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Gotty VS NumPy

Compare Gotty VS NumPy and see what are their differences

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Gotty logo Gotty

GoTTY is a simple command line tool that turns your CLI tools into web applications.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Gotty Landing page
    Landing page //
    2023-09-27
  • NumPy Landing page
    Landing page //
    2023-05-13

Gotty features and specs

  • Remote Access
    Gotty allows users to access terminal applications over the web, enabling remote command line operations without needing SSH access.
  • Ease of Use
    The setup process for Gotty is straightforward and easy, requiring minimal configuration to get started.
  • Cross-Platform Compatibility
    Gotty is written in Go, making it portable across different operating systems like Linux, macOS, and Windows.
  • No Client Installation Needed
    Clients can access the terminal via a web browser, eliminating the need for additional software installation on user devices.
  • HTML5-based
    The usage of HTML5 ensures a modern browsing experience with broad compatibility and no plugins required.

Possible disadvantages of Gotty

  • Security Concerns
    Gotty exposes terminal access over HTTP, which might be risky if not secured properly as it can lead to unauthorized access.
  • Limited to Terminal Applications
    Gotty is designed for running terminal-based applications only, so it may not be suitable for use cases requiring GUI-based applications.
  • Basic Authentication
    Gotty's authentication mechanism is relatively basic, relying on a single password, which may not be ideal for all use cases.
  • Network Dependency
    Accessing Gotty requires a stable network connection, and performance can be affected by network speed and latency.
  • No Built-in Authorization
    While Gotty can limit access with passwords, it doesn't natively support more sophisticated user role-based access control.

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 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.

Gotty videos

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NumPy videos

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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 Gotty and NumPy)

Gotty

NumPy

Testing
100 100%
Testing
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Localhost Tools
100 100%
Localhost 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 Gotty and NumPy

Gotty Reviews

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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

Based on our record, NumPy should be more popular than Gotty. 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.

Gotty mentions (13)

  • Advent of Sysadmin 2025
    We used to run terminal in browser using https://github.com/yudai/gotty and the entire dev team remapped their Ctrl+w to Ctrl+`. We did frontend and backend development with this setup almost for 1.5 years. Muscles memory and till this date, always have the fear if my actual terminal will get closed if I use Ctlr+w :P. - Source: Hacker News / 9 months ago
  • Turn Your Android Tablet into an IDE with VSCode and Nix
    I use nix-on-droid to keep a dev environment on my phone. Sometimes I have an hour or two to kill in the university library. I use their computers' screens and keyboards, but I'm coding on my phone through a browser tab and https://github.com/yudai/gotty Beats the hell out of trying to be productive on Windows. - Source: Hacker News / over 2 years ago
  • Show HN: A WireGuard Powered Remote Shell
    The shell itself doesn't really seem any better than e.g. [gotty](https://github.com/yudai/gotty), and there's a bunch more similar things, so at the moment, doesn't seem too useful... - Source: Hacker News / over 2 years ago
  • How to run functions on a remote server and get the result on my computer?
    (FYI: A fun manual remote terminal. Totally insecure, but fun.). Source: over 3 years ago
  • Terminal with web UI?
    Thank you for all the suggestions. I tried some of these and decided to go with GoTTY: Https://github.com/yudai/gotty. Source: over 3 years ago
View more

NumPy mentions (122)

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What are some alternatives?

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

Teleconsole - Teleconsole is a free service to share your terminal session with people you trust.

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

Pagekite - Bring your localhost servers on-line.

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

Warp - Warp (Windows Advanced Rasterization Platform) is a high-speed software rasterizer tool designed for the accurate reproduction of bitmap graphics on modern microprocessor-based systems.

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

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