Tools parameter will contain comma separated values which will add additional capabilities to the plot. We have also passed tools argument to figure method. There are lot more other glyphs available like line, cross, patches, etc that are provided by bokeh package. Here in the example shown above, we have used circle glyphs to generate visualization. Output_file('out_sales_price_garbage_area.html') Selection_color='green', nonselection_alpha=0.1, color='red', alpha=0.5) P.circle(x=housePropertyDataset, y=housePropertyDataset, size=5, P = figure(x_axis_label='Garbage Area', y_axis_label='Sales Price', tools='box_select') Let's look at the sample code to draw bokeh visualization: Its goal is to provide elegant, concise construction of versatile graphics, and to extend this capability with high-performance interactivity over very large or streaming datasets.īokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications. HousePropertyDataset = pd.read_csv('house_property_sales.csv')īokeh is an interactive visualization library that targets modern web browsers for presentation. The data has been loaded in housePropertyDataset variable. In the examples shown below, we will be using house property sales dataset from the kaggle. Continuing the visualizing the data, In this post, we will be looking at the technique used to visualize data using Bokeh package. We saw various visualizations provided by Seaborn and matplotlib package. In previous post Data Visualization using Python, we have learned about various visualization techniques to plot data.
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