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How to create horizontal and vertical bars in Bokeh.

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by l.rohitharohitha2001@gmail.com dot icon Updated: Aug 23, 2023

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Bokeh is used in photography to represent the aesthetic quality of the out-of-focus area in an image. This is about how the blurry lights look in the picture.


It makes it look soft. Bokeh originates from the Japanese term boke, which means blur or haze. Bokeh is desirable in certain types of photography, as it helps isolate backgrounds. The main subject stands out and draws the viewer's attention to it. Photographers often use this effect in portrait photography. It helps keep the subject's face clear and in focus. In macro photography, it accentuates the details of small subjects.   

  

Effects of Bokeh:   

  1. Subject Isolation: Bokeh helps isolate the subject from distracting or busy backgrounds. It makes the main subject stand out and draws the viewer's attention.  
  2. Depth and Dimension: Bokeh adds a sense of depth and dimension to graphs, enhancing it.  
  3. Artistic Element: Bokeh contributes to the artistic appeal of the photograph, providing.  

   

Bokeh is an essential artistic element in photography. It elevates images by creating soft, pleasing backgrounds that isolate the subjects. By experimenting with different lenses, apertures, and shooting techniques, photographers can use Bokeh.  

 

By mastering the techniques and understanding the nuances of bokeh photography, photographers. It uses powerful images to make the audience feel and remember strong emotions. It is a powerful tool in the photographer's arsenal, providing their creativity.   


Here is the example of how to create horizontal and vertical bars in Bokeh.

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

Code


In this solution we are using Bokeh library of Python.

Bokeh Clustered bar chart

PythondotLines of Code : 54dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
from bokeh.core.properties import value
from bokeh.io import show, output_file
from bokeh.models import ColumnDataSource, HoverTool, CustomJS
from bokeh.plotting import figure
from bokeh.transform import dodge
from bokeh.palettes import Spectral6

data = {'FFCB' : ['D', 'I'],
        'CS'   : [0.013254, 0.01],
        'FPSI'   : [0.00264871, 0.02],
        'SA'   : [0.03, 0.114216],
        'NA'   : [0.0127895, 0.00567031],
        'O'   : [0.00552444, 0.03],
        'FPSDA'   : [0.00136219, 0.04],
        'HDR'   : [0.03, 0.05]}

source = ColumnDataSource(data = data)

p = figure(x_range = data['FFCB'], y_range = (0, 0.2), plot_width = 600, plot_height = 400, title = "Clustered bar chart", tools = '')

vbar1 = p.vbar(x = dodge('FFCB', -0.25, range = p.x_range), top = 'CS', width = 0.1, source = source,
       color = Spectral6[0], legend = value("CS"))
hover_tool_vbar1 = HoverTool(tooltips = [('CS', '@CS{0.000}')], show_arrow = False, renderers = [vbar1])

vbar2 = p.vbar(x = dodge('FFCB', -0.15, range = p.x_range), top = 'FPSI', width = 0.1, source = source,
       color = Spectral6[1], legend = value("FPSI"))
hover_tool_vbar2 = HoverTool(tooltips = [('FPSI', '@FPSI{0.000}')], show_arrow = False, renderers = [vbar2])

vbar3 = p.vbar(x = dodge('FFCB', -0.05, range = p.x_range), top = 'SA', width = 0.1, source = source,
       color = Spectral6[2], legend = value("SA"))
hover_tool_vbar3 = HoverTool(tooltips = [('SA', '@SA{0.000}')], show_arrow = False, renderers = [vbar3])

vbar4 = p.vbar(x = dodge('FFCB', 0.05, range = p.x_range), top = 'NA', width = 0.1, source = source,
       color = Spectral6[3], legend = value("NA"))
hover_tool_vbar4 = HoverTool(tooltips = [('NA', '@NA{0.000}')], show_arrow = False, renderers = [vbar4])

vbar5 = p.vbar(x = dodge('FFCB', 0.15, range = p.x_range), top = 'O', width = 0.1, source = source,
       color = Spectral6[4], legend = value("O"))
hover_tool_vbar5 = HoverTool(tooltips = [('O', '@O{0.000}')], show_arrow = False, renderers = [vbar5])

vbar6 = p.vbar(x = dodge('FFCB', 0.25, range = p.x_range), top = 'HDR', width = 0.1, source = source,
       color = Spectral6[5], legend = value("HDR"))
hover_tool_vbar6 = HoverTool(tooltips = [('HDR', '@HDR{0.000}')], show_arrow = False, renderers = [vbar6])

p.x_range.range_padding = 0.2
p.xgrid.grid_line_color = None
p.legend.location = "top_left"
p.legend.click_policy = 'hide'
p.legend.orientation = "horizontal"

p.add_tools(hover_tool_vbar1, hover_tool_vbar2, hover_tool_vbar3, hover_tool_vbar4, hover_tool_vbar5, hover_tool_vbar6)

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 (v1.0.4).
  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. Remove 17 to 33 lines from the code.
  7. Run the current file to generate the output.


I hope you found this useful.


I found this code snippet by searching for 'Bokeh Clustered bar chart '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. PyCharm Community Edition 2023.1
  2. The solution is created in Python 3.8 Version
  3. Bokeh v1.0.4 Version.


Using this solution, we can be able to create horizontal and vertical bars in Bokeh using Python 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 create horizontal and vertical bars in Bokeh using Python.

Dependent Library


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

                      You can search for any dependent library on kandi like 'bokeh'.

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                      FAQ:  

                      1. What is Python Bokeh, and how does it support interactive data visualization?   

                      Python Bokeh is an open-source data visualization library that allows developers. The Bokeh designers made a tool. This tool can handle big and complicated datasets. It is very useful. It supports various visualization types, including scatter plots and more.  

                       

                      Python Bokeh is a powerful tool for creating interactive data visualizations that enable. Its ability to generate interactive web-based plots supports various interactive widgets. Data scientists, analysts, and developers often use it for data work because it's popular.  

                        

                      2. What are the pros of using Python Bokeh for beautiful data creation?   

                      Using Python Bokeh for data creation offers the benefits of appealing data.  

                      1. Interactive Visualizations: You can create captivating interactive plots using Bokeh for visualizations. It enhances user experience.  
                      2. Web-Based Visualization: Bokeh generates web-based visualizations, making them easy to share and access. It provides a seamless experience for users as they can interact with the data.  
                      3. Customizable Themes and Styling: Bokeh provides theming and styling options to customize. This flexibility allows you to match the visualizations to the branding and design.  
                      4. High-Performance Rendering: Bokeh leverages modern web technologies like HTML5 Canvas and WebSockets. This performance is crucial for creating data-rich and complex visualizations.  
                      5. Integration with Other Python Libraries: Bokeh integrates with popular Python data sciences. This integration allows you to perform data manipulation and transformation.  

                        

                      3. Who are the intended beneficiaries of this technology, Python developers or non-programmers?   

                      Python Bokeh technology aims to benefit both Python developers and non-programmers alike. The versatility and accessibility of the Bokeh Library make it valuable to different.  

                      1. Python Developers: Python Bokeh is an excellent tool for Python developers. With a Python API, developers can leverage their programming skills to create.  
                      2. Data Scientists and Analysts: Data scientists and analysts can use Bokeh to explore. Its integration with popular Python data science libraries, such as NumPy and Pandas.  
                      3. Data Visualization Experts: Bokeh provides an avenue for data visualization experts. Its customizability, theming, and styling options enable experts to create appealing.  

                        

                      4. How can one use scatter plots to understand their data better using the Python Bokeh library?   

                      Scatter plots with the Python Bokeh library can provide valuable insights. A scatter plot is a powerful visualization tool that allows you to visualize.  

                      1. Identifying patterns and trends: Scatter plots help visualize the relationship of variables.  
                      2. Understanding data distribution: The spread and concentration of data points can provide insights.  
                      3. Detecting anomalies: Scatter plots can reveal outliers or data points that deviate.  
                      4. Validating assumptions: You can use scatter plots to confirm assumptions and hypotheses.  

                        

                      5. Can you create visualizations with the Python Bokeh library? Are there any limitations?   

                      Yes, there are limitations when it comes to creating visualizations with Python.  

                      1. Learning Curve: Bokeh has a Python API, but it takes time and effort to learn how to use it. Understanding the various plotting options, interactions, and server components can be challenging.  
                      2. Limited Chart Types: Bokeh supports a wide range of chart types. It might only cover some types of visualization available in other libraries. You should explore other visualization tools for very specialized chart types.  
                      3. Performance with Huge Datasets: Bokeh performs well with moderate-sized datasets. Yet, the interactivity and rendering performance might remain the same with large datasets. One can apply data aggregation or sampling techniques in such cases to improve.  
                      4. Browser Compatibility: Bokeh generates web-based visualizations that rely on modern web technologies. Most modern browsers support these features, but older ones may have compatibility issues.  

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