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Highcharts VS Matplotlib

Compare Highcharts VS Matplotlib and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Highcharts logo Highcharts

A charting library written in pure JavaScript, offering an easy way of adding interactive charts to your web site or web application

Matplotlib logo Matplotlib

matplotlib is a python 2D plotting library which produces publication quality figures in a variety...
  • Highcharts Landing page
    Landing page //
    2023-03-16
  • Matplotlib Landing page
    Landing page //
    2023-06-14

Highcharts features and specs

  • Customization
    Highcharts provides extensive options to customize chart appearance and functionality, allowing for a tailored and specific data visualization experience.
  • Cross-Browser Compatibility
    Highcharts ensures compatibility across a wide range of browsers, making charts accessible to users regardless of their browser preferences.
  • Wide Range of Chart Types
    Offers a broad spectrum of chart types, including line, bar, pie, scatter, and more, catering to various data visualization needs.
  • Interactive Features
    Includes numerous interactive features such as tooltips, zooming, and clickable points, enhancing user engagement with the data.
  • Strong Community and Support
    Has an active community and provides extensive documentation, forums, and professional support options to assist users in overcoming challenges.
  • Performance
    Optimized for high performance, allowing for the rendering of large datasets without significant lag or performance issues.
  • Exporting and Sharing
    Built-in options for exporting charts to various formats (PNG, JPEG, PDF, etc.) and sharing them easily.

Possible disadvantages of Highcharts

  • Cost
    Highcharts is not free for commercial use, which may be a drawback for small businesses or individual developers with limited budgets.
  • Steep Learning Curve
    Despite comprehensive documentation, the abundance of features and customization options can result in a steeper learning curve for new users.
  • Dependency on JavaScript
    As a JavaScript library, Highcharts requires a solid understanding of JavaScript, making it less accessible for developers not familiar with the language.
  • Limited Free Support
    While there is a free support forum, professional support options are paid, which can be a limitation for users needing urgent assistance without extra costs.
  • Mobile Responsiveness
    Although Highcharts provides some support for mobile responsiveness, achieving optimal performance and displays on all device types may require additional customization.

Matplotlib features and specs

  • Versatility
    Matplotlib can generate a wide variety of plots, ranging from simple line plots to complex 3D plots. This versatility makes it a go-to library for many scientific and technical visualizations.
  • Customization
    It offers extensive customization options for virtually every element of a plot, including colors, labels, line styles, and more, allowing users to tailor plots to meet specific needs.
  • Integrations
    Matplotlib integrates well with other Python libraries such as NumPy, Pandas, and SciPy, making it easier to plot data directly from these sources.
  • Community and Documentation
    It has a large, active community and comprehensive documentation that includes tutorials, examples, and detailed references, which can help users solve problems and improve their plot-making skills.
  • Interactivity
    Matplotlib supports interactive plots, which can be embedded in Jupyter notebooks and GUIs, allowing for dynamic data exploration and presentation.
  • Publication-Quality
    The library is capable of producing high-quality, publication-ready graphics that meet the stringent requirements of academic journals and professional presentations.

Possible disadvantages of Matplotlib

  • Complexity
    While Matplotlib offers extensive customization, it can be complex and sometimes unintuitive for beginners, requiring a steep learning curve to master all its functionality.
  • Performance
    Rendering a large number of plots or handling very large datasets can be slow, making Matplotlib less suitable for real-time data visualization.
  • Modern Aesthetics
    Out-of-the-box plots from Matplotlib can look somewhat dated compared to those from newer plotting libraries like Seaborn or Plotly, requiring additional customization to achieve a modern look.
  • 3D Plots
    Although Matplotlib supports 3D plotting, its capabilities are relatively limited and less sophisticated compared to specialized 3D plotting libraries.
  • Size and Structure
    The package is relatively large and can be slow to import. Its extensive structure can make finding specific functions and understanding the overall architecture challenging.

Analysis of Highcharts

Overall verdict

  • Highcharts is a strong choice for those seeking a robust and feature-rich charting library. Its popularity among developers and businesses stems from its reliability and comprehensive feature set.

Why this product is good

  • Highcharts is considered good because it offers a wide range of chart types and is highly customizable. It is known for its detailed documentation, ease of use, and cross-platform compatibility. Additionally, Highcharts provides extensive support and a variety of integrations with popular frameworks, making it a versatile choice for developers.

Recommended for

  • Developers looking for a user-friendly and customizable charting library.
  • Projects that require extensive interactive data visualizations.
  • Businesses that need a reliable and professionally supported charting solution.
  • Teams using web technologies and frameworks like Angular, React, or Vue for front-end development.

Analysis of Matplotlib

Overall verdict

  • Yes, Matplotlib is a good library for data visualization, particularly for users who require a versatile and powerful plotting solution in Python.

Why this product is good

  • Matplotlib is highly regarded due to its extensive customization options, versatility in creating a wide range of static, animated, and interactive plots, and its large user community and support. It integrates well with other scientific libraries in Python, making it a staple for data visualization. The library is also open-source and frequently updated, ensuring it remains a reliable choice for users.

Recommended for

  • Data scientists and analysts needing to create detailed, customized visual representations of their data.
  • Researchers and engineers looking for a comprehensive plotting library that supports scientific and engineering formats.
  • Python developers who require integration with other scientific computing libraries like NumPy and Pandas.

Highcharts videos

+ Add

Angular 2 & HighCharts Quick-Tip: Dynamic Data & Draggable Points (2016)

More videos:

  • Tutorial - How to define the custom colors for Highcharts?
  • Review - Data Visualization HighCharts

Matplotlib videos

+ Add

Learn Matplotlib in 6 minutes | Matplotlib Python Tutorial

Category Popularity

0-100% (relative to Highcharts and Matplotlib)

Highcharts

Matplotlib

Data Dashboard
72 72%
Data Dashboard
28% 28
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Charting Libraries
100 100%
Charting Libraries
0% 0
Technical Computing
0 0%
Technical Computing
100% 100

User comments

Share your experience with using Highcharts and Matplotlib. For example, how are they different and which one is better?
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Reviews

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

Highcharts Reviews

6 JavaScript Charting Libraries for Powerful Data Visualizations in 2023
However, you might need to pay for additional packages to get exactly what youโ€™re looking for. The Highcharts Core package includes all the essentials (like line, bar, area, and pie charts) but Maps, Gantt, and Stock chart packages are all extra. In terms of cost, this makes Highcharts somewhat less scalable, depending on the budget available for your project.
Source: embeddable.com
15 JavaScript Libraries for Creating Beautiful Charts
Highcharts is another very popular library for building graphs. It comes loaded with many different types of cool animations that are sufficient to attract many eyeballs to your website. Just like other libraries, Highcharts comes with many pre-built graphs like spline, area, areaspline, column, bar, pie, scatter, etc. The charts are responsive and mobile-ready. Besides,...
Source: www.sitepoint.com
Best Data Visualization Tools
For companies that want to embed interactive visualizations in their online content, look no further than Datawrapper. Highcharts is another great option for embedding interactive content into your sites, though itโ€™s not as easy for non-specialists as Datawrapper.
Source: neilpatel.com
Top 10 JavaScript Charting Libraries for Every Data Visualization Need
Highcharts is one of the most comprehensive and popular JavaScript charting libraries based on HTML5, rendering in SVG/VML. It is lightweight, supports a wide range of diverse chart types, and ensures high performance.
Source: hackernoon.com
The Best Data Visualization Tools - Top 30 BI Software
Highcharts is a battle-tested SVG-based, multi-platform charting library that has been actively developed since 2009. Its JavaScript API integrates easily, and features robust documentation, advanced responsiveness and industry-leading accessibility support. You can add interactive, mobile-optimized charts to your web and mobile projects. Charts are rendered in SVG and a VML...
Source: improvado.io

Matplotlib Reviews

25 Python Frameworks to Master
Matplotlib is a widely used tool for data visualization in Python. It provides an object-oriented API for embedding plots into applications.
Source: kinsta.com
5 Best Python Libraries For Data Visualization in 2023
You can use this library for multiple purposes such as generating plots, bar charts, histograms, power spectra, stemplots, pie charts, and more. The best thing about Matplotlib is you just have to write a few lines of code and it handles the rest by itself. Metaplotilib focuses on static images for publication along with interactive figures using toolkits like Qt and GTK.
Source: www.technotification.com
15 data science tools to consider using in 2021
Matplotlib is an open source Python plotting library that's used to read, import and visualize data in analytics applications. Data scientists and other users can create static, animated and interactive data visualizations with Matplotlib, using it in Python scripts, the Python and IPython shells, Jupyter Notebook, web application servers and various GUI toolkits.
Source: searchbusinessanalytics.techtarget.com
Top Python Libraries For Image Processing In 2021
Matplotlib is primarily used for 2D visualizations such as scatter plots, bar graphs, histograms, and many more, but we can also use it for image processing. It is effective to get information out of an image. It doesnโ€™t support all file formats.
Source: www.analyticsvidhya.com
Top 8 Python Libraries for Data Visualization
Matplotlib is a data visualization library and 2-D plotting library of Python It was initially released in 2003 and it is the most popular and widely-used plotting library in the Python community. It comes with an interactive environment across multiple platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application...
Source: www.geeksforgeeks.org

Social recommendations and mentions

Based on our record, Matplotlib seems to be more popular. It has been mentiond 114 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.

Highcharts mentions (0)

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

Matplotlib mentions (114)

  • The soul file
    In February, an AI agent named MJ Rathbun submitted a pull request to matplotlib โ€” the Python plotting library used by half the scientific computing world. Scott Shambaugh, a volunteer maintainer, rejected it. Standard code review. Nothing unusual. - Source: dev.to / 5 months ago
  • How to Analyze CSV Files with Python and Pandas
    Numbers are useful, but sometimes itโ€™s easier to spot patterns when you can actually see your data. Pandas works seamlessly with Matplotlib, a popular Python library for creating visualizations. Together, they make it easy to turn raw numbers into clear charts. - Source: dev.to / 8 months ago
  • libmalloc, jemalloc, tcmalloc, mimalloc - Exploring Different Memory Allocators
    We are storing the results in JSON files, which we combine, analyze and visualize using matplotlib in Python. Here's the structure of a benchmark result file:. - Source: dev.to / 9 months ago
  • Building an AI Scoring Agent: Step-By-Step
    NetworkX and Matplotlib were used to visualize the graph structure of the agent. - Source: dev.to / 10 months ago
  • Top 5 GitHub Repositories for Data Science in 2026
    The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, Aโ€ฆ. - Source: dev.to / 11 months ago
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What are some alternatives?

When comparing Highcharts and Matplotlib, you can also consider the following products

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.

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

Chart.js - Easy, object oriented client side graphs for designers and developers.

NumPy - NumPy is the fundamental package for scientific computing with Python

Google Charts - Interactive charts for browsers and mobile devices.

Seaborn - Seaborn is a Python data visualization library that uses Matplotlib to make statistical graphics.

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