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NumPy VS Emscripten

Compare NumPy VS Emscripten and see what are their differences

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NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Emscripten logo Emscripten

Emscripten is an LLVM to JavaScript compiler.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Emscripten Landing page
    Landing page //
    2021-08-02

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.

Emscripten features and specs

  • Cross-platform compatibility
    Emscripten enables developers to compile C and C++ code to WebAssembly or JavaScript, allowing the same codebase to run on multiple platforms, such as browsers and node.js, without needing additional platform-specific adaptations.
  • Leverage existing libraries
    Developers can utilize a vast ecosystem of existing C and C++ libraries by compiling them for the web, saving time and resources required for rewriting or finding alternatives developed in JavaScript.
  • Performance optimization
    Emscripten's compilation to WebAssembly provides near-native performance for web applications, making it suitable for compute-intensive tasks like gaming, simulations, and data processing.
  • Familiar toolchain
    Developers can use familiar tools like CMake and others as part of their Emscripten workflow, making it easier for those with C/C++ backgrounds to adapt and integrate into their web development processes.

Possible disadvantages of Emscripten

  • Steep learning curve
    Developers unfamiliar with C and C++ may find Emscripten challenging to use effectively, as it requires knowledge of these languages and their build systems to create and debug applications.
  • Limitations in browser environments
    Certain features of C/C++ may not translate directly to web environments due to browser sandboxing constraints, leading to potential issues with file I/O, threading, and other system-level operations.
  • Code size
    Compiled WebAssembly and JavaScript code can sometimes be large, potentially affecting load times and performance, especially on lower-end devices with restrictive bandwidth or processing capabilities.
  • Debugging complexity
    Debugging WebAssembly code can be more complex than traditional JavaScript, requiring specialized tooling and techniques to trace and fix issues effectively.

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

Emscripten videos

+ Add

Monster Madness Online (Emscripten Web Technology Overview)

Category Popularity

0-100% (relative to NumPy and Emscripten)

NumPy

Emscripten

Data Science And Machine Learning
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Data Science And Machine Learning
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IDE
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IDE
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Data Science Tools
100 100%
Data Science Tools
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Email Marketing
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Email Marketing
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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 Emscripten

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

Emscripten Reviews

We have no reviews of Emscripten yet.
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Social recommendations and mentions

Based on our record, NumPy should be more popular than Emscripten. It has been mentiond 119 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 (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 / 5 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 / 9 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
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Emscripten mentions (47)

  • Ask HN: Qt style "Signals and Slots" based JavaScript UI library?
    The first thing that comes to mind is that Qt now has a WebAssembly port[1] using Emscripten[2], so depending on your use-case, you could possibly just run Qt on the Web platform and avoid the need for a JavaScript framework entirely. [1]: https://doc.qt.io/qt-5/wasm.html [2]: https://emscripten.org. - Source: Hacker News / about 2 months ago
  • Ask HN: Resources for Learning Graphics Programming
    Me and a friend build our own Graphics engines based on https://learnopengl.com I can highly recommend this to everyone who gets started with computer graphics. It is a lot of new information but not the most modern Graphics library, but the information will help you understand the field and pickup any other graphics library quicker. Once I had a small project up and running I started looking at... - Source: Hacker News / 9 months ago
  • Software Applications Incorporated
    Https://infinitemac.org, which is https://basilisk.cebix.net compiled for the web using https://emscripten.org. - Source: Hacker News / over 1 year ago
  • How does one get started with unit testing?
    One place that I’ve found some real, open source unit tests to look at for an example is in the emsdk for emscripten: https://emscripten.org. Source: over 1 year ago
  • Playing with low-level memory in WebAssembly
    I am playing around with Emscipten which wraps around clang to compile C/C++ code in WASM binary and provide some glue-code API to embed WASM binary into JavaScript. Look into MDN Docs and Emscripten SDK to get started. - Source: dev.to / almost 2 years ago
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What are some alternatives?

When comparing NumPy and Emscripten, 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.

WebAssembly - Application and Data, Languages & Frameworks, and Languages

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

GNU Compiler Collection - The GNU Compiler Collection (GCC) is a compiler system produced by the GNU Project supporting...

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

Cheerp - Enterprise-grade C/C++ compiler for Web applications. Compiles to WASM/JavaScript

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