Range Slider and Selector in Python
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Basic Range Slider and Range Selectors¶
In [1]:
import plotly.graph_objects as go
import pandas as pd
# Load data
df = pd.read_csv(
"https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv")
df.columns = [col.replace("AAPL.", "") for col in df.columns]
# Create figure
fig = go.Figure()
fig.add_trace(
go.Scatter(x=list(df.Date), y=list(df.High)))
# Set title
fig.update_layout(
title_text="Time series with range slider and selectors"
)
# Add range slider
fig.update_layout(
xaxis=dict(
rangeselector=dict(
buttons=list([
dict(count=1,
label="1m",
step="month",
stepmode="backward"),
dict(count=6,
label="6m",
step="month",
stepmode="backward"),
dict(count=1,
label="YTD",
step="year",
stepmode="todate"),
dict(count=1,
label="1y",
step="year",
stepmode="backward"),
dict(step="all")
])
),
rangeslider=dict(
visible=True
),
type="date"
)
)
fig.show()
Range Slider with Vertically Stacked Subplots¶
In [2]:
import plotly.graph_objects as go
# Create figure
fig = go.Figure()
# Add traces
fig.add_trace(go.Scatter(
x=["2013-01-15", "2013-01-29", "2013-02-26", "2013-04-19", "2013-07-02",
"2013-08-27",
"2013-10-22", "2014-01-20", "2014-05-05", "2014-07-01", "2015-02-09",
"2015-04-13",
"2015-05-13", "2015-06-08", "2015-08-05", "2016-02-25"],
y=["8", "3", "2", "10", "5", "5", "6", "8", "3", "3", "7", "5", "10", "10", "9",
"14"],
name="var0",
text=["8", "3", "2", "10", "5", "5", "6", "8", "3", "3", "7", "5", "10", "10", "9",
"14"],
yaxis="y",
))
fig.add_trace(go.Scatter(
x=["2015-04-13", "2015-05-13", "2015-06-08", "2015-08-05", "2016-02-25"],
y=["53.0", "69.0", "89.0", "41.0", "41.0"],
name="var1",
text=["53.0", "69.0", "89.0", "41.0", "41.0"],
yaxis="y2",
))
fig.add_trace(go.Scatter(
x=["2013-01-29", "2013-02-26", "2013-04-19", "2013-07-02", "2013-08-27",
"2013-10-22",
"2014-01-20", "2014-04-09", "2014-05-05", "2014-07-01", "2014-09-30",
"2015-02-09",
"2015-04-13", "2015-06-08", "2016-02-25"],
y=["9.6", "4.6", "2.7", "8.3", "18", "7.3", "3", "7.5", "1.0", "0.5", "2.8", "9.2",
"13", "5.8", "6.9"],
name="var2",
text=["9.6", "4.6", "2.7", "8.3", "18", "7.3", "3", "7.5", "1.0", "0.5", "2.8",
"9.2",
"13", "5.8", "6.9"],
yaxis="y3",
))
fig.add_trace(go.Scatter(
x=["2013-01-29", "2013-02-26", "2013-04-19", "2013-07-02", "2013-08-27",
"2013-10-22",
"2014-01-20", "2014-04-09", "2014-05-05", "2014-07-01", "2014-09-30",
"2015-02-09",
"2015-04-13", "2015-06-08", "2016-02-25"],
y=["6.9", "7.5", "7.3", "7.3", "6.9", "7.1", "8", "7.8", "7.4", "7.9", "7.9", "7.6",
"7.2", "7.2", "8.0"],
name="var3",
text=["6.9", "7.5", "7.3", "7.3", "6.9", "7.1", "8", "7.8", "7.4", "7.9", "7.9",
"7.6",
"7.2", "7.2", "8.0"],
yaxis="y4",
))
fig.add_trace(go.Scatter(
x=["2013-02-26", "2013-07-02", "2013-09-26", "2013-10-22", "2013-12-04",
"2014-01-02",
"2014-01-20", "2014-05-05", "2014-07-01", "2015-02-09", "2015-05-05"],
y=["290", "1078", "263", "407", "660", "740", "33", "374", "95", "734", "3000"],
name="var4",
text=["290", "1078", "263", "407", "660", "740", "33", "374", "95", "734", "3000"],
yaxis="y5",
))
# style all the traces
fig.update_traces(
hoverinfo="name+x+text",
line={"width": 0.5},
marker={"size": 8},
mode="lines+markers",
showlegend=False
)
# Add annotations
fig.update_layout(
annotations=[
dict(
x="2013-06-01",
y=0,
arrowcolor="rgba(63, 81, 181, 0.2)",
arrowsize=0.3,
ax=0,
ay=30,
text="state1",
xref="x",
yanchor="bottom",
yref="y"
),
dict(
x="2014-09-13",
y=0,
arrowcolor="rgba(76, 175, 80, 0.1)",
arrowsize=0.3,
ax=0,
ay=30,
text="state2",
xref="x",
yanchor="bottom",
yref="y"
)
],
)
# Add shapes
fig.update_layout(
shapes=[
dict(
fillcolor="rgba(63, 81, 181, 0.2)",
line={"width": 0},
type="rect",
x0="2013-01-15",
x1="2013-10-17",
xref="x",
y0=0,
y1=0.95,
yref="paper"
),
dict(
fillcolor="rgba(76, 175, 80, 0.1)",
line={"width": 0},
type="rect",
x0="2013-10-22",
x1="2015-08-05",
xref="x",
y0=0,
y1=0.95,
yref="paper"
)
]
)
# Update axes
fig.update_layout(
xaxis=dict(
autorange=True,
range=["2012-10-31 18:36:37.3129", "2016-05-10 05:23:22.6871"],
rangeslider=dict(
autorange=True,
range=["2012-10-31 18:36:37.3129", "2016-05-10 05:23:22.6871"]
),
type="date"
),
yaxis=dict(
anchor="x",
autorange=True,
domain=[0, 0.2],
linecolor="#673ab7",
mirror=True,
range=[-60.0858369099, 28.4406294707],
showline=True,
side="right",
tickfont={"color": "#673ab7"},
tickmode="auto",
ticks="",
title=dict(
font=dict(
color="#673ab7"
)
),
type="linear",
zeroline=False
),
yaxis2=dict(
anchor="x",
autorange=True,
domain=[0.2, 0.4],
linecolor="#E91E63",
mirror=True,
range=[29.3787777032, 100.621222297],
showline=True,
side="right",
tickfont={"color": "#E91E63"},
tickmode="auto",
ticks="",
title=dict(
font=dict(
color="#E91E63"
)
),
type="linear",
zeroline=False
),
yaxis3=dict(
anchor="x",
autorange=True,
domain=[0.4, 0.6],
linecolor="#795548",
mirror=True,
range=[-3.73690396239, 22.2369039624],
showline=True,
side="right",
tickfont={"color": "#795548"},
tickmode="auto",
ticks="",
title=dict(
text="mg/L",
font=dict(
color="#795548"
)
),
type="linear",
zeroline=False
),
yaxis4=dict(
anchor="x",
autorange=True,
domain=[0.6, 0.8],
linecolor="#607d8b",
mirror=True,
range=[6.63368032236, 8.26631967764],
showline=True,
side="right",
tickfont={"color": "#607d8b"},
tickmode="auto",
ticks="",
title=dict(
text="mmol/L",
font=dict(
color="#607d8b"
)
),
type="linear",
zeroline=False
),
yaxis5=dict(
anchor="x",
autorange=True,
domain=[0.8, 1],
linecolor="#2196F3",
mirror=True,
range=[-685.336803224, 3718.33680322],
showline=True,
side="right",
tickfont={"color": "#2196F3"},
tickmode="auto",
ticks="",
title=dict(
text="mg/Kg",
font=dict(
color="#2196F3"
)
),
type="linear",
zeroline=False
)
)
# Update layout
fig.update_layout(
dragmode="zoom",
hovermode="x",
legend=dict(traceorder="reversed"),
height=600,
template="plotly_white",
margin=dict(
t=100,
b=100
),
)
fig.show()
Reference¶
See https://plotly.com/python/reference/layout/xaxis/#layout-xaxis-rangeselector
and https://plotly.com/python/reference/layout/xaxis/#layout-xaxis-rangeslider
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(debug=True, use_reloader=False) # Turn off reloader if inside Jupyter
