Overview

Matplotlib

Stacks1.6K

Followers336

Votes11

JFreeChart

Stacks47

Followers23

Votes2

GitHub Stars1.3K

Forks569

JFreeChart vs Matplotlib: What are the differences?

Introduction:

JFreeChart and Matplotlib are both powerful libraries used for data visualization in Java and Python respectively. While they serve a similar purpose of creating charts and graphs, there are key differences between the two.

1. JFreeChart: Feature Rich Charting Library JFreeChart is a feature-rich charting library for the Java programming language. It offers a wide range of chart types, including line charts, bar charts, pie charts, scatter plots, and more. JFreeChart supports interactive charts with tooltips, zooming, and panning features. It also provides extensive configuration options for customizing chart appearance, including different color schemes, legends, and axes labeling.

2. Matplotlib: Widely Used Data Visualization Library Matplotlib is a widely used data visualization library for Python. It provides a comprehensive set of plotting tools for creating a variety of static, animated, and interactive plots. Matplotlib offers a simple and intuitive API, allowing users to quickly generate high-quality visualizations. It supports various types of charts, such as line plots, scatter plots, bar charts, histograms, and more. Matplotlib can also be used in conjunction with other Python libraries like NumPy and pandas for advanced data analysis and visualization.

3. JFreeChart: Java-based Approach JFreeChart is written in Java and designed to be used in Java-based applications. It leverages the capabilities of the Java platform and integrates seamlessly with other Java libraries and frameworks. JFreeChart provides native support for Java Swing and JavaFX, making it easy to embed charts in desktop applications or JavaFX-based user interfaces. It also offers support for server-side rendering, allowing charts to be generated and exported as image files for web applications.

4. Matplotlib: Pythonic Design Matplotlib is built on top of the Python programming language and follows a Pythonic design philosophy. It aligns well with the syntax and conventions of Python, making it easy for Python developers to use and understand. Matplotlib supports various output formats like PNG, PDF, SVG, and EPS, providing flexibility in generating publication-quality plots. It also integrates well with Jupyter notebooks, a popular tool for interactive data analysis and visualization.

5. JFreeChart: Widely Used in Enterprise Applications JFreeChart has been widely adopted in enterprise environments for its robustness and scalability. It is often used in business applications, financial systems, and scientific research projects where the requirements for data visualization are complex. JFreeChart provides advanced features like chart overlays, data point annotations, multiple axes, and combination charts, which are essential for producing sophisticated visualizations in enterprise scenarios.

6. Matplotlib: Extensive Community and Ecosystem Matplotlib benefits from a large and active community of developers and users. It has a vast ecosystem of packages and extensions that extend its functionality and enable integration with other libraries. Matplotlib is part of the SciPy ecosystem, a collection of open-source libraries for scientific computing in Python. This ecosystem provides access to advanced mathematical and statistical functions, enhancing the analytical capabilities of Matplotlib. The active community ensures regular updates, bug fixes, and new features, making Matplotlib a vibrant and reliable choice for data visualization in Python.

In summary, while JFreeChart and Matplotlib excel at data visualization, JFreeChart is a feature-rich Java library often used in enterprise applications, while Matplotlib is a widely used Python library with a Pythonic design and an extensive ecosystem.

🔥 Trending in UI Components on StackShare

FigForm UI Components

FigForm

Try it View Docs Alternatives

Try

1

2

Detailed Comparison

Matplotlib	JFreeChart
It is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. It can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.	It is a free Java chart library that makes it easy for developers to display professional quality charts in their applications. It has a consistent and well-documented API, supporting a wide range of chart types.
-	Flexible design that is easy to extend, and targets both server-side and client-side applications; Support for many output types, including Swing and JavaFX components, image files (including PNG and JPEG), and vector graphics file formats (including PDF, EPS and SVG); open source or, more specifically, free software
Statistics
GitHub Stars -	GitHub Stars 1.3K
GitHub Forks -	GitHub Forks 569
Stacks 1.6K	Stacks 47
Followers 336	Followers 23
Votes 11	Votes 2
Pros & Cons
Pros 11 The standard Swiss Army Knife of plotting Cons 5 Lots of code	Pros 1 Very, very customizable 1 Easy to use 0 Easy to user Cons 1 Lots of code
Integrations
No integrations available	Java JavaFX

What are some alternatives to Matplotlib, JFreeChart?

D3.js

It is a JavaScript library for manipulating documents based on data. Emphasises on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework.

Highcharts

Highcharts currently supports line, spline, area, areaspline, column, bar, pie, scatter, angular gauges, arearange, areasplinerange, columnrange, bubble, box plot, error bars, funnel, waterfall and polar chart types.

Plotly.js

It is a standalone Javascript data visualization library, and it also powers the Python and R modules named plotly in those respective ecosystems (referred to as Plotly.py and Plotly.R). It can be used to produce dozens of chart types and visualizations, including statistical charts, 3D graphs, scientific charts, SVG and tile maps, financial charts and more.

Chart.js

Visualize your data in 6 different ways. Each of them animated, with a load of customisation options and interactivity extensions.

Recharts

Quickly build your charts with decoupled, reusable React components. Built on top of SVG elements with a lightweight dependency on D3 submodules.

ECharts

It is an open source visualization library implemented in JavaScript, runs smoothly on PCs and mobile devices, and is compatible with most current browsers.

ZingChart

The most feature-rich, fully customizable JavaScript charting library available used by start-ups and the Fortune 100 alike.

amCharts

amCharts is an advanced charting library that will suit any data visualization need. Our charting solution include Column, Bar, Line, Area, Step, Step without risers, Smoothed line, Candlestick, OHLC, Pie/Donut, Radar/ Polar, XY/Scatter/Bubble, Bullet, Funnel/Pyramid charts as well as Gauges.

CanvasJS

Lightweight, Beautiful & Responsive Charts that make your dashboards fly even with millions of data points! Self-Hosted, Secure & Scalable charts that render across devices.

AnyChart

AnyChart is a flexible JavaScript (HTML5) based solution that allows you to create interactive and great looking charts. It is a cross-browser and cross-platform charting solution intended for everybody who deals with creation of dashboard, reporting, analytics, statistical, financial or any other data visualization solutions.