Bokeh VS LabPlot

Compare Bokeh VS LabPlot and see what are their differences

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Bokeh

Bokeh visualization library, documentation site.

LabPlot

LabPlot is a KDE-application for interactive graphing and analysis of scientific data.

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2022-11-01

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LabPlot is a FREE, open source and cross-platform Data Visualization and Analysis software accessible to everyone and trusted by professionals.

FEATURE HIGHLIGHTS

High-quality data visualization and interactive plotting
Data analysis, statistics, nonlinear regression, curve and peak fitting
Fast computing with interactive notebooks (for Python, R, Julia, Maxima and more)
Data extraction from images (Plot Digitizer)
Smooth data import and export to and from multiple formats (CSV, JSON, ODS, XLSX, Origin, SAS, Stata, SPSS, MATLAB, SQL, MQTT, BLF, HDF5, FITS, ROOT (CERN), LTspice, Ngspice and more)
Available for Windows, macOS, Linux, FreeBSD, Haiku, GNU

A full list of features: https://labplot.kde.org/features

Video tutorials: https://www.youtube.com/@LabPlot

Communication channels: https://labplot.kde.org/support

Get it here: https://labplot.kde.org/download

Bokeh

Website: docs.bokeh.org
$ Details: -
Release Date: -

Edit details

LabPlot

Website: labplot.org
$ Details
Release Date: 2024 July

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Bokeh features and specs

Interactive Visualizations
Bokeh is designed specifically for creating interactive and highly customizable visualizations, making it suitable for engaging data exploration.
Python Integration
Bokeh integrates well with the Python ecosystem, allowing direct use of pandas, NumPy, and other Python libraries, facilitating seamless data manipulation and visualization.
Web Compatibility
Bokeh generates plots that are ready to be embedded into web applications, making it a powerful tool for creating dashboards and interactive reports.
Server Functionality
Bokeh provides a server component that allows users to build and deploy sophisticated interactive applications using just Python.
Variety of Plotting Options
Bokeh offers a wide range of plotting capabilities including charts, maps, and streamgraphs, enabling users to create complex visual stories.

Possible disadvantages of Bokeh

Learning Curve
Bokeh may have a steeper learning curve for users unfamiliar with JavaScript or those looking for a very simple or quick plotting tool.
Performance Issues
When dealing with very large datasets, Bokeh might suffer from performance issues, as it is primarily client-side rendering.
Limited 3D Capabilities
Bokeh's support for 3D plotting is limited compared to other visualization libraries like Plotly, potentially restricting its use for applications that require 3D visualizations.
Documentation and Community Size
While Bokeh has good documentation, its user community is smaller compared to more mature libraries like Matplotlib, which can mean fewer resources and third-party support options.

LabPlot features and specs

Open Source
LabPlot is free and open source, allowing users to modify and distribute the software without any cost.
Integration with KDE
LabPlot is part of the KDE software collection, offering seamless integration with other KDE applications and a consistent look and feel.
Multiplatform Support
LabPlot is available on various platforms, including Linux, Windows, and macOS, making it accessible to a wide range of users.
Extensive Plotting Features
LabPlot offers a wide range of plotting capabilities, including 2D and 3D plots, which can accommodate diverse scientific and engineering needs.
Customizability
Users can customize plots extensively in LabPlot, adjusting parameters such as plot style, color, and data presentation to suit their specific needs.

Possible disadvantages of LabPlot

Steeper Learning Curve
Due to its comprehensive features, new users might find LabPlot challenging to learn and may require time to become proficient.
Limited Community Support
While there is a community around LabPlot, the size is relatively small compared to more widely used plotting tools, potentially limiting peer support.
Performance Issues with Large Datasets
LabPlot may experience performance slowdowns when handling very large datasets, which can hinder productivity for users working with such data.
Less Frequent Updates
LabPlot may receive updates less frequently than some commercial software, possibly affecting the pace of new feature integration.

Analysis of Bokeh

Overall verdict

Yes, Bokeh is a good choice for data visualization, particularly if you need to create interactive, high-quality plots that can be shared and displayed on the web.

Why this product is good

Bokeh is a powerful and interactive visualization library for Python that is known for its ability to create elegant, scalable, and versatile graphics. It is especially useful for creating web-ready, interactive plots that can be easily embedded into web pages or applications. Bokeh is praised for its intuitive and flexible interface, making it a great choice for both simple and complex visualizations.

Recommended for

Data scientists who need to create interactive visualizations for data exploration.
Web developers looking to incorporate dynamic plots into their applications.
Educators and researchers who need to present data interactively in a web-based format.
Anyone seeking a versatile tool compatible with various data formats and capable of producing real-time streaming plots.

Bokeh videos

+ Add

"Bokeh" - Netflix Film Review

LabPlot videos

+ Add

How to fit a curve using LabPlot

Category Popularity

0-100% (relative to Bokeh and LabPlot)

LabPlot

Charting Libraries

100 100%

Charting Libraries

0% 0

Technical Computing

0 0%

Technical Computing

100% 100

Data Dashboard

100 100%

Data Dashboard

0% 0

Office & Productivity

0 0%

Office & Productivity

100% 100

User comments

Share your experience with using Bokeh and LabPlot. For example, how are they different and which one is better?

Reviews

These are some of the external sources and on-site user reviews we've used to compare Bokeh and LabPlot

Bokeh Reviews

Top 8 Python Libraries for Data Visualization

Pygal is a Python data visualization library that is made for creating sexy charts! (According to their website!) While Pygal is similar to Plotly or Bokeh in that it creates data visualization charts that can be embedded into web pages and accessed using a web browser, a primary difference is that it can output charts in the form of SVG’s or Scalable Vector Graphics. These...

Source: www.geeksforgeeks.org

LabPlot Reviews

Mark

almost 2 years ago

LabPlot provides extensive capabilities for data import and export, along with tools for analysis, curve fitting, nonlinear regression and interactive visualization, including live data support. Users can export graphs in various formats and utilize a built-in plot digitizer to extract data from existing charts. Additionally, if users are familiar with programming languages such as Python or R, they can leverage these within LabPlot's interactive notebooks.

Social recommendations and mentions

Based on our record, Bokeh seems to be more popular. It has been mentiond 5 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Bokeh mentions (5)

[OC] Chemical Diversity of The GlobalChem Common Chemical Universe
Visualization: https://docs.bokeh.org/en/latest/. Source: over 4 years ago
Profiling workflows with the Amazon Genomics CLI
Now that we can get task timing information in a consistent manner, let’s do some plotting. For this, I’m going to use Bokeh which generates nice interactive plots. - Source: dev.to / over 4 years ago
10 Python Libraries For Data Visualization
Bokeh The Bokeh library is native to Python and is mainly used to create interactive, web-ready plots, which can be easily output as HTML documents, JSON objects, or interactive web applications. Like ggplot, its concepts are also based on the Grammar of Graphics. It has the added advantage of managing real-time data and streaming. This library can be used for creating common charts such as histograms, bar plots,... - Source: dev.to / over 4 years ago
Graphic library Bokeh is underrated and underdocumented
It's not in the least bit "underrated" and it's documentation is extensive. Source: about 5 years ago
Help with Bokeh Interactive Plot
Hi guys! I am currently working on a project to enrich my Master thesis with some interactive plots. I have been using the Bokeh library to make a standalone application, which I was then planning to deploy in Heroku. You can find the code in this repository. But I will also add it at the bottom of the post. Source: over 5 years ago

LabPlot mentions (0)

We have not tracked any mentions of LabPlot yet. Tracking of LabPlot recommendations started around Mar 2021.

What are some alternatives?

When comparing Bokeh and LabPlot, you can also consider the following products

Plotly - Low-Code Data Apps

SciDaVis - SciDAVis is a free application for Scientific Data Analysis and Visualization.

RAWGraphs - RAWGraphs is an open source app built with the goal of making the visualization of complex data...

RJS Graph - RJS Graph is an artificial intelligence-based data management platform that allows users or developers to organize the data by manipulating the binaries, scientific, mathematical, and other insights with accurate results.

D3.js - D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS.

OriginPro - OriginLab OriginPro is a comprehensive interface-based data management platform that allows users to calculate or visualize the data insights in various fields like engineering, scientific domain, or multi-sector industrial stats.