Filled Area Plots in Python

How to make filled area plots in Python with Plotly.

New to Plotly?

Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

This example shows how to fill the area enclosed by traces.

Filled area plot with plotly.express¶

Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures.

px.area creates a stacked area plot. Each filled area corresponds to one value of the column given by the line_group parameter.

In [1]:

import plotly.express as px
df = px.data.gapminder()
fig = px.area(df, x="year", y="pop", color="continent", line_group="country")
fig.show()

Filled area plot in Dash¶

Dash is the best way to build analytical apps in Python using Plotly figures. To run the app below, run pip install dash, click "Download" to get the code and run python app.py.

Get started with the official Dash docs and learn how to effortlessly style & deploy apps like this with Dash Enterprise.

Out[2]:

Sign up for Dash Club → Free cheat sheets plus updates from Chris Parmer and Adam Schroeder delivered to your inbox every two months. Includes tips and tricks, community apps, and deep dives into the Dash architecture. Join now.

Pattern Fills¶

New in v5.7

Area charts afford the use of patterns (also known as hatching or texture) in addition to color:

In [3]:

import plotly.express as px
df = px.data.medals_long()

fig = px.area(df, x="medal", y="count", color="nation",
             pattern_shape="nation", pattern_shape_sequence=[".", "x", "+"])
fig.show()

Filled area chart with plotly.graph_objects¶

Basic Overlaid Area Chart¶

In [4]:

import plotly.graph_objects as go

fig = go.Figure()
fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[0, 2, 3, 5], fill='tozeroy')) # fill down to xaxis
fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[3, 5, 1, 7], fill='tonexty')) # fill to trace0 y

fig.show()

Overlaid Area Chart Without Boundary Lines¶

In [5]:

import plotly.graph_objects as go

fig = go.Figure()
fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[0, 2, 3, 5], fill='tozeroy',
                    mode='none' # override default markers+lines
                    ))
fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[3, 5, 1, 7], fill='tonexty',
                    mode= 'none'))

fig.show()

Interior Filling for Area Chart¶

In [6]:

import plotly.graph_objects as go

fig = go.Figure()
fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[3, 4, 8, 3],
    fill=None,
    mode='lines',
    line_color='indigo',
    ))
fig.add_trace(go.Scatter(
    x=[1, 2, 3, 4],
    y=[1, 6, 2, 6],
    fill='tonexty', # fill area between trace0 and trace1
    mode='lines', line_color='indigo'))

fig.show()

Gradient Fill¶

New in 5.20

Scatter traces with a fill support a fillgradient, which is a dict of options that defines the gradient. Use fillgradient.colorscale to define the colorscale for the gradient and choose a type to define the orientation of the gradient ('horizontal', 'vertical' or 'radial').

In the following example, we've defined a horizontal fillgradient with a colorscale of three colors.

In [7]:

import plotly.graph_objects as go

fig = go.Figure(
    [
        go.Scatter(
            x=[1, 2, 3, 4],
            y=[3, 4, 8, 3],
            fill=None,
            mode="lines",
            line_color="darkblue",
        ),
        go.Scatter(
            x=[1, 2, 3, 4],
            y=[1, 6, 2, 6],
            fill="tonexty",
            mode="lines",
            line_color="darkblue",
            fillgradient=dict(
                type="horizontal",
                colorscale=[(0.0, "darkblue"), (0.5, "royalblue"), (1.0, "cyan")],
            ),
        ),
    ]
)

fig.show()

Stacked Area Chart¶

The stackgroup parameter is used to add the y values of the different traces in the same group. Traces in the same group fill up to the next trace of the group.

In [8]:

import plotly.graph_objects as go

x=['Winter', 'Spring', 'Summer', 'Fall']

fig = go.Figure()
fig.add_trace(go.Scatter(
    x=x, y=[40, 60, 40, 10],
    hoverinfo='x+y',
    mode='lines',
    line=dict(width=0.5, color='rgb(131, 90, 241)'),
    stackgroup='one' # define stack group
))
fig.add_trace(go.Scatter(
    x=x, y=[20, 10, 10, 60],
    hoverinfo='x+y',
    mode='lines',
    line=dict(width=0.5, color='rgb(111, 231, 219)'),
    stackgroup='one'
))
fig.add_trace(go.Scatter(
    x=x, y=[40, 30, 50, 30],
    hoverinfo='x+y',
    mode='lines',
    line=dict(width=0.5, color='rgb(184, 247, 212)'),
    stackgroup='one'
))

fig.update_layout(yaxis_range=(0, 100))
fig.show()

Stacked Area Chart with Normalized Values¶

In [9]:

import plotly.graph_objects as go

x=['Winter', 'Spring', 'Summer', 'Fall']
fig = go.Figure()

fig.add_trace(go.Scatter(
    x=x, y=[40, 20, 30, 40],
    mode='lines',
    line=dict(width=0.5, color='rgb(184, 247, 212)'),
    stackgroup='one',
    groupnorm='percent' # sets the normalization for the sum of the stackgroup
))
fig.add_trace(go.Scatter(
    x=x, y=[50, 70, 40, 60],
    mode='lines',
    line=dict(width=0.5, color='rgb(111, 231, 219)'),
    stackgroup='one'
))
fig.add_trace(go.Scatter(
    x=x, y=[70, 80, 60, 70],
    mode='lines',
    line=dict(width=0.5, color='rgb(127, 166, 238)'),
    stackgroup='one'
))
fig.add_trace(go.Scatter(
    x=x, y=[100, 100, 100, 100],
    mode='lines',
    line=dict(width=0.5, color='rgb(131, 90, 241)'),
    stackgroup='one'
))

fig.update_layout(
    showlegend=True,
    xaxis_type='category',
    yaxis=dict(
        type='linear',
        range=[1, 100],
        ticksuffix='%'))

fig.show()

Select Hover Points¶

In [10]:

import plotly.graph_objects as go

fig = go.Figure()
fig.add_trace(go.Scatter(x=[0,0.5,1,1.5,2], y=[0,1,2,1,0],
                    fill='toself', fillcolor='darkviolet',
                    hoveron = 'points+fills', # select where hover is active
                    line_color='darkviolet',
                    text="Points + Fills",
                    hoverinfo = 'text+x+y'))

fig.add_trace(go.Scatter(x=[3,3.5,4,4.5,5], y=[0,1,2,1,0],
                    fill='toself', fillcolor = 'violet',
                    hoveron='points',
                    line_color='violet',
                    text="Points only",
                    hoverinfo='text+x+y'))

fig.update_layout(
    title = "hover on <i>points</i> or <i>fill</i>",
    xaxis_range = [0,5.2],
    yaxis_range = [0,3]
)

fig.show()

Reference¶

See https://plotly.com/python/reference/scatter/#scatter-line and https://plotly.com/python/reference/scatter/#scatter-fill for more information and attribute options!

What About Dash?¶

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), 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 plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

from dash import Dash, dcc, html

app = Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run_server(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter