Add several scatter glyphs to one plot figure python bokeh

When working with data visualization in Python, the Bokeh library is a powerful tool that allows you to create interactive and visually appealing plots. One common task is to add scatter glyphs to a single plot figure. In this article, we will explore three different ways to achieve this using Bokeh.

Option 1: Using the scatter() method

The first option is to use the scatter() method provided by the Bokeh library. This method allows you to add scatter glyphs to a plot figure by specifying the x and y coordinates, as well as other optional parameters such as the size and color of the markers.


from bokeh.plotting import figure, show

# Create a new plot figure
p = figure()

# Add scatter glyphs to the plot
p.scatter([1, 2, 3], [4, 5, 6], size=10, color='blue')
p.scatter([2, 3, 4], [5, 6, 7], size=10, color='red')

# Show the plot
show(p)

This code snippet creates a new plot figure, adds two sets of scatter glyphs with different coordinates, sizes, and colors, and finally displays the plot using the show() function. This approach is straightforward and suitable for simple scatter plots.

Option 2: Using the ColumnDataSource

The second option involves using the ColumnDataSource class provided by Bokeh. This class allows you to create a data source that can be shared between multiple glyphs, making it easier to manage and update the data.


from bokeh.plotting import figure, show
from bokeh.models import ColumnDataSource

# Create a new plot figure
p = figure()

# Create a ColumnDataSource with the data
source = ColumnDataSource(data={
    'x': [1, 2, 3, 2, 3, 4],
    'y': [4, 5, 6, 5, 6, 7],
    'color': ['blue', 'blue', 'blue', 'red', 'red', 'red']
})

# Add scatter glyphs to the plot using the ColumnDataSource
p.scatter('x', 'y', size=10, color='color', source=source)

# Show the plot
show(p)

In this example, we create a ColumnDataSource object that contains the x and y coordinates, as well as the color for each scatter glyph. By passing the ColumnDataSource to the scatter() method, we can easily specify the column names as the arguments for the x, y, and color parameters. This approach is useful when you have a large dataset or need to update the plot dynamically.

Option 3: Using the add_glyph() method

The third option involves using the add_glyph() method provided by the GlyphRenderer class in Bokeh. This method allows you to add glyphs to a plot figure directly, without the need for a ColumnDataSource.


from bokeh.plotting import figure, show
from bokeh.models import Circle

# Create a new plot figure
p = figure()

# Add scatter glyphs to the plot using add_glyph()
p.add_glyph(None, Circle(x=[1, 2, 3], y=[4, 5, 6], size=10, fill_color='blue'))
p.add_glyph(None, Circle(x=[2, 3, 4], y=[5, 6, 7], size=10, fill_color='red'))

# Show the plot
show(p)

In this example, we create Circle objects with the desired x and y coordinates, size, and fill color. Then, we use the add_glyph() method to add the glyphs to the plot figure. This approach is useful when you want more control over the glyph properties or need to add glyphs of different types to the same plot.

After exploring these three options, it is clear that the best approach depends on the specific requirements of your project. If you are working with a simple scatter plot, Option 1 using the scatter() method is the most straightforward. However, if you have a large dataset or need to update the plot dynamically, Option 2 using the ColumnDataSource is more efficient. Lastly, if you require more control over the glyph properties or need to add different types of glyphs, Option 3 using the add_glyph() method is the most flexible.

Ultimately, the choice between these options will depend on the complexity of your project and your specific needs. It is recommended to experiment with each approach and choose the one that best suits your requirements.

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7 Responses

  1. Option 2 looks cool, but Option 1 is more straightforward. Whats your take, guys? #Python #DataVisualization

    1. Option 3 may have its perks, but simplicity and practicality should not be overlooked. Sometimes less is more, and in this case, a more straightforward approach may save time and effort. Lets not get carried away with cool hashtags. #functionalityoverflashiness

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