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  plotly:
    description: How to make dot plots in Python with Plotly.
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    name: Dot Plots
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---

#### Basic Dot Plot

Dot plots (also known as [Cleveland dot plots](<https://en.wikipedia.org/wiki/Dot_plot_(statistics)>)) are [scatter plots](https://plotly.com/python/line-and-scatter/) 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](/python/bar-charts/), 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](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on a variety of types of data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).

```python
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()
```

```python
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()
```

```python
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

```python
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!