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How to use bokeh.plotting

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by l.rohitharohitha2001@gmail.com dot icon Updated: Aug 24, 2023

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Bokeh is a term used in photography. It helps describe the quality of the out-of-focus areas in an image. It refers to how a camera lens renders points of light out of the depth of field, resulting in a soft.


Various factors, including the lens design and aperture setting, create Bokeh. Lenses with wider apertures are generally better at producing pronounced and appealing Bokeh. Bokeh can enhance the visual impact of a photograph by isolating the subjects.   

Types of Bokeh:  

  •  Soft and Creamy Bokeh   
  •  Harsh or Busy Bokeh   
  •  Circular Bokeh   
  •  Polygonal Bokeh   
  •  Cat's Eye Bokeh   
  •  Double Bokeh   
  •  Colorful Bokeh   
  •  Subject-Enhancing Bokeh   

Techniques of using Bokeh:  

To create Bokeh, you need a nice blurry background that highlights your main subject. Bokeh creates depth and separation, making the subject stand out.   

  1. Wide Aperture (Low f-number): To create Bokeh, you can use a wide aperture with a low f-number. When taking portraits, using a wide aperture like f/1.8 or f/2.8, the background blurs while the subject remains clear.  
  2. Long Focal Length: Telephoto lenses are longer, making the depth of field shallower. Use a telephoto lens and stay far away to blur the environment and focus on the subject.  
  3. Subject-to-Background Distance: Bring the subject closer to the camera to improve the picture. But away from the background. By using this method and a large opening, the background becomes very blurry.  
  4. Bokeh Balls and Shapes: Some lenses can make Bokeh look like circles or hexagons. The diaphragm blades within the lens create these shapes. A lens with more blades can make the Bokeh highlights smoother and rounder.  
  5. Foreground Elements: To make your photo appealing, add blurry objects in the foreground. They don't need to be in focus. Foreground elements can make the image more interesting and dynamic.  
  6. Bokeh Filters and Masks: Bokeh filters or masks are accessories that can attach to the front of your lens. They create custom-shaped bokeh highlights in your images. You can try different bokeh effects by using filters with different shapes.  
  7. Creative Lighting: Use various string or fairy lights for a better bokeh effect. Background city lights also work well. The lights in the background will turn into beautiful, soft, colorful highlights.  
  8. Selective Focus and Macro Photography: Macro photography is close-up pictures of small objects. It's called selective focus. Then, highlight specific details. The pictures usually have a blurry background. You can create a dreamy bokeh background by focusing selectively on a small part of your subject.  

   

In conclusion, Bokeh plotting represents a dynamic and compelling approach to data visualization. Its interactivity, customization, and versatility make it an essential tool for conveying insights. It enables a deeper exploration of data appealingly and interactively. Bokeh's unique attributes position it as a cornerstone in data visualization. It helps to unlock the stories hidden within datasets and facilitates informed decision-making.  

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 plot not showing

PythondotLines of Code : 23dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
from bokeh.plotting import figure, show, output_notebook, output_file
from bokeh.models import ColumnDataSource, Range1d
from bokeh.core.properties import value
import pandas as pd

df = pd.DataFrame({'Org': ['o1', 'o1','o1','o1', 'o2','o2','o2','o2','o3','o3','o3','o3'],
                   'Task': ['t1', 't2','t3','t4', 't1', 't2','t3','t4','t1', 't2','t3','t4'],
                   'Start': pd.date_range('2019-02-24', periods=4*3, freq='D'),
                   'Finish': pd.date_range('2019-03-24', periods=4*3, freq='D')})
df['Diff'] = df['Finish'] - df['Start']

x = df['Task'].unique().tolist()
y = df['Org'].unique().tolist()

data1 = {'y': y}
data2 = {t: [df['Diff'][j + i % 4] for j in range(len(y))] for i, t in enumerate(x)}
data = {**data1, **data2}

p = figure(title='Project Schedule', y_range = y, x_axis_type='datetime')
p.hbar_stack(x, y='y', height=0.9, color=['red', 'blue', 'orange', 'green'], legend=[value(k) for k in x], source=data)

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. 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 plot not showing' 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 v1.3.0 Version


Using this solution, we can be able to use bokeh plotting 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 use bokeh plotting 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 are the key plotting libraries used for creating Bokeh charts?   

                      Bokeh is a strong Python library for visualizing data with many types of charts. While Bokeh provides its plotting functions. It is often combined with other plotting libraries to make more visualizations.  

                      You can use these libraries with Bokeh to create a wide range of interactive. Each library has strengths and use cases, so the choice depends on your needs.  

                        

                      2. How do you create HTML output with Bokeh charts?   

                      Bokeh provides the capability to generate interactive HTML output for the charts. This allows you to share your interactive plots with others through web browsers.  

                      This process demonstrates the basic steps to create an HTML output with a simple Bokeh chart. You can customize the chart, add more visual elements, and incorporate interactive features. Those are tooltips, hover effects, and zooming to create more engaging and informative.  

                        

                      3. Can I create a bar chart using the Python Library to Create Interactive plots?   

                      Yes, creating an interactive bar chart using Python libraries is possible. One such library is Bokeh, which allows you to create dynamic and appealing bar charts.  

                        

                      4. What is a Bokeh figure, and how can I use it in my project?   

                      A Bokeh is a fundamental component used to create and configure visualizations. It acts as a canvas where you add visual elements, such as lines, bars, scatter plots, and more. The figure allows you to control the appearance and behavior of the visualization.  

                        

                      5. How does Python Bokeh help developers build interactive visualizations quickly and easily?  

                      Python Bokeh simplifies the process of building visualizations by providing a high level.  

                      • High-Level and Concise Syntax: Bokeh designed its API to be intuitive and easy. Developers can create complex visualizations with few lines of code, making it straightforward.  
                      • Pythonic Approach: Bokeh is a Python library, which means developers can leverage it. This eliminates the need to learn a new language or framework for data visualization.  
                      • Wide Range of Charts: Bokeh offers a variety of pre-built chart types, including charts. Developers can create these charts with a few lines of code, saving time and effort.  
                      • Interactive Widgets: Bokeh provides various interactive widgets, such as sliders and dropdowns. These widgets allow users to control and customize the visualization.  

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