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Matplotlib VS Parcel

Compare Matplotlib VS Parcel 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...

Parcel logo Parcel

Blazing fast, zero configuration web application bundler
  • Matplotlib Landing page
    Landing page //
    2023-06-14
  • Parcel Landing page
    Landing page //
    2021-12-13

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.

Parcel features and specs

  • Zero Configuration
    Parcel requires minimal to no configuration to get started, making it extremely user-friendly, especially for beginners or small projects.
  • Fast Bundling
    Parcel uses worker threads to parallelize tasks, which significantly speeds up the bundling process compared to other bundlers that do not use this approach.
  • Out-of-the-box support for many file types
    Parcel supports many file types (e.g., JavaScript, CSS, HTML, images) right out-of-the-box without needing additional plugins or configurations.
  • Hot Module Replacement (HMR)
    Parcel offers built-in HMR, allowing developers to see changes in real-time without needing to refresh the browser, leading to a faster development cycle.
  • Tree Shaking
    Parcel automatically performs tree shaking, removing unused code from the production build to reduce file sizes, which can improve loading times.
  • Code Splitting
    Parcel has automatic code splitting capabilities which help to improve performance by loading only the necessary assets.
  • Extensible via Plugins
    Parcelโ€™s plugin system allows developers to extend its functionality easily if custom or additional features are needed.

Possible disadvantages of Parcel

  • Community and Ecosystem
    The community and ecosystem around Parcel are smaller compared to other bundlers like Webpack, so finding solutions and third-party plugins might be more challenging.
  • Limited Customization
    While the zero-config aspect is beneficial, it also means there are fewer customization options out-of-the-box, which might be limiting for complex projects needing specific configurations.
  • Performance with Large Projects
    For very large projects, Parcel's performance can become a bottleneck, particularly when it comes to initial build times.
  • Documentation
    The documentation, while improving, is not as comprehensive as some other tools, making it harder for developers to find detailed information when they encounter issues.
  • Dependency Bloat
    Parcel can sometimes include more dependencies than necessary in the final bundle, potentially increasing the final bundle size.

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 Parcel

Overall verdict

  • Parcel is a good choice for developers looking for a hassle-free, efficient, and beginner-friendly bundler. Its minimal configuration approach and speed make it ideal for small to medium-sized projects. However, for highly complex projects that require intricate and highly customized build processes, other bundlers might be more suitable due to their advanced configuration capabilities.

Why this product is good

  • Parcel is a web application bundler that is appreciated for its simplicity and zero-config philosophy. It automatically detects the files needed for a project without requiring a complex configuration file. Its fast performance is attributed to parallelization and efficient caching. Additionally, Parcel offers out-of-the-box support for JavaScript, CSS, HTML, asset management, and various types of file transformations, making it a versatile tool for web developers.

Recommended for

  • Developers new to module bundlers or looking for an easy-to-setup tool.
  • Projects that value speed and simplicity in their build processes.
  • Developers who need a bundler capable of handling multiple asset types with minimal configuration.
  • Teams that prefer convention over configuration and want to get started quickly without diving deep into complex bundler settings.

Matplotlib videos

+ Add

Learn Matplotlib in 6 minutes | Matplotlib Python Tutorial

Parcel videos

+ Add

Danby Parcel Guard Smart Mailbox blogger Review

More videos:

  • Review - PARCEL MOVIE REVIEW | SASWATA CHATTERJEE | RITUPARNA SENGUPTA | RUPAM'S REVIEW
  • Review - Le Parcel Box review

Category Popularity

0-100% (relative to Matplotlib and Parcel)

Matplotlib

Parcel

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Web Application Bundler
0 0%
Web Application Bundler
100% 100
Technical Computing
100 100%
Technical Computing
0% 0
JS Build Tools
0 0%
JS Build Tools
100% 100

User comments

Share your experience with using Matplotlib and Parcel. 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 Matplotlib and Parcel

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

Parcel Reviews

Rollup v. Webpack v. Parcel
Parcel's caching feature sees dramatically decreases in time consumption after the initial run. For frequent, small changes, in smaller projects **Parcel*8 is a great choice.
Source: x-team.com
If youโ€™ve ever configured Webpack, Parcel will blow yourย mind!
document.body.className = document.body.className.replace(/(^|\s)is-noJs(\s|$)/, "$1is-js$2")HomepageHomepageJavascriptBecome a memberSign inGet startedIf youโ€™ve ever configured Webpack, Parcel will blow your mind!And how to hit the ground running with Parcel.Ibrahim ButtBlockedUnblockFollowFollowingMar 16, 2018Click here to share this article on LinkedIn ยปZero...
Source: medium.com
First impressions with Parcelย JS
The big selling point of Parcel though is that it offers a zero configuration experience. This means all the features are available out of the box! It also boasts blazing fast bundle times ๐Ÿ‘Ÿ You wonโ€™t be configuring how Parcel works or having to draft in various plugins to get started. If you do need something, Parcel magically detects this and will pull in stuff for you on...
Source: codeburst.io
Parcel vs webpack - Jakob Lind
Parcel has made their own benchmarks of Parcel and other bundlers. Parcel has been criticized because they have not made the benchmarks open source. People cannot verify that the benchmarks are true when they are not open source.
Source: blog.jakoblind.no

Social recommendations and mentions

Parcel might be a bit more popular than Matplotlib. We know about 115 links to it since March 2021 and only 114 links to Matplotlib. 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 / 8 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

Parcel mentions (115)

  • JavaScript Awesome Package
    Parcel - Blazing fast, zero configuration web application bundler. - Source: dev.to / 6 months ago
  • Nix + pnpm + Parcel + lydell/elm-safe-virtual-dom
    Pnpm and Parcel are used to build the application in nix/app.nix. - Source: dev.to / 6 months ago
  • Migrating a JavaScript Project from Prettier and ESLint to BiomeJS
    Https://parceljs.org/ is another. It even supports languages like `` out of the box which is pretty cool. IIRC it downloads necessarily plugins on the fly. - Source: Hacker News / about 1 year ago
  • Create React App is Deprecated โ€“ Whatโ€™s Next ?
    Parcel is another alternative that requires zero configuration and is super fast. If you want a simple React setup without any hassle, Parcel is a great choice. - Source: dev.to / over 1 year ago
  • Bun 1.2 Is Released
    From its documentation [1] it looks a lot like a parceljs replacement [2], i.e. a zero config bundler which processes and bundles the dependencies in .html pages. So great for simple websites, not for replacing an entire Vite stack. [1] https://bun.sh/docs/bundler/fullstack [2] https://parceljs.org. - Source: Hacker News / over 1 year ago
View more

What are some alternatives?

When comparing Matplotlib and Parcel, 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.

Webpack - Webpack is a module bundler. Its main purpose is to bundle JavaScript files for usage in a browser, yet it is also capable of transforming, bundling, or packaging just about any resource or asset.

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

rollup.js - Rollup is a module bundler for JavaScript which compiles small pieces of code into a larger piece such as application.

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

esbuild - An extremely fast JavaScript bundler and minifier

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