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AG Grid VS NumPy

Compare AG Grid 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

AG Grid logo AG Grid

The best HTML5 datagrid in the world

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • AG Grid Landing page
    Landing page //
    2023-08-02
  • NumPy Landing page
    Landing page //
    2023-05-13

AG Grid features and specs

  • Highly Customizable
    AG Grid provides extensive customization options to tailor the grid's appearance and behavior, allowing developers to adjust the grid according to specific project requirements.
  • Rich Feature Set
    It offers a comprehensive set of features including filtering, sorting, grouping, and pivoting, which can cater to complex data visualization needs.
  • Performance
    AG Grid is optimized for handling large datasets efficiently, providing smooth scrolling and quick data operations without significant lag.
  • Wide Range of Integrations
    It supports integration with major frontend frameworks like Angular, React, and Vue, enabling seamless incorporation into diverse tech stacks.
  • Community and Enterprise Editions
    AG Grid offers both free and paid versions, allowing users to choose based on budget and feature requirements, with enterprise options including additional advanced features.

Possible disadvantages of AG Grid

  • Complexity
    Due to its extensive feature set, AG Grid has a steep learning curve, which can be overwhelming for beginners.
  • Size
    The library can be quite large, potentially affecting the initial load time of applications that require quick startup performance.
  • Cost for Enterprise Features
    To access the most advanced features, users need to purchase the enterprise version, which may not be feasible for small projects or teams with limited budgets.
  • Overhead for Simple Use Cases
    For projects that require only basic grid functionalities, AG Grid might be overkill, leading to unnecessary complexity and resource usage.
  • Documentation Depth
    While documentation is generally comprehensive, it can sometimes lack depth in explaining specific use cases or advanced customization, requiring additional time for exploration and experimentation.

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.

AG Grid videos

+ Add

ag-Grid Conf 2018 | Complexity and Performance | Niall Crosby

More videos:

  • Review - All about ag-grid in angular 6
  • Review - Track 1 Day 2 Livestream | AngularConnect 2019 | Sponsored by ag-Grid

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 AG Grid and NumPy)

AG Grid

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

User comments

Share your experience with using AG Grid 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 AG Grid and NumPy

AG Grid Reviews

Using AG Grid in React: Guide and alternatives
In this guide, we introduced the basic functionalities of the ag-grid-react library and demonstrated how to use AG Grid to build and style a data grid in a React app. To compare alternatives to AG Grid, also built a similar data grid in TanStack Table, Glide Data Grid, and MUI Data Grid. Each library has a unique set of features and tradeoffs, so itโ€™s important to choose the...
Source: blog.logrocket.com
The Best React Data Grid/Table Libraries with Material Design in 2023 - MRT Blog
The "AG" in AG Grid stands for "Agnostic Grid," which means that the library works in multiple JavaScript Frameworks besides React. On the same note, though, AG Grid does not use Material UI under the hood like all of the other libraries on this list. However, it does stick very close to Material Design, so it will not stick out too far from the rest of the components in...
Source: www.material-react-table.com
Best Free and Open-Source JavaScript Data Grid Libraries and Widgets
AG Grid calls itself the best JavaScript library for creating data tables, and for good reason. Major highlights of the library include its excellent performance, no dependency on third-party libraries, and smooth integration with all the major JavaScript frameworks such as Angular, React, and Vue.js. This goes without saying, but you can also use the library with plain...
Source: code.tutsplus.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 a lot more popular than AG Grid. While we know about 122 links to NumPy, we've tracked only 10 mentions of AG Grid. 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.

AG Grid mentions (10)

  • My Failed Student Housing App
    I made extensive use of AG Grid. There are LiveView hooks and whatnot showing how I loaded data, and stylized different types like bools, links, and status. - Source: dev.to / about 2 years ago
  • Supporting Circularly Referenced Mapped Types in Typescript
    In the remainder of this post I will share how I resolved this error for a type called NestedFieldPaths that is a key part of the AG Grid library. - Source: dev.to / almost 3 years ago
  • Generate array of all an interface's keys with Typescript
    When working with a large and complex code base like AG Grid it is very easy to miss updating certain parts of the code base. - Source: dev.to / over 3 years ago
  • Does Angular Support Generic Component Types?
    To give a concrete example of the breakdown in inference we can look at the ag-grid-angular component from AG Grid. This component is generic with respect to row data. It is defined in the following way with many properties omitted for brevity. - Source: dev.to / almost 4 years ago
  • Write Typescript in the browser with SystemJs
    Yes, this does require you to get your SystemJs config setup right but that is why I am sharing this starter that I used for our AG Grid demos so that you can get started easily. - Source: dev.to / over 4 years ago
View more

NumPy mentions (122)

View more

What are some alternatives?

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

Handsontable - JavaScript Spreadsheet

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

MUI X Data Grid - A fast and extensible React data table and React data grid, with filtering, sorting, aggregation, and more.

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

TanStack Table - Headless UI for building powerful tables & datagrids with TS/JS, React, Solid, Svelte and Vue

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

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