LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives.
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Which is more popular?
Based on our record, NumPy
seems to be a lot more popular than Johnny-Five.
While we know about 122 links to NumPy,
we've tracked only 11 mentions of Johnny-Five.
social mentions
11 vs 122
Data Science And Machine Learning popularity
3% vs 97%
alternatives listed
27 vs 189
Base details
Website, pricing, platforms and company facts side by side.
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
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
An editorial look at what each product does well and who it suits.
Johnny-FiveNumPy
Overall verdict
Johnny-Five is a mature, well-established JavaScript robotics and IoT framework that offers an approachable, expressive API and broad hardware support, making it a solid choice for developers who want to build hardware projects using Node.js.
Why this product is good
Provides a clean, intuitive JavaScript/Node.js API that lowers the barrier to entry for hardware programming
Supports a wide range of platforms and boards including Arduino, Raspberry Pi, BeagleBone, and many others through IO plugins
Backed by an active open-source community with extensive documentation, examples, and tutorials
Enables rapid prototyping by leveraging the vast npm ecosystem alongside physical components
Free and open-source, with a stable and battle-tested codebase used in many real-world projects
Recommended for
JavaScript and Node.js developers who want to get into physical computing and robotics
Hobbyists and makers building Arduino or Raspberry Pi projects
Educators teaching programming and electronics through an accessible language
Rapid prototyping of IoT devices and hardware experiments
Developers who prefer working within the npm ecosystem for hardware integration
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.
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...
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...
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...
Recommendations tracked on public social media and blogs since March 2021.
Johnny-Five11 mentionsNumPy122 mentions
Cylon: JavaScript framework for robotics, drones, and the Internet of Things
What are the pros and cons of this compared to Johnny-Five? http://johnny-five.io/.
- Source: Hacker News
/
over 2 years ago
Qwik & Arduino with Johnny Five
Some times ago I have played with johnny-five, a JavaScript Robotics & IoT platform.
In short words, we can communicate with our Arduino or Raspberry by using JavaScript with a very friendly syntax.
- Source: dev.to
/
almost 3 years ago
Dockerize Javascript IOT Applications
The library Johnny-Five is a Javascript library that allows you to control Arduino and other microcontrollers using Javascript. It is very easy to use and it has a lot of examples that you can use to learn how to use it.
- Source: dev.to
/
over 3 years ago
The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages
Familiarity with Python as a language is assumed; if you need a quick...
- Source: dev.to
/
about 1 year ago
AI starts with math and coding. You don’t need a PhD—just high school math like algebra and some geometry. Linear algebra (think matrices) and calculus (like slopes) help understand how AI models work. Python is the main language for AI,...
- Source: dev.to
/
over 1 year ago
Keras
OpenCV
Losant
Hologram.io
Fossil
Node OS
Cisco Jasper
Pandas
Scikit-learn
Dataiku
Exploratory
htm.java
Figure Eight
