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Parcel VS NumPy

Compare Parcel VS NumPy and see what are their differences

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

Parcel logo Parcel

Blazing fast, zero configuration web application bundler

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Parcel Landing page
    Landing page //
    2021-12-13
  • NumPy Landing page
    Landing page //
    2023-05-13

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.

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

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.

Analysis of NumPy

Overall verdict

  • Yes, NumPy is considered good. It is a foundational library in the Python ecosystem for numerical computing and is used globally by researchers, engineers, and data scientists.

Why this product is good

  • NumPy is widely regarded as a good library because it offers fast, flexible, and efficient array handling that is integral to scientific computing in Python. It provides tools for integrating C/C++ and Fortran code, useful linear algebra, random number capabilities, and a vast collection of mathematical functions. Its array broadcasting capabilities and versatility make complex mathematical computations straightforward.

Recommended for

  • Scientists and researchers working with large-scale scientific computations.
  • Data scientists engaged in data analysis and manipulation.
  • Engineers and developers needing performance-optimized mathematical computations.
  • Educators and students in STEM fields.

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

NumPy videos

+ Add

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

Category Popularity

0-100% (relative to Parcel and NumPy)

Parcel

NumPy

Web Application Bundler
100 100%
Web Application Bundler
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
JS Build Tools
100 100%
JS Build Tools
0% 0
Data Science Tools
0 0%
Data Science Tools
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 Parcel and NumPy

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

NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
Source: www.analyticsvidhya.com
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

Social recommendations and mentions

NumPy might be a bit more popular than Parcel. We know about 122 links to it since March 2021 and only 115 links to Parcel. 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.

Parcel mentions (115)

  • JavaScript Awesome Package
    Parcel - Blazing fast, zero configuration web application bundler. - Source: dev.to / 5 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 / 5 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
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NumPy mentions (122)

View more

What are some alternatives?

When comparing Parcel and NumPy, you can also consider the following products

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.

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

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

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

esbuild - An extremely fast JavaScript bundler and minifier

OpenCV - OpenCV is the world's biggest computer vision library

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