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Sliders can be used in Plotly to change the data displayed or style of a plot.
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()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
The "update" method should be used when modifying the data and layout sections of the graph.
This example demonstrates how to update the data displayed while simultaneously updating layout attributes such as the annotations.
import plotly.graph_objects as go
import numpy as np
# Create figure
fig = go.Figure()
min_val = 0
max_val = 0
# Add traces, one for each slider step
start = -1
for step in np.arange(start, 5, 0.1):
x_vec=np.arange(0, 10, 0.01) #np.arange(start, 1, 0.1)
y_vec=np.cos(step * np.arange(0, 10, 0.01))
fig.add_trace(
go.Scatter(
visible=False,
line=dict(color="#00CED1", width=4),
name="𝜈 = " + str(step),
x=x_vec,
y=y_vec))
if step == start:
min_val = np.min(y_vec)
max_val = np.max(y_vec)
else:
tmp_min = np.min(y_vec)
tmp_max = np.max(y_vec)
min_val = min(min_val, tmp_min)
max_val = max(max_val, tmp_max)
# Make 10th trace visible
fig.data[10].visible = True
# Add Annotations
annotation_info = [dict(x=1,
y=0,
xref="paper", yref="paper",
text="Min value:<br> %.4f" % min_val,
ax=0, ay=40,
showarrow=False,
xanchor="left", yanchor="bottom"),
dict(x=1,
y=1,
xref="paper", yref="paper",
text="Max value:<br> %.4f" % max_val,
ax=0, ay=-40,
showarrow=False,
xanchor="left", yanchor="top")
]
# Create and add slider
steps = []
for i in range(len(fig.data)):
step = dict(
method="update",
label=str(i),
args=[{"visible": [False] * len(fig.data)},
{"title": "Slider switched to step: " + str(i), # layout attribute
"annotations": annotation_info}], # layout attribute
)
step["args"][0]["visible"][i] = True # Toggle i'th trace to "visible"
steps.append(step)
sliders = [dict(
active=10,
currentvalue={"prefix": "Slider value: "},
pad={"t": 30},
steps=steps
)]
fig.update_layout(
sliders=sliders
)
fig.show()This example demonstrates how sliders can be employed to data filtering. Here we show companies, represented with bars, when values of the outcome variable are above the threshold. The change in trace attributes is associated with the change in layout attribute. The title is updated when the value of the threshold is more than zero.
import plotly.graph_objects as go
import numpy as np
import math
companies = ['Company A','Company B','Company C','Company D','Company E','Company F','Company G','Company H']
outcomes = [7.8, 12.3, 20.4, 8.9, -5.7, -16.3, 10.2, -1.5]
# Create figure
fig = go.Figure()
# Add trace
fig.add_trace(go.Bar(
x=companies,
y=outcomes,
marker=dict(color = "green")
))
min_outcome = math.ceil(min(outcomes))
max_outcome = math.ceil(max(outcomes))
titles = ["Companies and outcomes", "Companies with positive outcomes"]
steps = [dict(method="update",
args=[{'x': [[c for c, o in zip(companies,outcomes) if o>k]], #trace attributes that are updated by each slider step
'y': [[y for y in outcomes if y>k]]}, #trace attributes that are updated by each slider step
{'title': titles[1] if k>0 else titles[0]}], #layout attributes that are updated
label=f"{k}") for k in range(min_outcome, max_outcome)]
sliders = [dict(
active=0,
currentvalue={"prefix": "threshold: "},
steps=steps
)]
fig.update_layout(title=titles[0],
yaxis_title="outcome [mil.]",
sliders=sliders)
fig.show()The "relayout" method should be used when modifying layout attributes.
This example demonstrates how to update which groups are in clusters.
import plotly.graph_objects as go
import numpy as np
# Create figure
fig = go.Figure()
x0 = np.random.normal(2, 0.2, 400)
y0 = np.random.normal(2, 0.3, 400)
x1 = np.random.normal(3, 0.1, 600)
y1 = np.random.normal(6, 0.3, 400)
x2 = np.random.normal(4, 0.4, 200)
y2 = np.random.normal(4, 0.5, 200)
# Add traces
fig.add_trace(
go.Scatter(
x=x0,
y=y0,
mode="markers",
marker=dict(color="DarkOrange")
)
)
fig.add_trace(
go.Scatter(
x=x1,
y=y1,
mode="markers",
marker=dict(color="Crimson")
)
)
fig.add_trace(
go.Scatter(
x=x2,
y=y2,
mode="markers",
marker=dict(color="RebeccaPurple")
)
)
initial_cluster = [dict(type="circle",
xref="x", yref="y",
x0=min(x0), y0=min(y0),
x1=max(x0), y1=max(y0),
line=dict(color="DarkOrange"))]
cluster2 = [dict(type="circle",
xref="x", yref="y",
x0=min(x0), y0=min(y0),
x1=max(x1), y1=max(y1),
line=dict(color="Crimson"))]
cluster3 = [dict(type="circle",
xref="x", yref="y",
x0=min(x0), y0=min(y0),
x1=max(x2), y1=max(y1),
line=dict(color="RebeccaPurple"))]
clusters = [[], initial_cluster, cluster2, cluster3]
# Create and add slider
steps = [dict(method="relayout",
args=["shapes", clusters[k]],
label=f"{k}") for k in range(len(clusters))]
sliders = [dict(
active=0,
currentvalue={"prefix": "Groups in cluster: "},
pad={"t": 50},
steps=steps
)]
fig.update_layout(
title_text="Groups",
showlegend=False,
sliders=sliders
)
fig.show()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:
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()Check out https://plotly.com/python/reference/layout/updatemenus/ for more information!