Software Alternatives, Accelerators & Startups
Register | Login

NumPy VS GitZip

Compare NumPy VS GitZip and see what are their differences

Flagsmith
Flagsmith lets you manage feature flags and remote config across web, mobile and server side applications. Deliver true Continuous Integration. Get builds out faster. Control who has access to new features. We're Open Source. featured
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

GitZip logo GitZip

Download or create a download link for a GitHub project folder/sub-folder or file.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • GitZip Landing page
    Landing page //
    2019-09-02

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.

GitZip features and specs

  • Selective Download
    GitZip allows users to download specific files or folders from a GitHub repository instead of cloning the entire repository, which is especially useful for large projects.
  • Ease of Use
    The extension provides a simple and intuitive interface to select and download files directly from GitHub, making it accessible for users with varying levels of technical expertise.
  • Browser Integration
    GitZip integrates directly with the browser, enabling users to download files without needing to switch to another tool.
  • Time Efficiency
    By allowing users to download only the necessary parts of a repository, GitZip helps in saving time that would otherwise be spent on downloading and processing unnecessary files.
  • Bandwidth Savings
    Avoiding the download of the entire repository helps in conserving bandwidth, particularly beneficial for users with limited internet resources.

Possible disadvantages of GitZip

  • Limited to GitHub
    GitZip is specifically designed for GitHub and does not support other Git hosting services, limiting its use to only GitHub repositories.
  • Browser Dependency
    As a browser extension, GitZip's functionality may be limited by browser-specific restrictions or lack of support in certain browsers.
  • Complexity with Large Repositories
    While GitZip is useful for downloading specific parts, navigating and selecting files in extremely large repositories can become cumbersome and less efficient.
  • Security Concerns
    Using third-party browser extensions may pose security risks, as they can potentially access sensitive data on GitHub.
  • Potential for Bugs
    As with many third-party tools, there is a possibility of encountering bugs or issues, especially following updates to GitHub’s interface or API.

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

GitZip videos

No GitZip videos yet. You could help us improve this page by suggesting one.

Add video

Category Popularity

0-100% (relative to NumPy and GitZip)

NumPy

GitZip

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Productivity
0 0%
Productivity
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Developer Tools
0 0%
Developer Tools
100% 100

User comments

Share your experience with using NumPy and GitZip. For example, how are they different and which one is better?
Log in or Post with

Reviews

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

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

GitZip Reviews

We have no reviews of GitZip yet.
Be the first one to post

Social recommendations and mentions

Based on our record, NumPy seems to be a lot more popular than GitZip. While we know about 119 links to NumPy, we've tracked only 2 mentions of GitZip. 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 (119)

  • Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
    The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / 4 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / 8 months ago
  • Intro to Ray on GKE
    The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / 9 months ago
  • Streamlit 101: The fundamentals of a Python data app
    It's compatible with a wide range of data libraries, including Pandas, NumPy, and Altair. Streamlit integrates with all the latest tools in generative AI, such as any LLM, vector database, or various AI frameworks like LangChain, LlamaIndex, or Weights & Biases. Streamlit’s chat elements make it especially easy to interact with AI so you can build chatbots that “talk to your data.”. - Source: dev.to / 10 months ago
  • A simple way to extract all detected objects from image and save them as separate images using YOLOv8.2 and OpenCV
    The OpenCV image is a regular NumPy array. You can see it shape:. - Source: dev.to / 10 months ago
View more

GitZip mentions (2)

  • How to only downlaod GFM without its submod on Github?
    If you don't want to trust a link from a stranger then you could use https://kinolien.github.io/gitzip/ where you can put the URL of a github folder and it'll give you a zip of the contents, so if you want the belle dark sub mod then you would paste in: https://github.com/Historical-Expansion-Mod/Greater-Flavor-Mod/blob/master/GFM%20Belle%20Dark.mod. Source: almost 3 years ago
  • WASD + mouse position movement on Isometric 2D
    Yeah, on GitHub there's no download directory button or something like this. You could for example use GitZip to download it zipped, just paste URL to that directory in there and download. Source: about 4 years ago

What are some alternatives?

When comparing NumPy and GitZip, 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.

Refined GitHub - Browser extension that makes GitHub cleaner & more powerful

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

Board for Github - A webview based GitHub project app with native features

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

DownGit - Directly download or create download links to GitHub public folders or files.

Loading...