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ZingGrid VS NumPy

Compare ZingGrid VS NumPy and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

ZingGrid logo ZingGrid

Built using web components, ZingGrid is a fully-featured, native solution for interactive, mobile-friendly JavaScript data grids and tables.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • ZingGrid Landing page
    Landing page //
    2021-07-16

ZingGrid is web component-based JavaScript library for data grids & tables with lots of built-in features and tons of out-of-the-box functionality. Whether you're looking for built-in interactivity like CRUD, data sorting and filtering, or a mobile-friendly solution for simple data visualization โ€“ ZingGrid gives you the flexibility to choose exactly the features you need for your next project.

  • NumPy Landing page
    Landing page //
    2023-05-13

ZingGrid

Website
zinggrid.com
Pricing URL
Official ZingGrid Pricing
$ Details
freemium $100.0 / Annually (Single-domain license for one website or application)
Platforms
Windows iOS Android Browser Mac OSX Web REST API JavaScript Edge Safari iPhone Firefox Google Chrome PHP
Release Date
2018 September
Edit details

NumPy

Website
numpy.org
Pricing URL
-
$ Details
Platforms
-
Release Date
-
Edit details

ZingGrid features and specs

  • Ease of Use
    ZingGrid provides an easy-to-use API that requires minimal setup, allowing developers to quickly integrate data grids into their applications without extensive coding knowledge.
  • Customizability
    Offers a variety of customization options for appearance and functionality, enabling developers to tailor the grid to meet specific project or client needs.
  • Feature-rich
    Includes a wide range of built-in features such as sorting, filtering, pagination, and data binding, which enhance the interactivity and usability of the data grid.
  • Responsive Design
    Designed to be responsive, ensuring that grids display well across different devices and screen sizes, which is important for mobile-friendly applications.
  • Documentation and Support
    Provides comprehensive documentation and support resources, which can facilitate a smoother implementation process and assist developers in troubleshooting issues.

Possible disadvantages of ZingGrid

  • Performance with Large Datasets
    May experience performance limitations when handling very large datasets, which can impact the speed and responsiveness of the grid.
  • Dependency on External Libraries
    Might require the integration of external libraries or dependencies, which can increase the complexity of the project and the potential for conflicts.
  • Learning Curve for Advanced Features
    While basic features are easy to implement, there can be a steeper learning curve for utilizing more advanced features or customizations.
  • Limited Flexibility in Complex Scenarios
    May not offer the needed flexibility for highly complex or unique data grid requirements, potentially necessitating workarounds or custom solutions.

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.

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.

ZingGrid videos

No ZingGrid videos yet. You could help us improve this page by suggesting one.

Add video

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

Category Popularity

0-100% (relative to ZingGrid and NumPy)

ZingGrid

NumPy

Data Grid
100 100%
Data Grid
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
JavaScript Tools
100 100%
JavaScript Tools
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

Questions & Answers

As answered by people managing ZingGrid and NumPy.

Which are the primary technologies used for building your product?

ZingGrid's answer

Standard web platform using vanilla JavaScript and relying on the web components API so it is agnostic to framework use.

What's the story behind your product?

ZingGrid's answer

We had built ZingChart, which is used by numerous small and large organizations worldwide, and wanted to address the other aspects of data presentation outside of charting. Given our emphasis at the time of long lived software we opted to go close to web platform and that is why we implemented it as a web component so early.

Why should a person choose your product over its competitors?

ZingGrid's answer

Web standards-focused, framework agnostic, very easy to tie it to a REST or GraphQL endpoint, lots of hooks for customization, and very easy to get started with

How would you describe the primary audience of your product?

ZingGrid's answer

Web developers and web designers looking for a data table or data grid solution for their site or application and not wanted to get locked into a non webstandards solution

What makes your product unique?

ZingGrid's answer

It's the first web component specific advanced datagrid on market and very focused on making common development tasks incredibly easy.

User comments

Share your experience with using ZingGrid and NumPy. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare ZingGrid and NumPy

ZingGrid Reviews

  1. Josh
    Easy to implement with tons of features at your disposal
    ๐Ÿ Competitors: FancyGrid
    ๐Ÿ‘ Pros:    Easy integration|All grids are accessible|Many built-in features|Easy customizability
    ๐Ÿ‘Ž Cons:    Some coding required
Roll20 Alternatives, Similar Games, Apps 2020
ZingGrid is a web component-based JavaScript documentation for data grids & tables with plenty of built-in characteristics and plenty of out-of-the-box functionality. ZingGrid offers you the elasticity to decide exactly the description you require for your subsequent scheme. You should try it if you are looking for Roll20 similar apps.
Source: solutionsuggest.com

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

Social recommendations and mentions

Based on our record, NumPy seems to be more popular. 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.

ZingGrid mentions (0)

We have not tracked any mentions of ZingGrid yet. Tracking of ZingGrid recommendations started around Mar 2021.

NumPy mentions (122)

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What are some alternatives?

When comparing ZingGrid and NumPy, you can also consider the following products

DataTables - DataTables is a plug-in for the jQuery Javascript library.

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

Handsontable - JavaScript Spreadsheet

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

Backgrid.js - A powerful widget set for building data grids with Backbone.js

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

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