Plotting with bokeh

Last updated on 2024-02-13 | Edit this page

Overview

Questions

  • Can I use Python to create plots?
  • How can I customize plots generated in Python?

Objectives

  • Create a ggplot object
  • Set universal plot settings
  • Modify an existing ggplot object
  • Change the aesthetics of a plot such as colour
  • Edit the axis labels
  • Build complex plots using a step-by-step approach
  • Create scatter plots, box plots, and time series plots
  • Use the facet_wrap and facet_grid commands to create a collection of plots splitting the data by a factor variable
  • Create customized plot styles to meet their needs
Disclaimer

Python has powerful built-in plotting capabilities such as matplotlib, but for this exercise, we will be using the bokeh package, which facilitates the creation of highly-informative plots of structured data.

PYTHON 

import pandas as pd

authors_complete = pd.read_csv( 'eebo.csv', index_col=0)
authors_complete.index.name = 'X'
authors_complete
        EEBO    VID  ... Page Count             Place

X …
A00002 99850634 15849 … 134 London A00005 99842408 7058 … 302 London A00007 99844302 9101 … 386 London A00008 99848896 14017 … 14 The Netherlands? A00011 99837000 1304 … 54 Amsterdam A00012 99853871 19269 … 99 London A00014 33143147 28259 … 1 London A00015 99837006 1310 … 16  London A00018 99850740 15965 … 26 Germany?

149 rows x 10 columns

PYTHON 

from bokeh.plotting import figure, output_file, show
from bokeh.io import output_notebook

Plotting with bokeh

We will make the same plot using the bokeh package.

bokeh is a plotting package that makes it simple to create complex plots from data in a dataframe. It uses default settings, which help creating publication quality plots with a minimal amount of settings and tweaking.

bokeh graphics are built step by step by adding new elements.

To build a bokeh plot we need to:

  • bind the plot to a specific data frame using the data argument

  • define figure (figure), by selecting the variables to be plotted and the variables to define the presentation such as plotting size, title etc.,

We also set some notebook settings with a “output_notebook()” statement to get interactive and exportable plots

PYTHON 

output_notebook()

p = figure(plot_width=400, plot_height=400)
png

We can add simple points to create a scatter plot using circle.

PYTHON 

list_dates = authors_complete['Date']
list_numbers = authors_complete['Page Count']
p.circle(list_dates, list_numbers)
show(p)
png

Building your plots

We can add extra arguments into circle’s argument.

For comparison, we create a new figure and then add the alpha argument to circle to change the opacity of the points.

PYTHON 

p1 = figure(plot_width=400, plot_height=400)

p1.circle(list_dates, list_numbers, alpha=0.1)

show(p1)
png

We can also add colors for all the points.

PYTHON 

p2 = figure(plot_width=400, plot_height=400)

p2.circle(list_dates, list_numbers, color="blue", alpha=0.1)

show(p2)
png

Plotting time series data

Let’s calculate number of counts per year across the dataset. To do that we need to group data first and count records within each group.

PYTHON 

yearly = authors_df[['Date','Place','Page Count']].groupby(['Date', 'Place']).count().reset_index()

PYTHON 

p3 = figure(plot_width=800, plot_height=250)

p3.line(yearly['Date'], yearly['Page Count'], color='navy', alpha=0.5)

show(p3)
year 	place 	count

0 1515 London 1 1 1519 Londini 1 2 1526 London 2 3 1528 London 1 4 1529 Malborow i.e. Antwerp 1 5 1531 London 1

[121 rows x 3 columns]

Timelapse data can be visualised as a line plot with years on x axis and counts on y axis.

p3 = figure(plot_width=800, plot_height=250)
p3.line(yearly['Date'], yearly['Page Count'], color='navy', alpha=0.5)
show(p3)
png

Customization

Now, let’s add a title to this figure:

PYTHON 

from bokeh.models import ColumnDataSource, Range1d, LabelSet, Label

p4 = figure(title="Plot of Page Counts by Year", plot_width=400, plot_height=400)

p4.circle(list_dates, list_numbers)

p4.xaxis[0].axis_label = 'Date'
p4.yaxis[0].axis_label = 'Page Count'

show(p4)
png

or we canadd labels to the axes and change the font size for the labels

PYTHON 

p5 = figure(title="Plot of Page Counts by Year", plot_width=400, plot_height=400)

p5.circle(list_dates, list_numbers)

p5.xaxis[0].axis_label = 'Date'
p5.yaxis[0].axis_label = 'Page Count'
p5.xaxis[0].axis_label_text_font_size = "24pt"

show(p5)
png

With all of this information in hand, please take another five minutes to either improve one of the plots generated in this exercise or create a beautiful graph of your own.

Here are some ideas:

  • Can you find a way to change its labels?
  • Use a different color palette.

After creating your plot, you can save it to a file as a png file:

PYTHON 

from bokeh.io import export_png

export_png(p4, filename="plot.png")