Dot Plots in Python

How to make dot plots 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.

Basic Dot Plot¶

Dot plots (also known as Cleveland dot plots) are scatter plots with one categorical axis and one continuous axis. They can be used to show changes between two (or more) points in time or between two (or more) conditions. Compared to a bar chart, dot plots can be less cluttered and allow for an easier comparison between conditions.

For the same data, we show below how to create a dot plot using either px.scatter or go.Scatter.

Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures.

In [1]:

import plotly.express as px
df = px.data.medals_long()

fig = px.scatter(df, y="nation", x="count", color="medal", symbol="medal")
fig.update_traces(marker_size=10)
fig.show()

In [2]:

import plotly.express as px
import pandas as pd

schools = ["Brown", "NYU", "Notre Dame", "Cornell", "Tufts", "Yale",
           "Dartmouth", "Chicago", "Columbia", "Duke", "Georgetown",
           "Princeton", "U.Penn", "Stanford", "MIT", "Harvard"]
n_schools = len(schools)

women_salary = [72, 67, 73, 80, 76, 79, 84, 78, 86, 93, 94, 90, 92, 96, 94, 112]
men_salary = [92, 94, 100, 107, 112, 114, 114, 118, 119, 124, 131, 137, 141, 151, 152, 165]

df = pd.DataFrame(dict(school=schools*2, salary=men_salary + women_salary,
                       gender=["Men"]*n_schools + ["Women"]*n_schools))

# Use column names of df for the different parameters x, y, color, ...
fig = px.scatter(df, x="salary", y="school", color="gender",
                 title="Gender Earnings Disparity",
                 labels={"salary":"Annual Salary (in thousands)"} # customize axis label
                )

fig.show()

In [3]:

import plotly.graph_objects as go

schools = ["Brown", "NYU", "Notre Dame", "Cornell", "Tufts", "Yale",
           "Dartmouth", "Chicago", "Columbia", "Duke", "Georgetown",
           "Princeton", "U.Penn", "Stanford", "MIT", "Harvard"]

fig = go.Figure()
fig.add_trace(go.Scatter(
    x=[72, 67, 73, 80, 76, 79, 84, 78, 86, 93, 94, 90, 92, 96, 94, 112],
    y=schools,
    marker=dict(color="crimson", size=12),
    mode="markers",
    name="Women",
))

fig.add_trace(go.Scatter(
    x=[92, 94, 100, 107, 112, 114, 114, 118, 119, 124, 131, 137, 141, 151, 152, 165],
    y=schools,
    marker=dict(color="gold", size=12),
    mode="markers",
    name="Men",
))

fig.update_layout(
    title=dict(
        text="Gender Earnings Disparity"
    ),
    xaxis=dict(
        title=dict(
            text="Annual Salary (in thousands)"
        )
    ),
    yaxis=dict(
        title=dict(
            text="School"
        )
    ),
)

fig.show()

Styled Categorical Dot Plot¶

In [4]:

import plotly.graph_objects as go

country = ['Switzerland (2011)', 'Chile (2013)', 'Japan (2014)',
           'United States (2012)', 'Slovenia (2014)', 'Canada (2011)',
           'Poland (2010)', 'Estonia (2015)', 'Luxembourg (2013)', 'Portugal (2011)']
voting_pop = [40, 45.7, 52, 53.6, 54.1, 54.2, 54.5, 54.7, 55.1, 56.6]
reg_voters = [49.1, 42, 52.7, 84.3, 51.7, 61.1, 55.3, 64.2, 91.1, 58.9]

fig = go.Figure()

fig.add_trace(go.Scatter(
    x=voting_pop,
    y=country,
    name='Percent of estimated voting age population',
    marker=dict(
        color='rgba(156, 165, 196, 0.95)',
        line_color='rgba(156, 165, 196, 1.0)',
    )
))
fig.add_trace(go.Scatter(
    x=reg_voters, y=country,
    name='Percent of estimated registered voters',
    marker=dict(
        color='rgba(204, 204, 204, 0.95)',
        line_color='rgba(217, 217, 217, 1.0)'
    )
))

fig.update_traces(mode='markers', marker=dict(line_width=1, symbol='circle', size=16))

fig.update_layout(
    title=dict(text="Votes cast for ten lowest voting age population in OECD countries"),
    xaxis=dict(
        showgrid=False,
        showline=True,
        linecolor='rgb(102, 102, 102)',
        tickfont_color='rgb(102, 102, 102)',
        showticklabels=True,
        dtick=10,
        ticks='outside',
        tickcolor='rgb(102, 102, 102)',
    ),
    margin=dict(l=140, r=40, b=50, t=80),
    legend=dict(
        font_size=10,
        yanchor='middle',
        xanchor='right',
    ),
    width=800,
    height=600,
    paper_bgcolor='white',
    plot_bgcolor='white',
    hovermode='closest',
)
fig.show()

Reference¶

See https://plotly.com/python/reference/scatter/ for more information and chart 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_server(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter