Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://www.geeksforgeeks.org/python-bokeh-tutorial-interactive-data-visualization-with-bokeh/ below:

Python Bokeh tutorial - Interactive Data Visualization with Bokeh

Python Bokeh is a Data Visualization library that provides interactive charts and plots. Bokeh renders its plots using HTML and JavaScript that uses modern web browsers for presenting elegant, concise construction of novel graphics with high-level interactivity. 

Features of Bokeh:

• Flexibility: Bokeh can be used for common plotting requirements and for custom and complex use-cases.
• Productivity: Its interaction with other popular Pydata tools (such as Pandas and Jupyter notebook) is very easy.
• Interactivity: It creates interactive plots that change with the user interaction.
• Powerful: Generation of visualizations for specialized use-cases can be done by adding JavaScript.
• Shareable: Visual data are shareable. They can also be rendered in Jupyter notebooks.
• Open source: Bokeh is an open-source project.

This tutorial aims at providing insight to Bokeh using well-explained concepts and examples with the help of a huge dataset. So let's dive deep into the Bokeh and learn all it from basic to advance.

Table Of Content
 

• Installation
• Bokeh Interfaces – Basic Concepts of Bokeh
• Getting Started
• Annotations and Legends 
  
  • Customizing Legends 
     
• Plotting Different Types of Plots 
  
  • Bar Plot
  • Scatter Plot
  • Patch Plot
  • Area Plot
  • Pie Chart
• Creating Different Shapes 
  
  • Circle
  • Oval
  • Triangle
  • Rectangle
  • Polygon
• Plotting Multiple Plots 
  
  • Vertical Layouts
  • Horizontal Layout
  • Grid Layout
• Interactive Data Visualization 
  
  • Configuring Plot Tools
  • Interactive Legends
  • Adding Widgets to the Plot
• Creating Different Types of Glyphs
• Visualizing Different Types of Data
• More Topics on Bokeh

Bokeh is supported by CPython 3.6 and older with both standard distribution and anaconda distribution. Bokeh package has the following dependencies.

1. Required Dependencies

• PyYAML>=3.10
• python-dateutil>=2.1
• Jinja2>=2.7
• numpy>=1.11.3
• pillow>=4.0
• packaging>=16.8
• tornado>=5
• typing_extensions >=3.7.4

2. Optional Dependencies

• Jupyter
• NodeJS
• NetworkX
• Pandas
• psutil
• Selenium, GeckoDriver, Firefox
• Sphinx

Bokeh can be installed using both conda package manager and pip. To install it using conda type the below command in the terminal.

conda install bokeh

This will install all the dependencies. If all the dependencies are installed then you can install the bokeh from PyPI using pip. Type the below command in the terminal.

pip install bokeh

Refer to the below article to get detailed information about the installation of Bokeh.

• Python – Setting up the Bokeh Environment

Bokeh is simple to use as it provides a simple interface to the data scientists who do not want to be distracted by its implementation and also provides a detailed interface to developers and software engineers who may want more control over the Bokeh to create more sophisticated features. To do this Bokeh follows the layered approach. 

This class is the Python Library for Bokeh that contains model classes that handle the JSON data created by Bokeh's JavaScript library (BokehJS). Most of the models are very basic consisting of very few attributes or no methods.

This is the mid-level interface that provides Matplotlib or MATLAB like features for plotting. It deals with the data that is to be plotted and creating the valid axes, grids, and tools. The main class of this interface is the Figure class.

After the installation and learning about the basic concepts of Bokeh let's create a simple plot.

Example:

# importing the modules 
from bokeh.plotting import figure, output_file, show 

# instantiating the figure object 
graph = figure(title = "Bokeh Line Graph") 

# the points to be plotted 
x = [1, 2, 3, 4, 5] 
y = [5, 4, 3, 2, 1] 

# plotting the line graph 
graph.line(x, y) 

# displaying the model 
show(graph) 

Output:

In the above example, we have created a simple Plot with the Title as Bokeh Line Graph. If you are using Jupyter then the output will be created in a new tab in the browser.

Annotations are the supplemental information such as titles, legends, arrows, etc that can be added to the graphs. In the above example, we have already seen how to add the titles to the graph. In this section, we will see about the legends.

Adding legends to your figures can help to properly describe and define them. Hence, giving more clarity. Legends in Bokeh are simple to implement. They can be basic, automatically grouped, manually mentioned, explicitly indexed, and also interactive.

Example:

# importing the modules
from bokeh.plotting import figure, output_file, show

# instantiating the figure object
graph = figure(title="Bokeh Line Graph")

# the points to be plotted
x = [1, 2, 3, 4, 5]
y = [5, 4, 3, 2, 1]

# plotting the 1st line graph
graph.line(x, x, legend_label="Line 1")

# plotting the 2nd line graph with a
# different color
graph.line(y, x, legend_label="Line 2",
           line_color="green")

# displaying the model
show(graph)

Output:

In the above example, we have plotted two different lines with a legend that simply states that which is line 1 and which is line 2. The color in the legends is also differentiated by the color.

Refer to the below articles to get detailed information about the annotations and legends

• Bokeh – Annotations and Legends

Legends in Bokeh can be customized using the following properties.

Example:

# importing the modules
from bokeh.plotting import figure, output_file, show

# instantiating the figure object
graph = figure(title="Bokeh Line Graph")

# the points to be plotted
x = [1, 2, 3, 4, 5]
y = [5, 4, 3, 2, 1]

# plotting the 1st line graph
graph.line(x, x, legend_label="Line 1")

# plotting the 2nd line graph with a
# different color
graph.line(y, x, legend_label="Line 2",
           line_color="green")

graph.legend.title = "Title of the legend"
graph.legend.location ="top_left"
graph.legend.label_text_font_size = "17pt"

# displaying the model
show(graph)

Output:

Glyphs in Bokeh terminology means the basic building blocks of the Bokeh plots such as lines, rectangles, squares, etc. Bokeh plots are created using the bokeh.plotting interface which uses a default set of tools and styles.

Line charts are used to represent the relation between two data X and Y on a different axis. A line plot can be created using the line() method of the plotting module.

Syntax:

line(parameters)

Example:

# importing the modules 
from bokeh.plotting import figure, output_file, show 

# instantiating the figure object 
graph = figure(title = "Bokeh Line Graph") 

# the points to be plotted 
x = [1, 2, 3, 4, 5] 
y = [5, 4, 3, 2, 1] 

# plotting the line graph 
graph.line(x, y) 

# displaying the model 
show(graph) 

Output:

Refer to the below articles to get detailed information about the line plots.

• Python Bokeh – Plotting a Line Graph
• Python Bokeh – Plotting Multiple Lines on a Graph

Bar plot or Bar chart is a graph that represents the category of data with rectangular bars with lengths and heights that is proportional to the values which they represent. It can be of two types horizontal bars and vertical bars. Each can be created using the hbar() and vbar() functions of the plotting interface respectively.

Syntax:

hbar(parameters)

vbar(parameters)

Example 1: Creating horizontal bars.

# importing the modules 
from bokeh.plotting import figure, output_file, show 

# instantiating the figure object 
graph = figure(title = "Bokeh Bar Graph") 

# the points to be plotted 
x = [1, 2, 3, 4, 5] 
y = [1, 2, 3, 4, 5]  

# height / thickness of the plot
height = 0.5

# plotting the bar graph 
graph.hbar(x, right = y, height = height) 

# displaying the model 
show(graph) 

Output:

Example 2: Creating the vertical bars

# importing the modules 
from bokeh.plotting import figure, output_file, show 

# instantiating the figure object 
graph = figure(title = "Bokeh Bar Graph") 

# the points to be plotted 
x = [1, 2, 3, 4, 5] 
y = [1, 2, 3, 4, 5]  

# height / thickness of the plot
width = 0.5

# plotting the bar graph 
graph.vbar(x, top = y, width = width) 

# displaying the model 
show(graph) 

Output:

Refer to the below articles to get detailed information about the bar charts.

• Python Bokeh – Plotting Horizontal Bar Graphs
• Python Bokeh – Plotting Vertical Bar Graphs

A scatter plot is a set of dotted points to represent individual pieces of data in the horizontal and vertical axis. A graph in which the values of two variables are plotted along X-axis and Y-axis, the pattern of the resulting points reveals a correlation between them. It can be plotted using the scatter() method of the plotting module.

Syntax:

scatter(parameters)

Example:

# importing the modules 
from bokeh.plotting import figure, output_file, show 
from bokeh.palettes import magma 
import random 

    
# instantiating the figure object 
graph = figure(title = "Bokeh Scatter Graph") 

# points to be plotted 
x = [n for n in range(256)] 
y = [random.random() + 1 for n in range(256)] 


# plotting the graph 
graph.scatter(x, y) 

# displaying the model 
show(graph) 

Output:

Refer to the below articles to get detailed information about the scatter plots.

• Python Bokeh – Plotting a Scatter Plot on a Graph

Patch Plot shades a region of area to show a group having same properties. It can be created using the patch() method of the plotting module.

Syntax:

patch(parameters)

Example:

# importing the modules 
from bokeh.plotting import figure, output_file, show 
from bokeh.palettes import magma 
import random 

    
# instantiating the figure object 
graph = figure(title = "Bokeh Patch Plo") 

# points to be plotted 
x = [n for n in range(256)] 
y = [random.random() + 1 for n in range(256)] 

# plotting the graph 
graph.patch(x, y) 

# displaying the model 
show(graph) 

Output:

Refer to the below articles to get detailed information about the Patch Plot.

• Python Bokeh – Plotting Patches on a Graph

Area plots are defined as the filled regions between two series that share a common areas. Bokeh Figure class has two methods which are - varea(), harea()

Syntax:

varea(x, y1, y2, **kwargs)

harea(x1, x2, y, **kwargs)

Example 1: Creating vertical area plot

# Implementation of bokeh function
import numpy as np 
from bokeh.plotting import figure, output_file, show 
    
x = [1, 2, 3, 4, 5] 
y1 = [2, 4, 5, 2, 4] 
y2 = [1, 2, 2, 3, 6] 

p = figure(plot_width=300, plot_height=300) 

# area plot 
p.varea(x=x, y1=y1, y2=y2,fill_color="green") 

show(p)

Output:

Example 2: Creating horizontal area plot

# Implementation of bokeh function 
    
import numpy as np 
from bokeh.plotting import figure, output_file, show 
    
y = [1, 2, 3, 4, 5] 
x1 = [2, 4, 5, 2, 4] 
x2 = [1, 2, 2, 3, 6] 

p = figure(plot_width=300, plot_height=300) 

# area plot 
p.harea(x1=x1, x2=x2, y=y,fill_color="green") 

show(p)

Output:

Refer to the below articles to get detailed information about the area charts

• Make an area plot in Python using Bokeh

Bokeh Does not provide a direct method to plot the Pie Chart. It can be created using the wedge() method. In the wedge() function, the primary parameters are the x and y coordinates of the wedge, the radius, the start_angle and the end_angle of the wedge. In order to plot the wedges in such a way that they look like a pie chart, the x, y, and radius parameters of all the wedges will be the same. We will only adjust the start_angle and the end_angle.

Syntax:

wedge(parameters)

Example: 

# importing the modules 
from bokeh.plotting import figure, output_file, show 
        
# instantiating the figure object 
graph = figure(title = "Bokeh Wedge Graph") 
    
# the points to be plotted 
x = 0
y = 0

# radius of the wedge 
radius = 15

# start angle of the wedge 
start_angle = 1

# end angle of the wedge 
end_angle = 2

# plotting the graph 
graph.wedge(x, y, radius = radius, 
            start_angle = start_angle, 
            end_angle = end_angle) 
    
# displaying the model 
show(graph) 

Output:

Refer to the below articles to get detailed information about the pie charts.

• Python Bokeh – Plotting Wedges on a Graph
• Python Bokeh – Making a Pie Chart

The Figure class in Bokeh allows us create vectorised glyphs of different shapes such as circle, rectangle, oval, polygon, etc. Let's discuss them in detail.

Bokeh Figure class following methods to draw circle glyphs which are given below:

• circle() method is a used to add a circle glyph to the figure and needs x and y coordinates of its center.
• circle_cross() method is a used to add a circle glyph with a ‘+’ cross through the center to the figure and needs x and y coordinates of its center.
• circle_x() method is a used to add a circle glyph with a ‘X’ cross through the center. to the figure and needs x and y coordinates of its center.

Example:

import numpy as np 
from bokeh.plotting import figure, output_file, show 

# creating the figure object
plot = figure(plot_width = 300, plot_height = 300) 

plot.circle(x = [1, 2, 3], y = [3, 7, 5], size = 20) 

show(plot) 

Output:

Refer to the below articles to get detailed information about the circle glyphs

• Make an Circle Glyphs in Python using Bokeh

oval() method can be used to plot ovals on the graph.

Syntax:

oval(parameters)

Example:

# importing the modules 
from bokeh.plotting import figure, output_file, show 

    
# instantiating the figure object 
graph = figure(title = "Bokeh Oval Graph") 
    
# the points to be plotted 
x = [1, 2, 3, 4, 5] 
y = [i * 2 for i in x] 

# plotting the graph 
graph.oval(x, y, 
        height = 0.5, 
        width = 1) 
    
# displaying the model 
show(graph) 

Output:

Refer o the below articles to get detailed information about the oval glyphs.

• Python Bokeh – Plotting Ovals on a Graph

Triangle can be created using the triangle() method.

Syntax:

triangle(parameters)

Example:

# importing the modules 
from bokeh.plotting import figure, output_file, show 


# instantiating the figure object 
graph = figure(title = "Bokeh Triangle Graph") 
    
# the points to be plotted 
x = 1
y = 1

# plotting the graph 
graph.triangle(x, y, size = 150) 
    
# displaying the model 
show(graph) 

Output:

Refer to the below article to get detailed information about the triangles.

• Python Bokeh – Plotting Triangles on a Graph

Just like circles and ovals rectangle can also be plotted in Bokeh. It can be plotted using the rect() method.

Syntax:

rect(parameters)

Example:

# importing the modules 
from bokeh.plotting import figure, output_file, show 

        
# instantiating the figure object 
graph = figure(title = "Bokeh Rectangle Graph", match_aspect = True) 

# the points to be plotted 
x = 0
y = 0
width = 10
height = 5

# plotting the graph 
graph.rect(x, y, width, height) 
    
# displaying the model 
show(graph) 

Output:

Bokeh can also be used to plot multiple polygons on a graph. Plotting multiple polygons on a graph can be done using the multi_polygons() method of the plotting module.

Syntax:

multi_polygons(parameters)

Example:

# importing the modules 
from bokeh.plotting import figure, output_file, show 

    
# instantiating the figure object 
graph = figure(title = "Bokeh Multiple Polygons Graph") 
    
# the points to be plotted 
xs = [[[[1, 1, 3, 4]]]] 
ys = [[[[1, 3, 2 ,1]]]] 
    
# plotting the graph 
graph.multi_polygons(xs, ys) 
    
# displaying the model 
show(graph) 

Output:

Refer to the below articles to get detailed information about the polygon glyphs.

• Python Bokeh – Plotting Multiple Polygons on a Graph

There are several layouts provided by the Bokeh in order to create Multiple Plots. These layouts are:

• Vertical Layout
• Horizontal Layout
• Grid Layout

Vertical Layout set all the plots in the vertical fashion and can be created using the column() method.

from bokeh.io import output_file, show
from bokeh.layouts import column
from bokeh.plotting import figure


x = [1, 2, 3, 4, 5, 6]
y0 = x
y1 = [i * 2 for i in x]
y2 = [i ** 2 for i in x]

# create a new plot
s1 = figure(width=200, plot_height=200)
s1.circle(x, y0, size=10, alpha=0.5)

# create another one
s2 = figure(width=200, height=200)
s2.triangle(x, y1, size=10, alpha=0.5)

# create and another
s3 = figure(width=200, height=200)
s3.square(x, y2, size=10, alpha=0.5)

# put all the plots in a VBox
p = column(s1, s2, s3)

# show the results
show(p)

Output:

Horizontal Layout set all the plots in the horizontal fashion. It can be created using the row() method.

Example:

from bokeh.io import output_file, show
from bokeh.layouts import row
from bokeh.plotting import figure


x = [1, 2, 3, 4, 5, 6]
y0 = x
y1 = [i * 2 for i in x]
y2 = [i ** 2 for i in x]

# create a new plot
s1 = figure(width=200, plot_height=200)
s1.circle(x, y0, size=10, alpha=0.5)

# create another one
s2 = figure(width=200, height=200)
s2.triangle(x, y1, size=10, alpha=0.5)

# create and another
s3 = figure(width=200, height=200)
s3.square(x, y2, size=10, alpha=0.5)

# put all the plots in a VBox
p = row(s1, s2, s3)

# show the results
show(p)

Output:

gridplot() method can be used to arrange all the plots in the grid fashion. we can also pass None to leave a space empty for a plot.

Example:

from bokeh.io import output_file, show
from bokeh.layouts import gridplot
from bokeh.plotting import figure


x = [1, 2, 3, 4, 5, 6]
y0 = x
y1 = [i * 2 for i in x]
y2 = [i ** 2 for i in x]

# create a new plot
s1 = figure()
s1.circle(x, y0, size=10, alpha=0.5)

# create another one
s2 = figure()
s2.triangle(x, y1, size=10, alpha=0.5)

# create and another
s3 = figure()
s3.square(x, y2, size=10, alpha=0.5)

# put all the plots in a grid
p = gridplot([[s1, None], [s2, s3]], plot_width=200, plot_height=200)

# show the results
show(p)

Output:

One of the key feature of Bokeh which differentiate it from other visualizing libraries is adding interaction to the Plot. Let's see various interactions that can be added to the plot.

In all the above graphs you must have noticed a toolbar that appears mostly at the right of the plot. Bokeh provides us the methods to handle these tools. Tools can be classified into four categories.

• Gestures: These tools handle the gestures such as pan movement. There are three types of gestures:
  • Pan/Drag Tools
  • Click/Tap Tools
  • Scroll/Pinch Tools
• Actions: These tools handle when a button is pressed.
• Inspectors: These tools report information or annotate the graph such as  HoverTool.
• Edit Tools: These are multi gestures tools that can add, delete glyphs from the graph.

We can specify the position of the toolbar according to our own needs. It can be done by passing the toolbar_location parameter to the figure() method. The possible value to this parameter is - 

• "above"
• "below"
• "left"
• "right"

Example: 

# importing the modules 
from bokeh.plotting import figure, output_file, show 

# instantiating the figure object 
graph = figure(title = "Bokeh ToolBar", toolbar_location="below") 

# the points to be plotted 
x = [1, 2, 3, 4, 5] 
y = [1, 2, 3, 4, 5]  

# height / thickness of the plot
width = 0.5

# plotting the scatter graph 
graph.scatter(x, y) 

# displaying the model 
show(graph) 

Output:

In the section annotations and legends we have seen the list of all the parameters of the legends, however, we have not discussed the click_policy parameter yet. This property makes the legend interactive. There are two types of interactivity -

• Hiding: Hides the Glyphs.
• Muting: Hiding the glyph makes it vanish completely, on the other hand, muting the glyph just de-emphasizes the glyph based on the parameters.

Example 1: Hiding the legend

# importing the modules 
from bokeh.plotting import figure, output_file, show 

# file to save the model 
output_file("gfg.html") 
        
# instantiating the figure object 
graph = figure(title = "Bokeh Hiding Glyphs") 

# plotting the graph 
graph.vbar(x = 1, top = 5, 
        width = 1, color = "violet", 
        legend_label = "Violet Bar") 
graph.vbar(x = 2, top = 5, 
        width = 1, color = "green", 
        legend_label = "Green Bar") 
graph.vbar(x = 3, top = 5, 
        width = 1, color = "yellow", 
        legend_label = "Yellow Bar") 
graph.vbar(x = 4, top = 5, 
        width = 1, color = "red", 
        legend_label = "Red Bar") 

# enable hiding of the glyphs 
graph.legend.click_policy = "hide"

# displaying the model 
show(graph) 

Output:

Example 2: Muting the legend

# importing the modules 
from bokeh.plotting import figure, output_file, show 

# file to save the model 
output_file("gfg.html") 
        
# instantiating the figure object 
graph = figure(title = "Bokeh Hiding Glyphs") 

# plotting the graph 
graph.vbar(x = 1, top = 5, 
        width = 1, color = "violet", 
        legend_label = "Violet Bar", 
        muted_alpha=0.2) 
graph.vbar(x = 2, top = 5, 
        width = 1, color = "green", 
        legend_label = "Green Bar", 
        muted_alpha=0.2) 
graph.vbar(x = 3, top = 5, 
        width = 1, color = "yellow", 
        legend_label = "Yellow Bar", 
        muted_alpha=0.2) 
graph.vbar(x = 4, top = 5, 
        width = 1, color = "red", 
        legend_label = "Red Bar", 
        muted_alpha=0.2) 

# enable hiding of the glyphs 
graph.legend.click_policy = "mute"

# displaying the model 
show(graph) 

Output:

Bokeh provides GUI features similar to HTML forms like buttons, slider, checkbox, etc. These provide an interactive interface to the plot that allows to change the parameters of the plot, modifying plot data, etc. Let's see how to use and add some commonly used widgets. 

• Buttons: This widget adds a simple button widget to the plot. We have to pass a custom JavaScript function to the CustomJS() method of the models class.

Syntax:

Button(label, icon, callback)

Example:

from bokeh.io import show
from bokeh.models import Button, CustomJS

button = Button(label="GFG")
button.js_on_click(CustomJS(
  code="console.log('button: click!', this.toString())"))

show(button)

Output: 

• CheckboxGroup: Adds a standard check box to the plot. Similarly to buttons we have to pass the custom JavaScript function to the CustomJS() method of the models class.

Example:

from bokeh.io import show
from bokeh.models import CheckboxGroup, CustomJS

L = ["First", "Second", "Third"]

# the active parameter sets checks the selected value 
# by default
checkbox_group = CheckboxGroup(labels=L, active=[0, 2])

checkbox_group.js_on_click(CustomJS(code="""
    console.log('checkbox_group: active=' + this.active, this.toString())
"""))

show(checkbox_group)

Output:

• RadioGroup: Adds a simple radio button and accepts a custom JavaScript function.

Syntax:

RadioGroup(labels, active)

Example:

from bokeh.io import show
from bokeh.models import RadioGroup, CustomJS

L = ["First", "Second", "Third"]

# the active parameter sets checks the selected value 
# by default
radio_group = RadioGroup(labels=L, active=1)

radio_group.js_on_click(CustomJS(code="""
    console.log('radio_group: active=' + this.active, this.toString())
"""))

show(radio_group)

Output:

• Sliders: Adds a slider to the plot. It also needs a custom JavaScript function.

Syntax:

Slider(start, end, step, value)

Example:

from bokeh.io import show
from bokeh.models import CustomJS, Slider

slider = Slider(start=1, end=20, value=1, step=2, title="Slider")

slider.js_on_change("value", CustomJS(code="""
    console.log('slider: value=' + this.value, this.toString())
"""))

show(slider)

Output:

• DropDown: Adds a dropdown to the plot and like every other widget it also needs a custom JavaScript function as callback.

Example:

from bokeh.io import show
from bokeh.models import CustomJS, Dropdown

menu = [("First", "First"), ("Second", "Second"), ("Third", "Third")]

dropdown = Dropdown(label="Dropdown Menu", button_type="success", menu=menu)

dropdown.js_on_event("menu_item_click", CustomJS(
    code="console.log('dropdown: ' + this.item, this.toString())"))

show(dropdown)

Output:

• Tab Widget: Tab Widget adds tabs and each tab show a different plot.

Example:

from bokeh.plotting import figure, output_file, show
from bokeh.models import Panel, Tabs
import numpy as np
import math


fig1 = figure(plot_width=300, plot_height=300)

x = [1, 2, 3, 4, 5]
y = [5, 4, 3, 2, 1]

fig1.line(x, y, line_color='green')
tab1 = Panel(child=fig1, title="Tab 1")

fig2 = figure(plot_width=300, plot_height=300)

fig2.line(y, x, line_color='red')
tab2 = Panel(child=fig2, title="Tab 2")

all_tabs = Tabs(tabs=[tab1, tab2])

show(all_tabs)

Output:

• Diamond Glyph
• Dash Glyph
• Cross Glyph
• Diamond Glyph
• Diamond Cross Glyph
• Diamond Dot Glyph
• Ray Glyph
• Triangle Pins Glyph
• Triangle with Dots Glyph
• Inverted Triangle Glyph
• Plus Glyph
• Quadrilateral Glyph
• Y Glyph
• X Glyph
• Oval Glyph
• Hexagon Glyph
• Hexagon Tiles Glyph
• Hexagon Dots Glyph
• Square with Cross Glyph
• Square with Dots Glyph
• Square with X's Glyph
• Ellipse glyph
• Annulus Glyph
• Bezier Glyph
• Asterisk Glyph
• AnnularWedge Glyph
• Step Glyph

• Python Bokeh – Visualizing the Iris Dataset
• Python Bokeh – Visualizing Stock Data
• Interactive visualization of data using Bokeh

• Python Bokeh – Colors Class
• How to use Color Palettes in Python-Bokeh?
• Python Bokeh – Plotting Quadratic Curves on a Graph
• Python Bokeh – Plotting Line Segments on a Graph
• Python Bokeh – Plotting Multiple Lines on a Graph
• Python Bokeh – Plotting glyphs over a Google Map
• Python Bokeh – Plot for all Types of Google Maps ( roadmap, satellite, hybrid, terrain)

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4