'free_x', 'free_y'

facet_grid Parameters¶

Parameter	Description
rows	Column name for rows (use '.' for none)
cols	Column name for columns (use '.' for none)
scales	'fixed', 'free', 'free_x', 'free_y'