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Which is more popular?
Based on our record, Matplotlib
seems to be a lot more popular than Plotly.js.
While we know about 114 links to Matplotlib,
we've tracked only 4 mentions of Plotly.js.
social mentions
114 vs 4
Data Science And Machine Learning popularity
100% vs 0%
alternatives listed
239 vs 22
Base details
Website, pricing, platforms and company facts side by side.
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
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.
Interactive Visualizations Plotly.js provides highly interactive charting capabilities, allowing users to hover, zoom, and pan easily within charts, enhancing the user experience.
Wide Range of Chart Types The library supports a comprehensive variety of chart types, from simple line and bar charts to more complex types like histograms, scatter plots, and even 3D and geographic plots.
Cross-Platform Compatibility Plotly.js works seamlessly across different platforms and browsers, ensuring consistent chart rendering and functionality whether used on desktops or mobile devices.
Customizable Users have a high degree of control over the appearance and behavior of plots, with numerous options to customize colors, legends, tooltips, and more.
Integration with Dash Plotly.js integrates well with the Dash framework, enabling the creation of interactive web applications that are highly visual and data-driven.
Possible disadvantages
Performance Concerns Rendering very large datasets can be slow, potentially impacting the performance of the application, particularly in resource-constrained environments.
Complexity For users new to the library, the learning curve can be steep due to the extensive options and configurations available.
Size of Library Plotly.js has a relatively large file size, which can be a concern for web applications where minimizing load times and data transfer is critical.
Limited Free Features Certain advanced features and functionalities may require a paid subscription to Plotly's services, which may not be ideal for all users or projects.
Dependency Management Managing dependencies and ensuring compatibility with other JavaScript libraries can sometimes pose challenges, especially in complex applications.
Analysis
An editorial look at what each product does well and who it suits.
MatplotlibPlotly.js
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.
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...
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...
Plotly.js is the first scientific JavaScript charting library for the web. It has been open-source since 2015, meaning anyone can use it for free. Plotly.js supports 20 chart types, including SVG maps, 3D charts, and...
Plotly.js is a high-level JavaScript library, free and open-source. It is built on D3.js and WebGL, so can be used to create many different chart types including 3D charts to statistical graphs.
Social recommendations and mentions
Recommendations tracked on public social media and blogs since March 2021.
Matplotlib114 mentionsPlotly.js4 mentions
The soul file
In February, an AI agent named MJ Rathbun submitted a pull request to matplotlib — the Python plotting library used by half the scientific computing world. Scott Shambaugh, a volunteer maintainer, rejected it. Standard code review....
- Source: dev.to
/
8 months ago
Numbers are useful, but sometimes it’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...
- Source: dev.to
/
11 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
/
12 months ago
Well, MathML[1] support is (nearly) everywhere now, and as the docs say: MathML Core is a subset with increased implementation details based on rules from LaTeX and the Open Font Format. It is tailored for browsers and designed...
- Source: Hacker News
/
over 3 years ago
Pandas
NumPy
Seaborn
D3.js
Plotly
GnuPlot
Jupyter
Highcharts
Chart.js
Google Charts
amCharts
ZingChart
AnyChart
