Continuous Error Bands in Python

Add continuous error bands to charts 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.

Continuous error bands are a graphical representation of error or uncertainty as a shaded region around a main trace, rather than as discrete whisker-like error bars. They can be implemented in a manner similar to filled area plots using scatter traces with the fill attribute.

Filling within a single trace

In this example we show how to construct a trace that goes from low to high X values along the upper Y edge of a region, and then from high to low X values along the lower Y edge of the region. This trace is then 'self-filled' using fill='toself'.

In [1]:
import plotly.graph_objs as go

x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
y = [1, 2, 7, 4, 5, 6, 7, 8, 9, 10]
y_upper = [2, 3, 8, 5, 6, 7, 8, 9, 10, 11]
y_lower = [0, 1, 5, 3, 4, 5, 6, 7, 8, 9]


fig = go.Figure([
    go.Scatter(
        x=x,
        y=y,
        line=dict(color='rgb(0,100,80)'),
        mode='lines'
    ),
    go.Scatter(
        x=x+x[::-1], # x, then x reversed
        y=y_upper+y_lower[::-1], # upper, then lower reversed
        fill='toself',
        fillcolor='rgba(0,100,80,0.2)',
        line=dict(color='rgba(255,255,255,0)'),
        hoverinfo="skip",
        showlegend=False
    )
])
fig.show()

Filling between two traces

In this example we show how to construct the bounds of the band using two traces, with the lower trace using fill='tonexty' to fill an area up to the upper trace.

In [2]:
import plotly.graph_objs as go
import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/wind_speed_laurel_nebraska.csv')

fig = go.Figure([
    go.Scatter(
        name='Measurement',
        x=df['Time'],
        y=df['10 Min Sampled Avg'],
        mode='lines',
        line=dict(color='rgb(31, 119, 180)'),
    ),
    go.Scatter(
        name='Upper Bound',
        x=df['Time'],
        y=df['10 Min Sampled Avg']+df['10 Min Std Dev'],
        mode='lines',
        marker=dict(color="#444"),
        line=dict(width=0),
        showlegend=False
    ),
    go.Scatter(
        name='Lower Bound',
        x=df['Time'],
        y=df['10 Min Sampled Avg']-df['10 Min Std Dev'],
        marker=dict(color="#444"),
        line=dict(width=0),
        mode='lines',
        fillcolor='rgba(68, 68, 68, 0.3)',
        fill='tonexty',
        showlegend=False
    )
])
fig.update_layout(
    yaxis_title='Wind speed (m/s)',
    title='Continuous, variable value error bars',
    hovermode="x"
)
fig.show()

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( ... )

import dash
import dash_core_components as dcc
import dash_html_components as html

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

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