![]() ![]() ![]() What I've tried before is the basemap module. I haven't tried it yet but I guess you can use shape files to have more accurate maps if you want to. P.patches(county_xs, county_ys, fill_color=county_colors, fill_alpha=0.7, You'll learn how to visualize your data, customize and organize your visualizations, and add interactivity. To use Google maps in Bokeh, we will use the gmap () function of the plotting class. P = figure(title="US Unemployment 2009", toolbar_location="left", Interactive Data Visualization in Python With Bokeh Real Python This Python tutorial will get you up and running with Bokeh, using examples and a real-world dataset. Bokeh can be used to display Google maps. Output_file("choropleth.html", title="choropleth.py example") Project description bokeh-plot Installation: pip install bokeh-plot Usage: To load this extension in jupyter notebook: loadext bokehplot The following syntax is supported: plot ( 1,4,9) x is automatic plot ( 1,4,9, '.-') line and dots plot ( 1,2,3, 1,4,9) x and y plot ( 1,2,3, 1,4,9, '. with either the Matplotlib, Plotly, or Bokeh plotting extensions. from bokeh.sampledata import us_states, us_counties, unemploymentĬounty_xs= for code in us_counties if us_counties not in ]Ĭounty_ys= for code in us_counties if us_counties not in ]Ĭolors = When viewed statically, the plots will not update fully when you zoom and pan. Plotting points like these are called "Scatter Markers" in Bokeh. P.circle(x, y, size=8, color='navy', alpha=1) # Now group these values together into a lists of x (longitude) and y (latitude) P.patches(state_xs, state_ys, fill_alpha=0.0, ![]() Toolbar_location="left", plot_width=1100, plot_height=700) P = figure(title="Plotting Points Example: The 5 Largest Cities in Texas", # separate latitude and longitude points for the borders I recognize that this thread is old, but I haven't found a super clear answer anywhere else, so hopefully this will help. ![]()
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