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NumPy VS Design Principles

Compare NumPy VS Design Principles and see what are their differences

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NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Design Principles logo Design Principles

An open source repository of design principles and methods
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Design Principles Landing page
    Landing page //
    2023-06-17

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.

Design Principles features and specs

  • Comprehensive Resource
    The website offers a wide collection of design principles from various companies and independent designers, providing a rich resource for learning and inspiration.
  • Diverse Perspectives
    The compilation includes principles from various industries and design philosophies, offering a well-rounded view of design thinking.
  • Easy to Navigate
    The site is well-organized and categorized, making it simple to find relevant design principles quickly.
  • Free Access
    The resource is available for free, making it accessible to anyone interested in design principles without any financial barrier.
  • Regularly Updated
    The collection is periodically updated with new content, ensuring users have access to the latest design thinking.

Possible disadvantages of Design Principles

  • Overwhelming Amount of Information
    The extensive collection of principles might be overwhelming for beginners who are just starting to learn about design.
  • Quality Variation
    Because the principles are sourced from various contributors, there is a variation in the quality and depth of the principles listed.
  • Lacks Interactivity
    The site mainly provides static information and lacks interactive elements that could enhance the learning experience.
  • No Community Features
    There are no built-in community features for users to discuss or collaborate on design principles, which could limit the exchange of ideas.
  • Sparse Context
    Some principles are presented without much context or explanation, which may make it difficult to understand their practical application.

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 Design Principles

Overall verdict

  • Yes, Design Principles (principles.design) is a valuable and insightful resource for designers looking to enhance their understanding and application of design principles.

Why this product is good

  • Design Principles (principles.design) is a highly regarded resource that provides a comprehensive collection of well-crafted design principles from various successful companies and products. It offers insights into how these principles guide design decisions, resulting in user-friendly and aesthetically pleasing interfaces. The platform also allows designers to learn from industry leaders, adapting and applying proven frameworks to their own projects, thus improving the overall quality and effectiveness of their design work.

Recommended for

  • UI/UX designers
  • Product designers
  • Design students
  • Creative directors
  • Design educators
  • Anyone interested in understanding how effective design principles can enhance user experiences

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

Design Principles videos

+ Add

GOTO 2016 • Secure by Design – the Architect's Guide to Security Design Principles • Eoin Woods

Category Popularity

0-100% (relative to NumPy and Design Principles)

NumPy

Design Principles

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 Design Principles

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

Design Principles Reviews

We have no reviews of Design Principles yet.
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Social recommendations and mentions

Based on our record, NumPy seems to be a lot more popular than Design Principles. While we know about 119 links to NumPy, we've tracked only 9 mentions of Design Principles. 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 / 8 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

Design Principles mentions (9)

  • Meaningful distinction between heuristics and principles?
    Your comment is an interesting one, and I can see how it’s be helpful for some folks who are just setting out in their careers. I was asking not about style guides, but the nuanced differences between heuristics, such as NNg’s, and design principles for decision-making: https://principles.design/. Source: over 2 years ago
  • I Found 15 Free Resources For Entrepreneurs To Help Them with Social Media, SEO and Growth!
    Principle Design is a Free Resource to learn more about designing better user interfaces and logos for your business. Access 195+ Examples and 1445 principles to learn more about design. (no-signup). Source: over 2 years ago
  • The importance of having a design system
    Http://styleguides.io/ and https://principles.design/ are worth keeping an eye on, especially for trends that come up and to see what the industry is up to. Source: over 2 years ago
  • The importance of having a design system
    Https://principles.design/ (collection, guiding ethos). Source: over 2 years ago
  • Ask HN: How do you design good primitives?
    Https://paperform.co/blog/principles-of-design/ https://principles.design/ https://99designs.com/blog/tips/principles-of-design/. - Source: Hacker News / about 3 years ago
View more

What are some alternatives?

When comparing NumPy and Design Principles, 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.

Atlassian Design - Design, develop, and deliver

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

Product Disrupt - A design student's list of resources to learn Product Design

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

Checklist Design - The best UI and UX practices for production ready design.

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