

Facet and Trellis Plots in Python

How to make Facet and Trellis Plots in Python with Plotly.

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Facet and Trellis Plots

Facet plots, also known as trellis plots or small multiples, are figures made up of multiple subplots which have the same set of axes, where each subplot shows a subset of the data. While it is straightforward to use plotly's subplot capabilities to make such figures, it's far easier to use the built-in facet_row and facet_col arguments in the various Plotly Express functions.

Scatter Plot Column Facets

In [1]:
import as px
df =
fig = px.scatter(df, x="total_bill", y="tip", color="smoker", facet_col="sex")

Bar Chart Row Facets

In [2]:
import as px
df =
fig =, x="size", y="total_bill", color="sex", facet_row="smoker")

Wrapping Column Facets

When the facet dimension has a large number of unique values, it is possible to wrap columns using the facet_col_wrap argument.

In [3]:
import as px
df =
fig = px.scatter(df, x='gdpPercap', y='lifeExp', color='continent', size='pop',
                facet_col='year', facet_col_wrap=4)

Histogram Facet Grids

In [4]:
import as px
df =
fig = px.histogram(df, x="total_bill", y="tip", color="sex", facet_row="time", facet_col="day",
       category_orders={"day": ["Thur", "Fri", "Sat", "Sun"], "time": ["Lunch", "Dinner"]})

Facets With Independent Axes

By default, facet axes are linked together: zooming inside one of the facets will also zoom in the other facets. You can disable this behaviour when you use facet_row only, by disabling matches on the Y axes, or when using facet_col only, by disabling matches on the X axes. It is not recommended to use this approach when using facet_row and facet_col together, as in this case it becomes very hard to understand the labelling of axes and grid lines.

In [5]:
import as px
df =
fig = px.scatter(df, x="total_bill", y="tip", color='sex', facet_row="day")
In [6]:
import as px
df =
fig = px.scatter(df, x="total_bill", y="tip", color='sex', facet_col="day")

Customize Subplot Figure Titles

Since subplot figure titles are annotations, you can use the for_each_annotation function to customize them.

In the following example, we pass a lambda function to for_each_annotation in order to change the figure subplot titles from smoker=No and smoker=Yes to just No and Yes.

In [7]:
import as px

fig = px.scatter(, x="total_bill", y="tip", facet_col="smoker")
fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
In [ ]:

Synchronizing axes in subplots with matches

Using facet_col from let zoom and pan each facet to the same range implicitly. However, if the subplots are created with make_subplots, the axis needs to be updated with matches parameter to update all the subplots accordingly.

Zoom in one trace below, to see the other subplots zoomed to the same x-axis range. To pan all the subplots, click and drag from the center of x-axis to the side:

In [8]:
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np

N = 20
x = np.linspace(0, 1, N)

fig = make_subplots(1, 3)
for i in range(1, 4):
    fig.add_trace(go.Scatter(x=x, y=np.random.random(N)), 1, i)

What About Dash?


Learn about how to install Dash at

Everywhere in this page that you see, you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or as px
# fig.add_trace( ... )
# fig.update_layout( ... )

import dash
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash()
app.layout = html.Div([
