Pandas Bokeh provides a Bokeh plotting backend for Pandas and GeoPandas, similar to the already existing Visualization feature of Pandas. Importing the library adds a complementary plotting method plot_bokeh() on DataFrames and Series. It also has native plotting backend support for Pandas >= 0.25.
For more information and examples have a look at the Github Repository.
You can install Pandas Bokeh from PyPI via pip:
pip install pandas-bokeh
or conda:
conda install -c patrikhlobil pandas-bokeh
Pandas Bokeh is officially supported on Python 3.5 and above.
With Pandas Bokeh, creating stunning, interactive, HTML-based visualization is as easy as calling:
df.plot_bokeh()The following plot types are supported:
- line
- step
- point
- scatter
- bar
- histogram
- area
- pie
- mapplot
Furthermore, also GeoPandas and Pyspark have a new plotting backend as can be seen in the provided examples.
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. Nevertheless, there are many options for customizing the plots, for example:
- figsize: Choose width & height of the plot
- title: Sets title of the plot
- xlim/ylim: Set visible range of plot for x- and y-axis (also works for datetime x-axis)
- xlabel/ylabel: Set x- and y-labels
- logx/logy: Set log-scale on x-/y-axis
- xticks/yticks: Explicitly set the ticks on the axes
- colormap: Defines the colors to plot. Can be either a list of colors or the name of a Bokeh color palette
- hovertool_string: For customization of hovertool content
Each plot type like scatterplot or histogram further has many more additional customization options that is described here.