Like many Python libraries, Bokeh is a large library with complex commands and detailed representations of many types of plots.
In order to get started, you need to have the library installed on your computer. Write:
pip install bokeh
Bokeh is used for several kinds of plots namely scatter plots, grid plots, and line plots. Let’s see how to make a simple scatter plot in bokeh.
Scatter plots are used to display all the coordinates of your x-y values onto the graph. It is a type of plot that shows data as a collection of points. A point’s position depends on its two-dimensional value, where each value is positioned on either the horizontal or vertical dimension.
A dataset can have a million values, so scatter plots are used to visualize large pieces of data.
## Scatter Plot import bokeh from bokeh.plotting import figure, output_notebook, show from random import seed from random import randint seed(1) x_value= y_value= for i in range(20): d = randint(0,30) x_value.append(d) #fill x with random values. seed(1) y_value = [4, 11, 27, 23, 24, 20, 18, 13, 1, 4, 14, 5, 15, 2, 5, 16, 13, 5, 10, 8] #fill y with above values. You can always play around with them. key=[1,4,7,9,22] value=[6,1,3,10,15] # paramters of figure # plot_width - The width of the solution space for plotting. # plot_height - The height of the solution space for plotting a=figure(plot_width = 500, plot_height=500) # color = 'red' makes sure co-ordinates are of red color. a.circle(key, value,size=14,color='red') # color = 'red' makes sure co-ordinates are of red color. a.circle(x_value, y_value, size=14, color = 'blue') # show the plot. show(a) # In the circles and diamond you can visualize a scatter plot. There can be thousand of such values.
For this code,
y are the data points on the x and y-axis. The function
figure creates a space for the data to be plotted, and the
.circle function draws the respective co-ordinates. As you can see in the output, there are co-ordinates that have a circle shape. This function has several parameters, but we have used #key#
color(refers to the color of the co-ordinate) and
size(refers to the size of the co-ordinates to be plotted) for this example. In order to differentiate between two different datasets, we have written
color = 'red'.
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