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NumPy VS Deno

Compare NumPy VS Deno and see what are their differences

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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

Deno logo Deno

A secure runtime for JavaScript and TypeScript built with V8, Rust, and Tokio.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Deno Landing page
    Landing page //
    2023-10-15

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.

Deno features and specs

  • Security
    Deno has a secure-by-default approach, requiring explicit permission for file, network, and environment access, which reduces the risk of malicious code.
  • Built-in Tooling
    Deno includes built-in tools like a dependency inspector, a code formatter, and a test runner, reducing the need for additional setup.
  • Modern JavaScript/TypeScript
    Deno supports modern JavaScript and has built-in TypeScript support, making it easier to work with contemporary codebases without additional configuration.
  • Simplified Module Management
    Deno uses URLs for importing modules, eliminating the need for a package manager like npm and simplifying dependency management.
  • Compatibility with Web Standards
    Deno aims to be browser-compatible, adhering closely to web standards like the Fetch API, making it easier to share code between the server and the client.

Possible disadvantages of Deno

  • Ecosystem Maturity
    Deno's ecosystem is relatively new compared to Node.js, resulting in fewer libraries, tools, and community resources.
  • Breaking Changes
    Due to its rapid development, Deno can have breaking changes between versions, potentially requiring more frequent updates and code adjustments.
  • Performance
    Deno's performance may not match that of optimized Node.js applications, especially for certain workloads where Node.js has been highly tuned.
  • Learning Curve
    Even though Deno is designed to be familiar to JavaScript and TypeScript developers, it introduces new concepts (like secure-by-default) that may require a learning curve.
  • Limited Enterprise Adoption
    Being relatively new, Deno has limited enterprise adoption, which might make it less appealing for large-scale or long-term projects that rely on a robust support ecosystem.

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 Deno

Overall verdict

  • Deno is a strong option for developers who prioritize security and modern JavaScript/TypeScript features. Its out-of-the-box toolchain can simplify development by reducing dependencies on external libraries and tools.

Why this product is good

  • Deno is designed to address some of the shortcomings of Node.js. It includes built-in TypeScript support, a secure-by-default runtime, module management using URLs instead of package managers like npm, and built-in utilities for tasks such as linting, formatting, and testing.

Recommended for

    Deno is recommended for developers who are starting new projects that can benefit from its modern approach, those who prioritize security, and developers who prefer using TypeScript. However, for large-scale projects that depend heavily on Node.js's extensive package ecosystem, the transition might require additional considerations.

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

Deno videos

+ Add

Why nobody is using Deno?

More videos:

  • Review - What is Deno & Will it replace Node.js?
  • Review - Will Deno replace Node.js: Which programming language is better? | TechLead

Category Popularity

0-100% (relative to NumPy and Deno)

NumPy

Deno

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Typescript
0 0%
Typescript
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
JavaScript
0 0%
JavaScript
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 Deno

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

Deno Reviews

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

Social recommendations and mentions

Based on our record, Deno should be more popular than NumPy. It has been mentiond 201 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)

View more

Deno mentions (201)

  • 100 Most Useful Open Source Projects
    Deno โ€” https://deno.land Technology: JavaScript/TypeScript runtime. Backed / sponsored by: Deno Company + open source community. How to generate revenue: Paid hosting, enterprise support, managed Deno services, training. Description / details: Secure by default runtime by Nodeโ€™s original author; integrates TypeScript natively. - Source: dev.to / 10 months ago
  • Benchmarking in Node.js vs Deno: A Comprehensive Comparison
    Deno.bench("URL parsing", () => { new URL("https://deno.land"); }); Deno.bench("Async method", async () => { await crypto.subtle.digest("SHA-256", new Uint8Array([1, 2, 3])); }); Deno.bench({ name: "Long form", fn: () => { new URL("https://deno.land"); }, }); Deno.bench({ name: "Date.now()", group: "timing", baseline: true, fn: () => { Date.now(); }, }); Deno.bench({ name:... - Source: dev.to / over 1 year ago
  • Deno 2.0 REST API Explained: Faster, Secure JavaScript Development
    // Importing the serve function from Deno's standard library Import { serve } from "https://deno.land/std@0.196.0/http/server.ts"; // Function to handle requests Async function handler(req: Request): Promise { const { pathname, searchParams } = new URL(req.url); // Handling different routes if (pathname === "/api/greet" && req.method === "GET") { const name = searchParams.get("name") ||... - Source: dev.to / almost 2 years ago
  • Building a Simple Todo App with Deno and Oak
    Import { Application, Router } from "https://deno.land/x/oak/mod.ts";. - Source: dev.to / almost 2 years ago
  • LogTape: Zero-Dependency Logging for JavaScript That Just Works
    LogTape is a shiny new logging library for JavaScript and TypeScript that's designed with one goal in mind: to make logging simple, flexible, and hassle-free across all your JavaScript environments. Whether you're building applications for Deno, Node.js, Bun, edge functions, or browsers, LogTape has got you covered. - Source: dev.to / almost 2 years ago
View more

What are some alternatives?

When comparing NumPy and Deno, 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.

Bun.sh - Bun is an all-in-one JavaScript runtime & toolkit designed for speed, complete with a bundler, test runner, and Node.js-compatible package manager.

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

Node.js - Node.js is a platform built on Chrome's JavaScript runtime for easily building fast, scalable network applications

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

Fresh Framework - Fresh is a next generation web framework, built for speed, reliability, and simplicity.

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