Python Bokeh tutorial – Interactive Data Visualization with Bokeh

19 Jan, 2022
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  
Customizing Legends  
Plotting Different Types of Plots Bar PlotScatter PlotPatch PlotArea PlotPie Chart
Bar Plot
Scatter Plot
Patch Plot
Area Plot
Pie Chart
Creating Different Shapes CircleOvalTriangleRectanglePolygon
Circle
Oval
Triangle
Rectangle
Polygon
Plotting Multiple Plots Vertical LayoutsHorizontal LayoutGrid Layout
Vertical Layouts
Horizontal Layout
Grid Layout
Interactive Data Visualization Configuring Plot ToolsInteractive LegendsAdding Widgets to the Plot
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:
Python3
# importing the modulesfrom bokeh.plotting import figure, output_file, show # instantiating the figure objectgraph = figure(title = "Bokeh Line Graph") # the points to be plottedx = [1, 2, 3, 4, 5]y = [5, 4, 3, 2, 1] # plotting the line graphgraph.line(x, y) # displaying the modelshow(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:
Python3
# importing the modulesfrom bokeh.plotting import figure, output_file, show # instantiating the figure objectgraph = figure(title="Bokeh Line Graph") # the points to be plottedx = [1, 2, 3, 4, 5]y = [5, 4, 3, 2, 1] # plotting the 1st line graphgraph.line(x, x, legend_label="Line 1") # plotting the 2nd line graph with a# different colorgraph.line(y, x, legend_label="Line 2",           line_color="green") # displaying the modelshow(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:
Python3
# importing the modulesfrom bokeh.plotting import figure, output_file, show # instantiating the figure objectgraph = figure(title="Bokeh Line Graph") # the points to be plottedx = [1, 2, 3, 4, 5]y = [5, 4, 3, 2, 1] # plotting the 1st line graphgraph.line(x, x, legend_label="Line 1") # plotting the 2nd line graph with a# different colorgraph.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 modelshow(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:
Python3
# importing the modulesfrom bokeh.plotting import figure, output_file, show # instantiating the figure objectgraph = figure(title = "Bokeh Line Graph") # the points to be plottedx = [1, 2, 3, 4, 5]y = [5, 4, 3, 2, 1] # plotting the line graphgraph.line(x, y) # displaying the modelshow(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.
Python3
# importing the modulesfrom bokeh.plotting import figure, output_file, show # instantiating the figure objectgraph = figure(title = "Bokeh Bar Graph") # the points to be plottedx = [1, 2, 3, 4, 5]y = [1, 2, 3, 4, 5]  # height / thickness of the plotheight = 0.5 # plotting the bar graphgraph.hbar(x, right = y, height = height) # displaying the modelshow(graph)
Output:
Example 2: Creating the vertical bars
Python3
# importing the modulesfrom bokeh.plotting import figure, output_file, show # instantiating the figure objectgraph = figure(title = "Bokeh Bar Graph") # the points to be plottedx = [1, 2, 3, 4, 5]y = [1, 2, 3, 4, 5]  # height / thickness of the plotwidth = 0.5 # plotting the bar graphgraph.vbar(x, top = y, width = width) # displaying the modelshow(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:
Python3
# importing the modulesfrom bokeh.plotting import figure, output_file, showfrom bokeh.palettes import magmaimport random      # instantiating the figure objectgraph = figure(title = "Bokeh Scatter Graph") # points to be plottedx = [n for n in range(256)]y = [random.random() + 1 for n in range(256)]  # plotting the graphgraph.scatter(x, y) # displaying the modelshow(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:
Python3
# importing the modulesfrom bokeh.plotting import figure, output_file, showfrom bokeh.palettes import magmaimport random      # instantiating the figure objectgraph = figure(title = "Bokeh Patch Plo") # points to be plottedx = [n for n in range(256)]y = [random.random() + 1 for n in range(256)] # plotting the graphgraph.patch(x, y) # displaying the modelshow(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
Python
# Implementation of bokeh functionimport numpy as npfrom 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 plotp.varea(x=x, y1=y1, y2=y2,fill_color="green") show(p)
Output:
Example 2: Creating horizontal area plot
Python3
# Implementation of bokeh function     import numpy as npfrom 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 plotp.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: 
Python3
# importing the modulesfrom bokeh.plotting import figure, output_file, show         # instantiating the figure objectgraph = figure(title = "Bokeh Wedge Graph")     # the points to be plottedx = 0y = 0 # radius of the wedgeradius = 15 # start angle of the wedgestart_angle = 1 # end angle of the wedgeend_angle = 2 # plotting the graphgraph.wedge(x, y, radius = radius,            start_angle = start_angle,            end_angle = end_angle)     # displaying the modelshow(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:
Python3
import numpy as npfrom bokeh.plotting import figure, output_file, show # creating the figure objectplot = 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:
Python3
# importing the modulesfrom bokeh.plotting import figure, output_file, show      # instantiating the figure objectgraph = figure(title = "Bokeh Oval Graph")     # the points to be plottedx = [1, 2, 3, 4, 5]y = [i * 2 for i in x] # plotting the graphgraph.oval(x, y,        height = 0.5,        width = 1)     # displaying the modelshow(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:
Python3
# importing the modulesfrom bokeh.plotting import figure, output_file, show  # instantiating the figure objectgraph = figure(title = "Bokeh Triangle Graph")     # the points to be plottedx = 1y = 1 # plotting the graphgraph.triangle(x, y, size = 150)     # displaying the modelshow(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:
Python3
# importing the modulesfrom bokeh.plotting import figure, output_file, show          # instantiating the figure objectgraph = figure(title = "Bokeh Rectangle Graph", match_aspect = True) # the points to be plottedx = 0y = 0width = 10height = 5 # plotting the graphgraph.rect(x, y, width, height)     # displaying the modelshow(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:
Python3
# importing the modulesfrom bokeh.plotting import figure, output_file, show      # instantiating the figure objectgraph = figure(title = "Bokeh Multiple Polygons Graph")     # the points to be plottedxs = [[[[1, 1, 3, 4]]]]ys = [[[[1, 3, 2 ,1]]]]     # plotting the graphgraph.multi_polygons(xs, ys)     # displaying the modelshow(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.
Python3
from bokeh.io import output_file, showfrom bokeh.layouts import columnfrom bokeh.plotting import figure  x = [1, 2, 3, 4, 5, 6]y0 = xy1 = [i * 2 for i in x]y2 = [i ** 2 for i in x] # create a new plots1 = figure(width=200, plot_height=200)s1.circle(x, y0, size=10, alpha=0.5) # create another ones2 = figure(width=200, height=200)s2.triangle(x, y1, size=10, alpha=0.5) # create and anothers3 = figure(width=200, height=200)s3.square(x, y2, size=10, alpha=0.5) # put all the plots in a VBoxp = column(s1, s2, s3) # show the resultsshow(p)
Output:
Horizontal Layout set all the plots in the horizontal fashion. It can be created using the row() method.
Example:
Python3
from bokeh.io import output_file, showfrom bokeh.layouts import rowfrom bokeh.plotting import figure  x = [1, 2, 3, 4, 5, 6]y0 = xy1 = [i * 2 for i in x]y2 = [i ** 2 for i in x] # create a new plots1 = figure(width=200, plot_height=200)s1.circle(x, y0, size=10, alpha=0.5) # create another ones2 = figure(width=200, height=200)s2.triangle(x, y1, size=10, alpha=0.5) # create and anothers3 = figure(width=200, height=200)s3.square(x, y2, size=10, alpha=0.5) # put all the plots in a VBoxp = row(s1, s2, s3) # show the resultsshow(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:
Python3
from bokeh.io import output_file, showfrom bokeh.layouts import gridplotfrom bokeh.plotting import figure  x = [1, 2, 3, 4, 5, 6]y0 = xy1 = [i * 2 for i in x]y2 = [i ** 2 for i in x] # create a new plots1 = figure()s1.circle(x, y0, size=10, alpha=0.5) # create another ones2 = figure()s2.triangle(x, y1, size=10, alpha=0.5) # create and anothers3 = figure()s3.square(x, y2, size=10, alpha=0.5) # put all the plots in a gridp = gridplot([[s1, None], [s2, s3]], plot_width=200, plot_height=200) # show the resultsshow(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 ToolsClick/Tap ToolsScroll/Pinch Tools
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: 
Python3
# importing the modulesfrom bokeh.plotting import figure, output_file, show # instantiating the figure objectgraph = figure(title = "Bokeh ToolBar", toolbar_location="below") # the points to be plottedx = [1, 2, 3, 4, 5]y = [1, 2, 3, 4, 5]  # height / thickness of the plotwidth = 0.5 # plotting the scatter graphgraph.scatter(x, y) # displaying the modelshow(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
Python3
# importing the modulesfrom bokeh.plotting import figure, output_file, show # file to save the modeloutput_file("gfg.html")         # instantiating the figure objectgraph = figure(title = "Bokeh Hiding Glyphs") # plotting the graphgraph.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 glyphsgraph.legend.click_policy = "hide" # displaying the modelshow(graph)
Output:
Example 2: Muting the legend
Python3
# importing the modulesfrom bokeh.plotting import figure, output_file, show # file to save the modeloutput_file("gfg.html")         # instantiating the figure objectgraph = figure(title = "Bokeh Hiding Glyphs") # plotting the graphgraph.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 glyphsgraph.legend.click_policy = "mute" # displaying the modelshow(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:
Python3
from bokeh.io import showfrom 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:
Python3
from bokeh.io import showfrom bokeh.models import CheckboxGroup, CustomJS L = ["First", "Second", "Third"] # the active parameter sets checks the selected value# by defaultcheckbox_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:
Python3
from bokeh.io import showfrom bokeh.models import RadioGroup, CustomJS L = ["First", "Second", "Third"] # the active parameter sets checks the selected value# by defaultradio_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:
Python3
from bokeh.io import showfrom 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:
Python3
from bokeh.io import showfrom 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:
Python3
from bokeh.plotting import figure, output_file, showfrom bokeh.models import Panel, Tabsimport numpy as npimport 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
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