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Matplotlib VS yEd

Compare Matplotlib VS yEd and see what are their differences

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

Matplotlib logo Matplotlib

matplotlib is a python 2D plotting library which produces publication quality figures in a variety...

yEd logo yEd

yEd is a free desktop application to quickly create, import, edit, and automatically arrange diagrams. It runs on Windows, Mac OS X, and Unix/Linux.
  • Matplotlib Landing page
    Landing page //
    2023-06-14
  • yEd Landing page
    Landing page //
    2022-07-16

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.

yEd features and specs

  • User-Friendly Interface
    yEd offers a clean, intuitive interface that makes it easy for users to get started and create diagrams without a steep learning curve.
  • Versatile Diagram Types
    The software supports a wide range of diagram types including flowcharts, UML diagrams, network diagrams, and more, making it versatile for different needs.
  • Automatic Layouts
    yEd provides several powerful automatic layout algorithms that can quickly arrange complex diagrams into clear structures.
  • Cross-Platform
    yEd is compatible with multiple operating systems such as Windows, macOS, and Linux, providing flexibility for users across different platforms.
  • Free to Use
    yEd is free to download and use, which makes it an attractive option for individuals and organizations with budget constraints.

Possible disadvantages of yEd

  • Limited Collaboration Features
    yEd lacks built-in real-time collaboration features, which can be a disadvantage for teams needing to work simultaneously on the same diagram.
  • No Mobile Version
    There is no mobile version of yEd, which limits its usability for users who prefer creating diagrams on tablets or smartphones.
  • Steep Learning Curve for Advanced Features
    While the basic features are user-friendly, some of the more advanced functionalities can have a steep learning curve and may require time to master.
  • Limited Integration Options
    yEd does not offer extensive integration options with other productivity tools or software, which can be a drawback for users looking for a more connected workflow.
  • Occasional Performance Issues
    Users have reported occasional performance issues, especially when dealing with very large and complex diagrams.

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.

Analysis of yEd

Overall verdict

  • yEd is a good choice for users looking for a robust and versatile diagramming solution. Its free availability and rich features make it a strong contender among diagramming tools.

Why this product is good

  • yEd is considered a powerful diagramming tool because it offers an extensive range of features like automatic layout algorithms, various diagram types, easy-to-use interface, and cross-platform compatibility. It is especially appreciated for its ability to handle large data sets and produce clear, understandable visual representations quickly.

Recommended for

  • Business professionals who need to create organizational charts or flowcharts
  • Software developers who design complex system architectures
  • Researchers and analysts visualizing large data sets
  • Educators preparing educational materials
  • Students managing complex information for projects

Matplotlib videos

+ Add

Learn Matplotlib in 6 minutes | Matplotlib Python Tutorial

yEd videos

+ Add

yEd Graph Editor in 90 seconds

More videos:

  • Tutorial - yED Graph Editor Tutorial - Make flowcharts, trees, graph Freeware.

Category Popularity

0-100% (relative to Matplotlib and yEd)

Matplotlib

yEd

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Diagrams
0 0%
Diagrams
100% 100
Technical Computing
100 100%
Technical Computing
0% 0
Flowcharts
0 0%
Flowcharts
100% 100

User comments

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Reviews

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

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

yEd Reviews

Best 7 Free Online XMind Alternatives for Windows
Another excellent tool that you can use for common and complex visual illustration is yEd. This aims to help users in terms of creating diagrams like UML, flowcharts, network diagram, org chart, and other process illustrations. With this XMind free alternative, you will find every icon and symbol you need for the aforementioned diagrams. On the other hand, users are...
Source: gitmind.com
40 Open Source, Free and Top Unified Modeling Language (UML) Tools
yEd is a desktop application that can be used to quickly and effectively generate high-quality diagrams. Users can create diagrams manually, or import their external data for analysis. yEdโ€™s automatic layout algorithms arranges even large data sets with just the press of a button. yEd is freely available and runs on all major platforms: Windows, Unix/Linux, and Mac OS X.With...
Source: www.predictiveanalyticstoday.com

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.

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 / 4 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 / 7 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 / 7 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 / 9 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 / 10 months ago
View more

yEd mentions (0)

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

What are some alternatives?

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

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

draw.io - Online diagramming application

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

LucidChart - LucidChart is the missing link in online productivity suites. LucidChart allows users to create, collaborate on, and publish attractive flowcharts and other diagrams from a web browser.

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

OmniGraffle - OmniGraffle is for creating precise graphics like website wireframes, an electrical system designs, or mapping out software class.

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