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NumPy VS DHTMLX

Compare NumPy VS DHTMLX and see what are their differences

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Contents:
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  3. » Reviews
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NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

DHTMLX logo DHTMLX

JavaScript Library for cross-platform web and mobile app development with HTML5 JavaScript widgets. Easy integration with popular JavaScript Frameworks.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • DHTMLX Landing page
    Landing page //
    2023-07-27

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.

DHTMLX features and specs

  • Comprehensive Suite
    DHTMLX offers a wide range of UI components, from grids and charts to complex Gantt and scheduler components, providing a comprehensive toolkit for web application development.
  • Rich Documentation
    The platform provides extensive documentation, demos, and examples, which are invaluable for developers in understanding and implementing various components efficiently.
  • Cross-Browser Compatibility
    DHTMLX components are designed to be fully compatible across major browsers, ensuring a consistent user experience regardless of the user's environment.
  • Support and Community
    DHTMLX offers various support options including forums and ticket-based support, alongside a strong user community that can provide insights and assistance.
  • Customizability
    The components are highly customizable, allowing developers to tailor them to fit the specific aesthetics and functionality requirements of their projects.

Possible disadvantages of DHTMLX

  • Cost
    While DHTMLX provides a free version, the full suite with advanced features requires a paid license, which can be a drawback for startups or individual developers with limited budgets.
  • Complexity
    With its comprehensive set of features, DHTMLX can have a steep learning curve for newcomers who are unfamiliar with its architecture and functionalities.
  • Dependency on Proprietary Framework
    Using DHTMLX can lead to a dependency on its particular frameworks and methodologies, which might be a concern for projects that prioritize open-source solutions.
  • Performance Overhead
    Implementing multiple DHTMLX components in a single application might introduce performance overhead, which requires optimization to maintain responsiveness.
  • Limited Open Source Contributions
    As a commercial library, DHTMLX might not benefit from as many open source contributions and innovations as purely open-source alternatives.

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

DHTMLX videos

+ Add

Dhtmlx Scheduler

More videos:

  • Review - dhtmlxGantt 6.1 Release: Time Constraints, Backward Scheduling, S-curve and dataProcessor Update
  • Review - Dhtmlx-Grid with Flux4 Part2

Category Popularity

0-100% (relative to NumPy and DHTMLX)

NumPy

DHTMLX

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Javascript UI Libraries
0 0%
Javascript UI Libraries
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Development Tools
0 0%
Development 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 NumPy and DHTMLX

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

DHTMLX Reviews

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

Based on our record, NumPy seems to be a lot more popular than DHTMLX. While we know about 121 links to NumPy, we've tracked only 1 mention of DHTMLX. 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 (121)

  • Top 5 GitHub Repositories for Data Science in 2026
    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 introduction to the language itself, see the free companion project, A…. - Source: dev.to / 14 days ago
  • Your 2025 Roadmap to Becoming an AI Engineer for Free for Vue.js Developers
    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, thanks to tools like TensorFlow and NumPy. If you know JavaScript from Vue.js, Python’s syntax is straightforward. - Source: dev.to / about 2 months ago
  • 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 / 8 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 / about 1 year 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 / about 1 year ago
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DHTMLX mentions (1)

  • How Much Does It Cost To a Project Management App
    To finish projects on time, efficient time management is a must. To help managers deal with deadlines, we usually implement a fully functional Gantt chart. To create a reliable and user-friendly UI, choosing the right set of tools is essential. In our work, we usually rely on Webix, a JavaScript UI library developed by the brightest minds of XB Software. Another ace up our sleeve is JavaScript UI libraries by... - Source: dev.to / over 3 years ago

What are some alternatives?

When comparing NumPy and DHTMLX, 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.

Bryntum - High performance web components for SaaS apps - including Gantt, Scheduler, Grid, Calendar and Kanban widgets. Seamless integration with React, Vue, Angular or plain JS apps.

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

Sencha Ext JS - Sencha Ext JS is the most comprehensive JavaScript framework for building data-intensive, cross-platform web and mobile applications for any modern device. Ext JS includes 140+ pre-integrated and tested high-performance UI components.

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

Webix UI - An enterprise JavaScript Library for cross-platform app development with HTML5 JavaScript widgets and easy integration with most popular JavaScript Frameworks.

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