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NumPy VS Greasy Fork

Compare NumPy VS Greasy Fork and see what are their differences

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

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Greasy Fork logo Greasy Fork

A site for user scripts.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Greasy Fork Landing page
    Landing page //
    2022-01-22

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.

Greasy Fork features and specs

  • Wide Selection of Scripts
    Greasy Fork hosts a large variety of user scripts that cater to many different needs and interests, allowing users to customize their web browsing experience.
  • Open Source and Community-Driven
    The platform leverages an open-source approach, enabling users to contribute and modify scripts, fostering a collaborative and community-driven environment.
  • Ease of Use
    The website is user-friendly and straightforward, making it easy to browse, search, and install scripts directly onto supported browsers.
  • Free to Use
    Greasy Fork provides all its scripts for free, making it accessible without any financial barriers to entry.
  • No Sign-up Required for Download
    Users can download and use scripts without needing to create an account, simplifying the process and enhancing user privacy.

Possible disadvantages of Greasy Fork

  • Quality Variability
    Given the open nature of submissions, the quality and reliability of scripts can vary greatly, which may lead to security vulnerabilities or inconsistent performance.
  • Lack of Moderation
    Scripts are not always rigorously vetted, potentially allowing malicious or poorly-written scripts to be available on the platform.
  • Dependence on Browser Extensions
    Users need to install browser extensions like Tampermonkey or Greasemonkey to use the scripts, which might not appeal to people who prefer fewer extensions.
  • Limited Support for Non-Desktop Browsers
    The effectiveness of scripts on mobile browsers is not guaranteed, as they primarily target desktop environments, limiting functionality for mobile users.
  • Community-Driven Support
    Support largely comes from the community or script authors, which might not be as reliable or timely as professional support services.

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.

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

Greasy Fork videos

+ Add

moonlight feels right

Category Popularity

0-100% (relative to NumPy and Greasy Fork)

NumPy

Greasy Fork

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Browser Extensions
0 0%
Browser Extensions
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Dark Mode
0 0%
Dark Mode
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 NumPy and Greasy Fork

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

Greasy Fork Reviews

We have no reviews of Greasy Fork yet.
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Social recommendations and mentions

Based on our record, NumPy should be more popular than Greasy Fork. It has been mentiond 122 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.

NumPy mentions (122)

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Greasy Fork mentions (29)

  • [Announcement] c.ai+ LABS New Feature: Creative Mode!
    Have tampermonkey installed (google), then go to greasyfork (website) I have the link here https://greasyfork.org/en and search up character ai, have fun :)). Source: about 3 years ago
  • How can I make a site always redirect to something else
    If the above mentioned URL rewriter doesn't work for you (I found it hard to use myself, and never could get the rules figured out), then you could try using https://github.com/janekptacijarabaci/greasemonkey and finding a redirect script here: https://greasyfork.org/en. Source: about 3 years ago
  • Recent arc update in a nutshell
    I was thinking more greasemonkey / userscripts. Source: about 3 years ago
  • Mozilla removes Bypass Paywalls Clean extension from its add-ons repository
    Https://greasyfork.org/en is sort of what you're looking for. - Source: Hacker News / over 3 years ago
  • Youtube player functions
    Then you should rather look for simple userscripts on for example https://greasyfork.org/en then use them or convert to uBO scriptlet syntax (which should be easy). Source: over 3 years ago
View more

What are some alternatives?

When comparing NumPy and Greasy Fork, 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.

Violentmonkey - Violentmonkey is a userscript manager to support running userscripts in web pages.

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

Database Script Tool - Database Script Tool is an all-in-one functional code generator that allows you to generate several types of code, including SQL standard commands, classes, resource files, HTML 5 forms, Data managers, and more to add.

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

Userscripts - An open-source userscript editor for Safari.

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