dot-plots.md

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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.

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

importplotly.expressaspximportpandasaspdschools= ["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()

importplotly.graph_objectsasgoschools= ["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

importplotly.graph_objectsasgocountry= ['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!