Introduction
Bokeh is a powerful data visualization library in Python that allows you to create interactive plots and visualizations. It is particularly useful for creating interactive web-based visualizations, as it can generate HTML, JavaScript, and CSS code that can be embedded in a web page.
In this tutorial, we will explore how to plot glyphs over a Google Map using the Bokeh library in Python. We will start by installing Bokeh and creating a basic plot. Then, we will customize the plot and add interactivity to it. Finally, we will learn how to plot glyphs over a Google Map.
Installing Bokeh
Before we can start using Bokeh, we need to install it. Bokeh can be installed using pip, the Python package installer. Open your terminal or command prompt and run the following command:
pip install bokeh
This will install the latest version of Bokeh and its dependencies.
Creating a Basic Bokeh Plot
Once Bokeh is installed, we can start creating our first plot. Open your favorite Python editor or IDE and create a new Python script. Import the necessary modules by adding the following lines of code at the beginning of your script:
from bokeh.plotting import figure, show
Next, we need to create a figure object, which will be the container for our plot. Add the following code to create a basic figure:
plot = figure()
Now, we can add some data to our plot. Bokeh supports various types of glyphs, such as lines, circles, squares, and more. For this example, let’s add a line glyph. Add the following code to create a line glyph:
plot.line([1, 2, 3, 4, 5], [6, 7, 2, 4, 5])
Finally, we can display our plot by calling the show() function. Add the following code at the end of your script:
show(plot)
Save your script and run it. You should see a new browser window open with your plot displayed.
Customizing the Plot
Now that we have created a basic plot, let’s explore how to customize it. Bokeh provides a wide range of options for customizing the appearance of your plot.
One of the most common customizations is changing the color of the glyphs. Bokeh allows you to specify colors using various formats, such as named colors, hexadecimal codes, and RGB values. To change the color of the line glyph in our plot, add the following code before calling the show() function:
plot.line([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], line_color=»red»)
You can also customize the line width by adding the line_width parameter:
plot.line([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], line_color=»red», line_width=2)
In addition to changing the appearance of the glyphs, you can also customize the axes, titles, and legends of your plot. Bokeh provides various options for controlling the appearance of these elements. For example, you can change the title of the plot by adding the following code:
plot.title.text = «My Plot»
You can also customize the appearance of the axes by accessing the xaxis and yaxis attributes of the plot object. For example, to change the label of the x-axis, add the following code:
plot.xaxis.axis_label = «X-axis»
These are just a few examples of the many customization options available in Bokeh. I encourage you to explore the Bokeh documentation for more information on how to customize your plots.
Adding Interactivity to the Plot
One of the key features of Bokeh is its ability to create interactive plots. Bokeh provides various tools and widgets that allow users to interact with the plot and explore the data.
To add interactivity to our plot, we need to create a ColumnDataSource object. This object holds the data that will be displayed in the plot and allows us to update the plot dynamically. Add the following code to create a ColumnDataSource object:
from bokeh.models import ColumnDataSource
source = ColumnDataSource(data=dict(x=[1, 2, 3, 4, 5], y=[6, 7, 2, 4, 5]))
Next, we can update our plot to use the data from the ColumnDataSource object. Replace the line glyph code with the following code:
plot.line(‘x’, ‘y’, source=source)
Now, let’s add a hover tool to our plot. The hover tool displays additional information about the data points when the user hovers over them with the mouse. Add the following code to create a hover tool:
from bokeh.models import HoverTool
hover = HoverTool(tooltips=[(«x», «@x»), («y», «@y»)])
plot.add_tools(hover)
Finally, we can add some widgets to our plot. Widgets are interactive controls that allow users to change the data or parameters of the plot. Bokeh provides various types of widgets, such as sliders, dropdown menus, and buttons.
For this example, let’s add a slider widget that allows users to change the y-values of the data. Add the following code to create a slider widget:
from bokeh.models import Slider
slider = Slider(start=0, end=10, value=5, step=1, title=»Y-value»)
Next, we need to define a callback function that will be called whenever the value of the slider changes. Add the following code to define a callback function:
from bokeh.models import CustomJS
callback = CustomJS(args=dict(source=source, slider=slider), code=»»»
var data = source.data;
var value = slider.value;
var y = data[‘y’];
for (var i = 0; i < y.length; i++) {
y[i] = value;
}
source.change.emit();
""")
Finally, we can associate the callback function with the slider widget by adding the following code:
slider.js_on_change(‘value’, callback)
Save your script and run it. You should see a new browser window open with your plot displayed. Try moving the slider and hovering over the data points to see the interactivity in action.
Plotting Glyphs over a Google Map
Now that we have learned the basics of creating plots and adding interactivity using Bokeh, let’s explore how to plot glyphs over a Google Map.
To plot glyphs over a Google Map, we need to use the Google Maps API and the Bokeh library. The Google Maps API allows us to retrieve map tiles and overlay them with our glyphs. Bokeh provides the TileSource class, which allows us to use map tiles from various tile providers, including Google Maps.
To get started, we need to import the necessary modules and create a figure object. Add the following code at the beginning of your script:
from bokeh.tile_providers import get_provider, Vendors
from bokeh.models import Range1d
plot = figure(x_range=Range1d(), y_range=Range1d(), width=800, height=600)
Next, we need to specify the tile provider we want to use. Bokeh provides various tile providers, such as OpenStreetMap, Stamen, and CartoDB. For this example, let’s use the Google Maps tile provider. Add the following code to specify the tile provider:
tile_provider = get_provider(Vendors.GOOGLE_MAPS)
Now, we can add the map tiles to our plot. Add the following code to add the map tiles:
plot.add_tile(tile_provider)
Next, let’s add some glyphs to our plot. For this example, let’s add a circle glyph at a specific latitude and longitude. Add the following code to add the circle glyph:
plot.circle(-122.4194, 37.7749, size=10, fill_color=»red», fill_alpha=0.5)
Finally, we can display our plot by calling the show() function. Add the following code at the end of your script:
show(plot)
Save your script and run it. You should see a new browser window open with a Google Map displayed. The circle glyph should be plotted at the specified latitude and longitude.
Conclusion
In this tutorial, we have learned how to plot glyphs over a Google Map using the Bokeh library in Python. We started by installing Bokeh and creating a basic plot. Then, we explored how to customize the plot and add interactivity to it. Finally, we learned how to plot glyphs over a Google Map.
Bokeh is a powerful library that allows you to create interactive plots and visualizations. It provides a wide range of options for customizing the appearance of your plots and adding interactivity to them. By combining Bokeh with the Google Maps API, you can create stunning visualizations that overlay your data on top of a map.
I hope you found this tutorial helpful. Happy plotting!
