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NumPy VS sish

Compare NumPy VS sish and see what are their differences

LibHunt
LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives. featured
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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

sish logo sish

An open source serveo/ngrok alternative. HTTP(S)/WS(S)/TCP Tunnels to localhost using only SSH.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • sish Landing page
    Landing page //
    2023-09-25

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.

sish features and specs

  • Open Source
    sish is open-source, allowing users to inspect, modify, and contribute to the project's codebase.
  • Self-Hosted
    Users can host their own instance of sish, giving them complete control over their tunneling service and associated data.
  • Simple Setup
    The installation and setup process for sish is straightforward, requiring minimal configuration to get started.
  • Custom Subdomains
    sish allows users to utilize custom subdomains for their tunnels, enhancing branding and easier access.
  • Security Features
    Built-in support for TLS and authentication options, ensuring that tunnels are secure and accessible only to authorized users.
  • Portability
    sish supports multiple platforms, allowing it to be used in various environments such as local development, testing, or cloud deployment.

Possible disadvantages of sish

  • Self-Management
    Users need to manage their own server and configurations, which can require additional maintenance and oversight compared to managed services.
  • Resource Consumption
    Hosting your own instance of sish requires computational resources, which could be a con if the service is heavily used.
  • Complexity for Non-Developers
    Non-developers might find the setup and maintenance process challenging without prior experience in server management and configuration.
  • Limited Community Support
    As a niche project, sish may not have as large of a community or as many resources available for troubleshooting as more popular alternatives.
  • No Built-In Analytics
    Unlike some other tunneling services, sish does not provide built-in analytics or monitoring tools, requiring users to implement their own solutions.

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.

Analysis of sish

Overall verdict

  • Overall, Sish is considered a good choice for those looking for a straightforward tunneling solution, especially if they are familiar with SSH. It provides reliable service without the need for complex setups, making it a popular option among developers who prefer lightweight and open-source tools.

Why this product is good

  • Sish is a simple, open-source tool that allows users to serve local applications over the internet using SSH. It's appreciated for its ease of use, minimal configuration, and the ability to handle dynamic port forwarding, making it suitable for both individual developers and small teams seeking an alternative to Ngrok or similar services.

Recommended for

  • Developers who are familiar with SSH and want a simple way to expose their local applications.
  • Teams looking for a free and open-source alternative to paid tunneling services like Ngrok.
  • Individuals who need to quickly share a local application without involving complex configurations.
  • Developers working on side projects or prototypes who need a temporary way to test webhooks or collaborate over the internet.

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

sish videos

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Category Popularity

0-100% (relative to NumPy and sish)

NumPy

sish

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Localhost Tools
0 0%
Localhost Tools
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Testing
0 0%
Testing
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 sish

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

sish Reviews

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Social recommendations and mentions

Based on our record, NumPy should be more popular than sish. 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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sish mentions (17)

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What are some alternatives?

When comparing NumPy and sish, 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.

ngrok - ngrok enables secure introspectable tunnels to localhost webhook development tool and debugging tool.

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

localhost.run - Instantly share your localhost environment!

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

Portmap.io - Expose your local PC to Internet from behind firewall and without real IP address

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