12/5/2023 0 Comments Python bokeh tutorial playlistIt'll color code states of the US according to the count of Starbucks stores in that state. The first choropleth map that we'll plot using bokeh is Starbucks store count per US states. It does not need any tile providers or latitude/longitude information. The process of plotting choropleth maps using bokeh is different from previous chart types. The third chart type that we'll introduce using bokeh is a choropleth map. United States Starbucks Store Count Per State Choropleth Map ¶ We have also used tooltip which highlights the source country, the destination country, and a number of flights to that country. We have used line and circle glyphs of bokeh to plot a line between the source and destination of flight and highlight source and destinations. We have used STAMEN_TONER and ESRI_IMAGERY tiles for this chart. We'll then convert source and destination latitude/longitude data to web Mercator projection and add it to the dataframe for later use. We'll then aggregate data to keep all combinations of flights from Brazil to other countries to get a count of flights from brazil to all other countries. We'll first filter the brazil flight dataset to keep only rows where the source country is brazil. The connection map that we'll plot using the Brazil flights dataset will show flights from brazil to all other countries along with their count when hovered over the endpoint of the edge. We'll follow the same steps as mentioned earlier but will use the Brazil flight dataset this time for explanation purposes. The second type of chart that we'll be plotting using bokeh is a connection map. įlights From Brazil To Other Countries Connection Map ¶ # Add a line renderer with legend and line thickness P = figure(title="Simple Line Plot in Bokeh", x_axis_label='x', y_axis_label='y') # Create a new plot with a title and axis labels # Make Bokeh Push push output to Jupyter Notebook.įrom bokeh.io import push_notebook, show, output_notebook Here is a simple example of how to use Bokeh in Jupyter Notebook: import numpy as np If you already have a version of Python then you can run the following in cmd.exe on Windows or terminal on Mac: pip install bokehīe sure to check out the Bokeh quick start guide for several examples. Once you have anaconda installed onto your machine then you can simply run the following in cmd.exe on Windows or terminal on Mac: conda install bokeh Which you can download and install for free. Īll of those come with the Anaconda Python Distribution. If you plan on installing with Python 2.7 you will also need future. NumPy, Jinja2, Six, Requests, Tornado >= 4.0, PyYaml, DateUtil Installing Bokeh Bokeh's Docs on Installationīokeh runs on Python it has the following dependencies The -show parameter tells bokeh to open a browser window and show document defined in hello_world.py. To launch it you need to execute bokeh on the command line and use the serve command to launch the server: $ bokeh serve -show hello_world.py Plot.line('x', 'y', source=data_source, line_width=3, line_alpha=0.6) """Add a plotted function to the document.ĭoc: A bokeh document to which elements can be added.ĭata_source = ColumnDataSource(data=dict(x=x_values, y=y_values)) We will use this example script ( hello_world.py ): from bokeh.models import ColumnDataSource To use bokeh you need to launch a bokeh server and connect to it using a browser. Its goal is to provide elegant, concise construction of novel graphics in the style of D3.js, 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. Bokeh is a Python interactive visualization library that targets modern web browsers for presentation.
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