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How to use Bokeh .palettes

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by vsasikalabe dot icon Updated: Aug 29, 2023

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Bokeh.palettes create a collection of palettes for color mapping. Bokeh is a palette, a simple, plain Python list of (hex) RGB color strings. 

 

This module has the following sets of palettes:   

  • All ColorBrewer palettes   
  • Categorical D3 palettes   
  • Magma, Inferno, Plasma, and Viridis   
  • The Bokeh shutter logo colors   
  • Palettes designed for color-deficient usability   

   

The object is not only the foundation of your data visualization. Also, the object unlocks all the Bokeh's available tools for visualizing data. Implementing interactive elements opens possibilities for exploring your data. You need help to do the static visualizations. Tabs display many visualizations. They relate to each other without needing to generate a dashboard. Also, scroll through several images in a Jupyter Notebook.   

   

Import Bokeh modules for interactive plotting. Users use the Bokeh library to add tooltips to many visualization types. It includes bar charts, line graphs, and scatterplots. The figure of the Bokeh is a subclass of the Plot object. It provides many of the parameters. It makes it possible to configure the aesthetic elements of our figure. Another Bokeh widget is the Dropdown widget. A button displays a dropdown list of exclusive items when clicked. We can use the Matplotlib palettes by plotting many vertical bars using the v bar() function.   

   

People use Bokeh to produce a wide range of visualizations. This is from simple scatter plots and line graphs to complex, interactive plots. To get a complete group of palettes, index this dictionary with a palette name. Then, an integer indexes it to select a palette of a certain size from the group. Pandas AI is an extra Python library. It enhances Pandas—a tool many use to analyze and change data, similar to a painter's palette. 

 

A Bokeh palette is a set of colors to color map. The same colormaps are also registered with matplotlib's string-based dictionary with the prefix. It is making them available by name within various matplotlib functions. By default, make a line plot with joins line segments. You saw the sharp points, some of which dip below zero, that they mitered.   

   

We must import the plotting libraries. So, we get some historical data. This is to create scatter plots. Color mapping P. Color cycles and styles are helpful for categorical plots. The attribute specifies the actual data to map colors. Since this is a stand-alone HTML page, it references BokehJS.   

   

Data explorers use these libraries. As well as used in visualizing and generating graphics for reports. It's important to note font weight, size, and color in data visualization. It provides versatile and interactive visualization tools. It is particularly useful for web-based dashboards and applications. Virtual environments are useful.  

Preview of the output that you will get on running this code from your IDE.

Code

In this solution, we used the Bokeh library.

Multiple line plots using python bokeh.palettes

PythondotLines of Code : 68dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
In [1]: from bokeh.palettes import Spectral11

In [2]: w=['F1','F2','F3','F4','F5','F6','F7','F8','F9','F10','F11','F12','G1','G2','G3','G4','G5','G6','G7','G8'
    ...: ,'G9','G10','G11','G12']

In [3]: for name, color in zip(w, Spectral11):
    ...:     print(name, color)
    ...:
F1 #5e4fa2
F2 #3288bd
F3 #66c2a5
F4 #abdda4
F5 #e6f598
F6 #ffffbf
F7 #fee08b
F8 #fdae61
F9 #f46d43
F10 #d53e4f
F11 #9e0142

In [4]: import itertools

In [5]: for name, color in zip(w, itertools.cycle(Spectral11)):
    ...:     print(name, color)
    ...:
F1 #5e4fa2
F2 #3288bd
F3 #66c2a5
F4 #abdda4
F5 #e6f598
F6 #ffffbf
F7 #fee08b
F8 #fdae61
F9 #f46d43
F10 #d53e4f
F11 #9e0142
F12 #5e4fa2
G1 #3288bd
G2 #66c2a5
G3 #abdda4
G4 #e6f598
G5 #ffffbf
G6 #fee08b
G7 #fdae61
G8 #f46d43
G9 #d53e4f
G10 #9e0142
G11 #5e4fa2
G12 #3288bd

In [6]: import bokeh.plotting
   ...: import numpy as np
   ...: import pandas as pd
   ...:
   ...: bokeh.plotting.output_file('cycle_demo.html')
   ...: lines = np.random.random((100, len(w))) + np.arange(24)
   ...: df = pd.DataFrame(lines)
   ...: df.columns = w
   ...:
   ...: line_dash_styles = [[10, 0], [20, 1], [10, 1], [5, 1]]
   ...: p = bokeh.plotting.figure()
   ...: for name, color, line_dash in zip(w, itertools.cycle(Spectral11), itertools.cycle(line_dash_styles)):
   ...:     p.line(np.arange(100), df[name], color=color, legend=name, line_dash=line_dash)
   ...:
   ...: p.legend.location = "top_left"
   ...: bokeh.plotting.show(p)
   ...:

Instructions

Follow the steps carefully to get the output easily.

  1. Download and Install the PyCharm Community Edition on your computer.
  2. Open the terminal and install the required libraries with the following commands.
  3. Install Bokeh - pip install bokeh.
  4. Create a new Python file on your IDE.
  5. Copy the snippet using the 'copy' button and paste it into your Python file.
  6. Delete all hash tag lines, then from line no 6 to end.
  7. Run the current file to generate the output.


I hope you found this useful. I have added the link to dependent libraries, and version information in the following sections.


I found this code snippet by searching for ' Multiple line plots using python bokeh.palettes ' in Kandi. You can try any such use case!

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. The solution is created in PyCharm 2022.3.
  2. The solution is tested on Python 3.11.1
  3. Bokeh version- 3.1.1.


Using this solution, we are able to use Bokeh palettes with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to use Bokeh palettes.

Dependent Libraries

bokehby bokeh

Python doticonstar image 17667 doticonVersion:Currentdoticon
License: Permissive (BSD-3-Clause)

Interactive Data Visualization in the browser, from Python

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            bokehby bokeh

            Python doticon star image 17667 doticonVersion:Currentdoticon License: Permissive (BSD-3-Clause)

            Interactive Data Visualization in the browser, from Python
            Support
              Quality
                Security
                  License
                    Reuse

                      If you do not have the bokeh library that is required to run this code, you can install them by clicking on the above link.

                      You can search for any dependent library on Kandi like Bokeh.

                      Support

                      1. For any support on kandi solution kits, please use the chat
                      2. For further learning resources, visit the Open Weaver Community learning page.

                      FAQ:   

                      1. What is the Python data visualization library Bokeh? And how does it compare to Matplotlib?    

                      Matplotlib can create any plot. Because it is a low-level visualization library. You can use Bokeh as a high-level or low-level interface. Thus, it can create many sophisticated plots. Matplotlib creates a Plot but with fewer lines of code and higher resolution.   

                         

                      2. Can you explain what a bokeh visualization is and why they are useful?   

                      Bokeh is an interactive visualization library. The best feature that Bokeh provides is highly interactive graphs. Modern web browsers use plots for presentations. Bokeh used to make elegant and concise charts with a wide range of various charts.   

                        

                      3. Are there any advantages of using a simple, plain Python list? Over other methods when making Bokeh visuals?    

                      Bokeh creates its styling option and widgets for the charts. This is the advantage of creating the bokeh charts. And website using Flask or Django. Bokeh provides two simple interface levels. We can adapt easily. Bokeh plotting offers a high-level interface for creating visual glyphs.   

                         

                      4. Can I use Jupyter Notebooks to create complex visualizations with Bokeh?    

                      The powerful feature of Jupyter Notebook is its ability to create interactive visualizations. Bokeh is the most popular interactive visualization library for Python.   

                         

                      5. How can ColorBrewer help me choose colors for my bokeh visuals?    

                      ColorBrewer is the traditional reference for color palettes. It provides many different palettes of each type. We can use the ColorBrewer palettes by plotting many vertical bars. We can do this using the vbar() function. 

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