Introduction to Bokeh

Interactive Data Visualization with Bokeh

George Boorman

Core Curriculum Manager, DataCamp

What is Bokeh?

Documentation

Gallery

finance_interactive

documentation

cars_interactive

Course overview

Chapter 1: Introduction to Bokeh
Chapter 2: Customizing visualizations
Chapter 3: Storytelling with visualizations
Chapter 4: Introduction to widgets

import pandas as pd
nba = pd.read_csv("nba.csv")
print(nba.columns)

Index(['player', 'position', 'minutes', 'field_goal_perc', 'three_point_perc',
       'free_throw_perc', 'rebounds', 'assists', 'steals', 'blocks', 'points',
       'team', 'conference', 'scorer_category'],
      dtype='object')

print(nba.shape)

(424, 14)

Setting up a figure

from bokeh.plotting import figure

from bokeh.io import output_file, show

fig = figure()

output_file(filename="empty_figure.html")

show(fig)

Building a scatter plot

fig = figure(x_axis_label="Minutes Played", y_axis_label="Points Per Game")

fig.circle(x=nba["minutes"], y=nba["points"])

output_file(filename="nba_points.html")
show(fig)

nba_scatter

Displaying a line plot

fig = figure(x_axis_label="Season", y_axis_label="Points")

fig.line(x=lebron["season"], y=lebron["points"])

output_file(filename="lebron_points_per_season.html")
show(fig)

Lebron_line_plot

Plotting categorical data

positions = nba.groupby("position", as_index=False)["points"].mean()

fig = figure(x_axis_label="Position", y_axis_label="Points per Game",

             x_range=positions["position"])

fig.vbar(x=positions["position"], top=positions["points"])

output_file(filename="nba_points_by_position.html")
show(fig)

nba_positions

Let's practice!

Interactive Data Visualization with Bokeh