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How to use the scatter method of plotting in Bokeh

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by vsasikalabe dot icon Updated: Aug 29, 2023

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Bokeh is an introduced Python library like D3.js. Web browsers use it for interactive data visualization. The scatter() function creates a basic scatter plot in Bokeh. 


Users can choose the data to plot, pick colors and markers, and set other plot properties. Bokeh can plot hexagonal tiles, often used to show binned aggregations. The hex_tile() method takes a size parameter. It is to define the size of the hex grid and axial coordinates to specify which tiles are present. The best feature that Bokeh provides is interactive graphs and plots. It targets modern web browsers for presentations. HoloViews is a high-level plotting library. It creates interactive plots with simple syntax and minimal effort.   

   

To make a scatter chart with more than one legend, we should use the circle to figure objects. The ColumnDataSource() function converts the data into a format accepted by Python bokeh. Since this is a stand-alone HTML page, it references BokehJS. It can be passed to a co-worker for exploration or posted online. You can see various visualizations and decide how to apply these techniques to your data. The main purpose of the Bokeh server is to synchronize Python objects. To make web apps that connect to PyData libraries such as NumPy, SciPy, Pandas, and sci-kit-learn.   

   

Bokeh goes as far as describing itself as an interactive visualization library. Bokeh makes elegant and concise charts with various charts. Bokeh uses JavaScript to render visual graphics. It makes a great option for building web-based dashboards and applications. (Unlike its counterparts Seaborn, matplotlib, and ggplot.) It helps you build beautiful graphics. (Ranging from simple plots to complex dashboards with streaming datasets.) Bokeh is a very interactive library. It provides interactivity functionality to the graphs and also to static plots.   


from Bokeh.plotting import figure   


The Bokeh.plotting module uses the figure class to create a new plot. Consider the 'data' to be the random y values. We measured those data values at the x positions. We must make a from our grouped data and create a. Since our x-axis will list the five countries. We need to tell the figure how to handle the x-axis. The main purpose of the Bokeh server is to synchronize Python objects. Web applications in a browser accomplished this.  

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

Code

In this solution, we used the Bokeh library.

Cannot import name 'Scatter' from 'bokeh.plotting'

PythondotLines of Code : 29dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
from bokeh.plotting import figure, show
import pandas as pd

df = pd.DataFrame({"X" :[1,2,3,4,5,6,7],
                   "Y": [23,43,32,12,34,54,33]})

p = figure(x_axis_label="Tempetature", y_axis_label="Day", 
           title="Day Temperature measurement")
p.circle("X", "Y", size=15, source=df)

show(p)

from bokeh.plotting import figure, show
from bokeh.sampledata.iris import flowers
from bokeh.transform import factor_cmap, factor_mark

SPECIES = ['setosa', 'versicolor', 'virginica']
MARKERS = ['hex', 'circle_x', 'triangle']

p = figure(title = "Iris Morphology")
p.xaxis.axis_label = 'Petal Length'
p.yaxis.axis_label = 'Sepal Width'

p.scatter("petal_length", "sepal_width", source=flowers, legend_field="species", fill_alpha=0.4, size=12,
          marker=factor_mark('species', MARKERS, SPECIES),
          color=factor_cmap('species', 'Category10_3', SPECIES))

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. 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 ' Cannot import name 'Scatter' from 'bokeh.plotting'' 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 3.2.1


Using this solution, we are able to use the scatter method of plotting in Bokeh 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 the scatter method of plotting in Bokeh.

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 a Bokeh scatter plot, and how does it differ from other scatter plots?   

                      Scatter plots are like line graphs. They start with mapping quantitative data points, and the difference is that with a scatter plot, as given below.  

                      They decide that the individual points should not be together with a line. But instead, express a trend.   

                         

                      2. What are some of the benefits of using interactive graphs?   

                      A chart you can engage with allows a better understanding of whatever topic. We present this because it can organize data, make hierarchies, and use them in reports. It makes the documents more interesting and easier to read. It allows viewers to see trends.   

                         

                      3. Can I use a bar chart instead of a scatter plot for data visualization?   

                      Avoid a scatter plot when your data is not at all related. These two variables have no relationship. While they can still be fun to graph, a bar chart (one for each data value) might be the better choice here.   

                         

                      4. Are there any limitations when creating basic charts with the Bokeh library?   

                      Visualization can have a limited degree of interactivity. Bokeh enables you to “chart” in the more conventional sense. It offers a 2-D, grid-like canvas with axes as the baseline.   

                         

                      5. How do you create a basic Bokeh scatter plot with the Python library?  

                      • ## Scatter Plot.   
                      • import bokeh.   
                      • from Bokeh.plotting import figure, output_notebook, show.   
                      • from random import seed.   
                      • from random import randint.   
                      • seed(1)   
                      • x_value=[]   
                      • y_value=[]  

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