With interactive data visualizations, we can zoom in and out, filter and sort data, and explore relationships between different variables. Interactive data visualization allows us to explore data in a more intuitive and dynamic way. Why is interactive data visualization important? In other words, data visualization is the use of charts, graphs, and other graphical representations to present data in a way that is easily understood.
BOKEH EXAMPLES HOW TO
The shown above examples demonstrate how to make the graph data interactive and hope will help to make quick start in this direction.ġ. Toggle2 = Button(label="Load data file 2", callback=callback, name="2")Īs mentioned on the web, Interactive data visualizations turn plots into powerful interfaces for data exploration. Toggle1 = Button(label="Load data file 1", callback=callback, name="1") Source2 = ColumnDataSource(data=dict(x1=df1.x.values, y1=df1.y.values,Ĭallback = CustomJS(args=dict(source=source, source2=source2), code="""ĭata = data2 Source = ColumnDataSource(data=dict(x=, y=)) Plot = figure(plot_width=400, plot_height=400, title="xxx") We use two buttons, two files and 2 dataframes, the buttons allow to switch between data files and reload the graph.įrom otting import figure, output_file, show
This example is borrowed from stackoverflow In this example we also use button, but here we load data from file to dataframe.
Our 3rd example demonstrates how to load data from csv data file. Toggle1 = Button(label="Change Graph", callback=callback, name="1")
In this example we use Button on_click method to change graph.įrom bokeh.models import CustomJS, ColumnDataSource Our second example shows how to use button control. Slider = Slider(start=0.1, end=4, value=1, step=.1, title="power",Ĭallback=om_py_func(callback))Īlternatively we can attach callback through the js_on_change method of Bokeh slider model:Ĭallback = CustomJS(args=dict(source=source), code=""" Plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)ĭef callback(source=source, window=None): Plot = Figure(plot_width=400, plot_height=400) Source = ColumnDataSource(data=dict(x=x, y=y)) The example is using callbak function attached to slider control.įrom bokeh.models import CustomJS, ColumnDataSource, Sliderįrom otting import Figure, output_file, show See Fig 2 for example how the plot is changing. When we change the slider value the line is changing its properties. This example is taken from Bokeh documentation. Our first example demonstrates how to add interactivity with slide control. The examples show how to add create plot and how to add interactivity using Bokeh and will help to make quick start in using Bokeh. They use loading data from data files which is common situation in practice. The examples include such popular controls like slider, button. In this post we put together 4 most common examples of data plots using Bokeh. Trend for Bokeh and Other Data Visualizations Libraries. There is a sharp increase of popularity for Bokeh data visualization libraries (Fig 1), reflecting the increased interest in machine learning and data science over the last few years.
Bokeh provides a variety of ways to embed plots and data into HTML documents including generating standalone HTML documents. Bokeh allows to add interactive controls like slider, buttons, dropdown menu and so on to the data graphs. Bokeh is a Python library for building advanced and modern data visualization web applications.
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