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Atlassian Design VS NumPy

Compare Atlassian Design VS NumPy and see what are their differences

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RealityMAX is the AI-powered 3D design collaboration platform, 100% online. Cooperate remotely with your colleagues on 3D projects and easily share your work with clients. Create Web3D and AR experiences in a snap, without a line of code. featured
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Atlassian Design logo Atlassian Design

Design, develop, and deliver

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Atlassian Design Landing page
    Landing page //
    2023-06-22
  • NumPy Landing page
    Landing page //
    2023-05-13

Atlassian Design features and specs

  • Comprehensive Design System
    Atlassian Design provides a complete and consistent design system for building applications, which helps ensure user interfaces are coherent and professional.
  • Access to Components
    It offers a wide range of pre-built UI components that can be easily integrated into projects, saving time in the development process.
  • Documentation
    Extensive and detailed documentation is available, which helps developers and designers understand how to use the system effectively.
  • Consistency
    Ensures that all components and patterns follow the same design principles, resulting in a more consistent user experience across different products.
  • Community Support
    Being a part of the broader Atlassian community means that there is a wealth of shared knowledge and resources available to help solve common problems.

Possible disadvantages of Atlassian Design

  • Learning Curve
    For new users, especially those not familiar with Atlassian products, the system can have a steep learning curve.
  • Customization Limitations
    While it provides many components, customization options might be limited for more unique or advanced use cases.
  • Dependency
    Relying heavily on Atlassian's design system means that changes or updates from Atlassian can impact your products, necessitating continuous adaptation.
  • Performance
    Using a large number of pre-built components might affect the performance of your application, especially if all components are not optimized for your specific use case.
  • Integration Complexity
    Integrating Atlassian Design with other systems or legacy codebases may require additional effort and potentially complex workarounds.

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.

Atlassian Design videos

+ Add

5 things our users want from the Atlassian Design System

More videos:

  • Review - Atlassian Design Week 2017

NumPy videos

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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 Atlassian Design and NumPy)

Atlassian Design

NumPy

Design Tools
100 100%
Design Tools
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Color Palette Generator
100 100%
Color Palette Generator
0% 0
Data Science Tools
0 0%
Data Science 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 Atlassian Design and NumPy

Atlassian Design Reviews

We have no reviews of Atlassian Design yet.
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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 should be more popular than Atlassian Design. It has been mentiond 119 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.

Atlassian Design mentions (12)

  • Top 5 Drag-and-Drop Libraries for React
    As the official evolution of react-beautiful-dnd, this library also comes with extensible accessibility features right out of the box. The default assistive controls are based on the Atlassian Design System, so if you’re already using that, integration will be seamless. But if you aren’t, you can easily replace those components with your own, or completely redefine how accessibility is provided and take a more... - Source: dev.to / 3 months ago
  • Getting Started with Color Module for Your Design System
    Atlassian Design System: Atlassian's Design System encompasses a color module encompassing primary, secondary, and functional colors, along with an extended palette for shades and tints. The system provides comprehensive guidelines for effective color usage and emphasizes accessibility. - Source: dev.to / over 1 year ago
  • Making a UI Kit. Is there a good checklist for Must Have elements?
    Atlassian design system: https://atlassian.design/. Source: about 2 years ago
  • What's the best way to encapsulate a feature to make it reusable?
    Regarding discoverability, you could build a directory with documentation. Similarly to how design systems are documented, e.g: https://atlassian.design/ But if you really want to share them you'll probably need to evangelize it somehow. Source: about 2 years ago
  • UI Design Roadmap 2023
    Step 5: Study design system Atlassian design system Primer design system Spectrum, Adobe’s design system Carbon design system. - Source: dev.to / over 2 years ago
View more

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 / 9 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 / 9 months ago
View more

What are some alternatives?

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

Design Principles - An open source repository of design principles and methods

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

Colorbox.io - Create accessible color systems 🎨

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

Facebook Design - Resources for Designers from the Facebook Design team

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

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