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NumPy VS Eva Design System

Compare NumPy VS Eva Design System and see what are their differences

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NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Eva Design System logo Eva Design System

A free customizable design system
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Eva Design System Landing page
    Landing page //
    2022-07-16

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.

Eva Design System features and specs

  • Customization Flexibility
    Eva Design System offers a high degree of customization, allowing developers and designers to easily adjust themes and components to match specific brand guidelines.
  • Comprehensive Component Library
    The system provides a wide range of pre-designed components, making it easier and faster to build applications with consistent design.
  • Open Source
    Being open-source, Eva Design System allows for community contributions and ensures that it can be freely used and shared, fostering a collaborative environment for improvement.
  • Styled with Theme Variables
    Eva uses theme variables extensively, which facilitates skinning and theming applications, ensuring a unified look and feel across different components and screens.
  • Built-in Dark Mode
    Eva Design System comes with built-in support for dark mode, making it easier for developers to implement this feature without additional complexity.

Possible disadvantages of Eva Design System

  • Learning Curve
    Due to its comprehensive nature and customization options, there may be a steep learning curve for new users who are unfamiliar with the system.
  • Documentation Gaps
    While it is generally well-documented, there can be occasional gaps in the documentation that may hinder smooth implementation or require users to seek additional help.
  • Dependency Management
    Managing dependencies can become complex, especially when integrating Eva Design System with other libraries or frameworks, potentially leading to conflicts or additional overhead.
  • Performance Overhead
    The extensive customization and theming options, while beneficial, may also introduce some performance overhead, particularly in large-scale applications.
  • Community Size
    Compared to more popular design systems, Eva's community is relatively smaller, which may result in fewer third-party integrations, plugins, or community-driven solutions.

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 Eva Design System

Overall verdict

  • Eva Design System is a highly recommended choice for teams looking for a modern and cohesive design language that can accelerate development and ensure design consistency across projects.

Why this product is good

  • Eva Design System is considered good because it offers a comprehensive and consistent framework for building user interfaces that are visually pleasing and user-friendly. It provides a customizable design language, a collection of reusable UI components, and detailed documentation that can help streamline the design and development process. Additionally, it is open-source, which allows for community contributions and makes it adaptable to various project needs.

Recommended for

    Eva Design System is ideal for UI/UX designers, front-end developers, and product teams who want a flexible and robust system for creating applications with a consistent look and feel. It is especially useful for those working on cross-platform projects, as it provides components and guidelines that work well across different environments.

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

Eva Design System videos

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Category Popularity

0-100% (relative to NumPy and Eva Design System)

NumPy

Eva Design System

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Design Tools
0 0%
Design Tools
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Prototyping
0 0%
Prototyping
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 Eva Design System

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

Eva Design System Reviews

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Social recommendations and mentions

Based on our record, NumPy seems to be a lot more popular than Eva Design System. While we know about 119 links to NumPy, we've tracked only 1 mention of Eva Design System. 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 / 4 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
View more

Eva Design System mentions (1)

  • Dark Mode for Developers
    Based on the EVA Design System, the Nebular library is one of the best UI libraries for Angular. Nebular provides inbuilt, customizable themes like the default theme, cosmic theme, dark theme, etc. The mobile version of Nebular, called UI Kitten, also supports the dark theme. - Source: dev.to / over 3 years ago

What are some alternatives?

When comparing NumPy and Eva Design System, 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.

Ant Design System for Figma - A large library of 2100+ handcrafted UI components

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

Design Systems Repo - A collection of design system examples and resources

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

Fabrx.co - Create the UI Kits, Bootstrap 5 or HTML Dashboards that you need in minutes. Have some design sense to spare? Fabrx will help you tackle even the trickiest of projects.

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