Lead by Team Anaconda, Data Science Training
Bokeh is an interactive data visualization library for Python—and other languages—that targets modern web browsers for presentation. It can create versatile, data-driven graphics and connect the full power of the entire Python data science stack to create rich, interactive visualizations.
- Glyphs
- Visual shapes
- circles, squares, triangles
- rectangles, lines, wedges
- With properties a ached to data
- coordinates (x, y)
- size, color, transparency
- Visual shapes
- Markers
circle(),asterisk(),inverted_triangle(),x(), ...
- Additional glyphs
- Lines
- Lines and Markers together
- Patches
- Useful for showing geographic regions
- Data given as "list of lists"
patch(),patches(),annular_wedge(),image_url(),bezier(), etc.
from bokeh.io import output_file, show
from bokeh.plotting import figure
plot = figure(plot_width=400, tools='pan,box_zoom')
plot.circle([1,2,3,4,5], [8,6,5,2,3])
output_file('circle.html')
show(plot)
plot = figure()
plot.circle(x=10, y=[2,5,8,12], size=[10,20,30,40])
x = [1,2,3,4,5]
y = [8,6,4,2,3]
plot = figure()
plot.line(x, y, line_width=3)
output_file('line.html')
show(plot)
plot.line(x, y, line_width=2)
plot.circle(x, y, fill_color='white', size=10)
output_file('line.html')
show(plot)
xs = [[1,1,2,2], [2,2,4], [2,2,3,3]]
ys = [[2,5,5,2], [3,5,5], [2,3,4,2]]
plot = figure()
plot.patches(xs, ys,
fill_color=['red', 'blue', 'green'],
line_color='white')
output_file('patches.html')
show(plot)- Data formats
- Python Basic Types
- NumPy Arrays
- Pandas
- Column Data Source
- Common fundamental data structure for Bokeh
- Maps string column names to sequences of data
- Often created automatically for you
- Can be shared between glpyhs to link selections
- Extra columns can be used with hover tooltips
from bokeh.io import output_file, show
from bokeh.plotting import figure
import numpy as np
x = np.linspace(0, 10, 1000)
y = np.sin(x) + np.random.random(1000) * 0.2
plot = figure()
plot.line(x, y)
output_file('numpy.html')
show(plot)
# Flowers is a Pandas DataFrame
from bokeh.sampledata.iris import flowers
plot = figure()
plot.circle(flowers['petal_length'],
flowers['sepal_length'],
size=10)
output_file('pandas.html')
show(plot)
from bokeh.models import ColumnDataSource
source = ColumnDataSource(data={
'x':[1, 2, 3, 4, 5],
'y':[8, 6, 5, 2, 3]})
from bokeh.sampledata.iris import flowers as df
source = ColumnDataSource(df)- Customizing glyphs
- Selection appearance
- Hover appearance
- Color mapping
plot = figure(tools='box_select, lasso_select')
plot.circle(petal_length, sepal_length,
selection_color='red',
nonselection_fill_alpha=0.2,
nonselection_fill_color='grey')
from bokeh.models import HoverTool
hover = HoverTool(tooltips=None, mode='hline')
plot = figure(tools=[hover, 'crosshair'])
# x and y are lists of random points
plot.circle(x, y, size=15, hover_color='red')
from bokeh.models import CategoricalColorMapper
mapper = CategoricalColorMapper(
factors=['setosa', 'virginica', 'versicolor'],
palette=['red', 'green', 'blue'])
plot = figure(x_axis_label='petal_length', axis_label='sepal_length')
plot.circle('petal_length', 'sepal_length',
sze=10, source=source,
color={'field': 'species', 'transform': mapper})Introduction to basic plotting with Bokeh. First plots, different data formats Bokeh understands, and visual customizations for selections and mouse hovering.
Combine multiple Bokeh plots into different kinds of layouts on a page, easily link different plots together, and add annotations such as legends and hover tooltips.
Bokeh server applications allow to connect all of the powerful Python libraries for data science and analytics, such as NumPy and pandas to create rich, interactive Bokeh visualizations. Bokeh's built-in widgets, add them to Bokeh documents alongside plots, and connect everything to real Python code using the Bokeh server.
Build a more sophisticated Bokeh data exploration application from the ground up based on the famous Gapminder dataset.