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Simple Slider Control¶

Sliders can be used in Plotly to change the data displayed or style of a plot.

In [1]:

import plotly.graph_objects as go
import numpy as np

# Create figure
fig = go.Figure()

# Add traces, one for each slider step
for step in np.arange(0, 5, 0.1):
    fig.add_trace(
        go.Scatter(
            visible=False,
            line=dict(color="#00CED1", width=6),
            name="𝜈 = " + str(step),
            x=np.arange(0, 10, 0.01),
            y=np.sin(step * np.arange(0, 10, 0.01))))

# Make 10th trace visible
fig.data[10].visible = True

# Create and add slider
steps = []
for i in range(len(fig.data)):
    step = dict(
        method="update",
        args=[{"visible": [False] * len(fig.data)},
              {"title": "Slider switched to step: " + str(i)}],  # layout attribute
    )
    step["args"][0]["visible"][i] = True  # Toggle i'th trace to "visible"
    steps.append(step)

sliders = [dict(
    active=10,
    currentvalue={"prefix": "Frequency: "},
    pad={"t": 50},
    steps=steps
)]

fig.update_layout(
    sliders=sliders
)

fig.show()

Methods¶

The method determines which plotly.js function will be used to update the chart. Plotly can use several updatemenu methods to add the slider:

"update": modify data and layout attributes (as above)
"restyle": modify data attributes
"relayout": modify layout attributes
"animate": start or pause an animation

Sliders in Plotly Express¶

Plotly Express provide sliders, but with implicit animation using the "animate" method described above. The animation play button can be omitted by removing updatemenus in the layout:

In [2]:

import plotly.express as px

df = px.data.gapminder()
fig = px.scatter(df, x="gdpPercap", y="lifeExp", animation_frame="year", animation_group="country",
           size="pop", color="continent", hover_name="country",
           log_x=True, size_max=55, range_x=[100,100000], range_y=[25,90])

fig["layout"].pop("updatemenus") # optional, drop animation buttons
fig.show()

Reference¶

Check out https://plotly.com/python/reference/layout/updatemenus/ for more information!

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

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