1/10/2024 0 Comments Bokeh python interactive plotI put together a Python Developer Kit with over 100 pre-built Python scripts covering data structures, Pandas, NumPy, Seaborn, machine learning, file processing, web scraping and a whole lot more - and I want you to have it for free. vbar ( source = source, x = 'class', top = 'fare', width = 0.80, color = color_map ) f. tolist () color_map = factor_cmap ( field_name = 'class', palette = Spectral3, factors = classes ) f = figure ( plot_width = 500, plot_height = 500, x_range = classes ) f. To do so, you can simply pass the Year and Life_Expectancy columns from the dataframe to the x and y attributes of the line() function.įrom otting import figure, output_notebook, show from import HoverTool from bokeh.models import ColumnDataSource from bokeh.palettes import Spectral3 from ansform import factor_cmap output_notebook () source = ColumnDataSource ( dataset ) classes = source. We’re going to filter the records for the US, then we’ll plot a line plot that displays yearly average life expectancy. It shows the spending in USD vs the average life expectancy per country from years 1970 to 2020. The script below imports the healthexp dataset from the Python seaborn module. Plotting Bokeh Plots using Pandas DataFramesĪ useful feature of the Bokeh library is that it allows you to plot data from a Pandas dataframes. 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. hovertool_string: For customization of hovertool contentĮach plot type like scatterplot or histogram further has many more additional customization options that is described here.This section will show how to make charts with the Python Bokeh library.Can be either a list of colors or the name of a Bokeh color palette xticks/ yticks: Explicitly set the ticks on the axes.xlim/ ylim: Set visible range of plot for x- and y-axis (also works for datetime x-axis).figsize: Choose width & height of the plot.Nevertheless, there are many options for customizing the plots, for example: Pandas Bokeh is a high-level API for Bokeh on top of Pandas and GeoPandas that tries to figure out best, what the user wants to plot. In release 0.5.5, the following plot types are supported:įurthermore, also GeoPandas and Pyspark have a new plotting backend as can be seen in the provided examples. With Pandas Bokeh, creating stunning, interactive, HTML-based visualization is as easy as calling: df. Pandas Bokeh is officially supported on Python 3.5 and above. Or conda: conda install -c patrikhlobil pandas-bokeh You can install Pandas Bokeh from PyPI via pip: pip install pandas-bokeh It also has native plotting backend support for Pandas >= 0.25.įor more information and examples have a look at the Github Repository. Importing the library adds a complementary plotting method plot_bokeh() on DataFrames and Series. Pandas Bokeh provides a Bokeh plotting backend for Pandas and GeoPandas, similar to the already existing Visualization feature of Pandas.
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