When you run this script yourself, you can drag, zoom, and save the above plot using the controls displayed on the right-hand side of the plot. circle ( x, y, size = 5, color = 'red', legend_label = 'circle' ) f. ![]() line ( x, y, line_width = 2, color = "blue", legend_label = 'line' ) f. For example, clicking the line legend will hide the line plot, in the output of the following script.įinally, call the show() function on the figure object to display the chart.įrom otting import figure, output_notebook, show import numpy as np x = list ( range ( 11 )) y = output_notebook () f = figure ( plot_width = 400, plot_height = 400 ) f. Setting the legend.click_policy to hide allows you to hide legends by clicking on the legend values. You can then pass the line width, color, and the label for the legend to line_width, color, and legend_label attributes, respectively. Once this is done, you can plot any plot using this figure object.įor example, to make a line plot, use the line() function and pass it the x and y coordinates of your line. You can optionally pass the width and height of your plot here. Next, you need to create a figure object. Otherwise, the plot will be displayed in your default browser. If you want to display the chart inside a Python notebook, you must call the output_notebook() function. To plot a chart with Bokeh, you need to import a figure object, then import the output_notebook and show functions from the otting module. In a later section, we’ll explain how to plot charts with the Pandas-Bokeh library. P.This section will show how to make charts with the Python Bokeh library. # 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) Tools="crosshair,pan,reset,save,wheel_zoom",) ![]() ![]() """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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