Dumbbell Plots in Python

How to create dumbbell plots in Python with Plotly.

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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.

Basic Dumbbell Plot¶

Dumbbell plots are useful for demonstrating change between two sets of data points, for example, the population change for a selection of countries for two different years.

In this example, we compare life expectancy in 1952 with life expectancy in 2002 for countries in Europe.

In [1]:

import plotly.graph_objects as go
from plotly import data

import pandas as pd

df = data.gapminder()
df = df.loc[(df.continent == "Europe") & (df.year.isin([1952, 2002]))]

countries = (
    df.loc[(df.continent == "Europe") & (df.year.isin([2002]))]
    .sort_values(by=["lifeExp"], ascending=True)["country"]
    .unique()
)

data = {"line_x": [], "line_y": [], "1952": [], "2002": [], "colors": [], "years": [], "countries": []}

for country in countries:
    data["1952"].extend([df.loc[(df.year == 1952) & (df.country == country)]["lifeExp"].values[0]])
    data["2002"].extend([df.loc[(df.year == 2002) & (df.country == country)]["lifeExp"].values[0]])
    data["line_x"].extend(
        [
            df.loc[(df.year == 1952) & (df.country == country)]["lifeExp"].values[0],
            df.loc[(df.year == 2002) & (df.country == country)]["lifeExp"].values[0],
            None,
        ]
    )
    data["line_y"].extend([country, country, None]),

fig = go.Figure(
    data=[
        go.Scatter(
            x=data["line_x"],
            y=data["line_y"],
            mode="lines",
            showlegend=False,
            marker=dict(
                color="grey"
            )
        ),
        go.Scatter(
            x=data["1952"],
            y=countries,
            mode="markers",
            name="1952",
            marker=dict(
                color="green",
                size=10
            )
            
        ),
        go.Scatter(
            x=data["2002"],
            y=countries,
            mode="markers",
            name="2002",
            marker=dict(
                color="blue",
                size=10
            )   
        ),
    ]
)

fig.update_layout(
    title="Life Expectancy in Europe: 1952 and 2002",
    height=1000,
    legend_itemclick=False
)

fig.show()

Dumbbell Plot with Arrow Markers¶

Note: The arrow, angleref, and standoff properties used on the marker in this example are new in 5.11

In this example, we add arrow markers to the plot. The first trace adds the lines connecting the data points and arrow markers. The second trace adds circle markers. On the first trace, we use standoff=8 to position the arrow marker back from the data point. For the arrow marker to point directly at the circle marker, this value should be half the circle marker size, which is hardcoded to 16 here.

In [2]:

import pandas as pd
import plotly.graph_objects as go
from plotly import data

df = data.gapminder()
df = df.loc[(df.continent == "Europe") & (df.year.isin([1952, 2002]))]

countries = (
    df.loc[(df.continent == "Europe") & (df.year.isin([2002]))]
    .sort_values(by=["lifeExp"], ascending=True)["country"]
    .unique()
)

data = {"line_x": [], "line_y": [], "1952": [], "2002": [], "colors": [], "years": [], "countries": []}

for country in countries:
    data["1952"].extend([df.loc[(df.year == 1952) & (df.country == country)]["lifeExp"].values[0]])
    data["2002"].extend([df.loc[(df.year == 2002) & (df.country == country)]["lifeExp"].values[0]])
    data["line_x"].extend(
        [
            df.loc[(df.year == 1952) & (df.country == country)]["lifeExp"].values[0],
            df.loc[(df.year == 2002) & (df.country == country)]["lifeExp"].values[0],
            None,
        ]
    )
    data["line_y"].extend([country, country, None]),

fig = go.Figure(
    data=[
        go.Scatter(
            x=data["line_x"],
            y=data["line_y"],
            mode="markers+lines",
            showlegend=False,
            marker=dict(
                symbol="arrow", 
                color="black", 
                size=16, 
                angleref="previous", 
                standoff=8
            )
        ),
        go.Scatter(
            x=data["1952"],
            y=countries, 
            name="1952",
            mode="markers",
            marker=dict(
                color="silver",
                size=16,
            )
        ),
        go.Scatter(
            x=data["2002"],
            y=countries,
            name="2002",
            mode="markers",
            marker=dict(
                color="lightskyblue",
                size=16,
            ),
        ),
    ]
)

fig.update_layout(
    title="Life Expectancy in Europe: 1952 and 2002",
    height=1000,
    legend_itemclick=False
)


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( ... )

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