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Pixabay VS NumPy

Compare Pixabay VS NumPy and see what are their differences

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Pixabay logo Pixabay

Over 270,000 free photos, vectors and art illustrations

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Pixabay Landing page
    Landing page //
    2018-11-21
  • NumPy Landing page
    Landing page //
    2023-05-13

Pixabay features and specs

  • Free to Use
    Pixabay offers a vast collection of high-quality images, videos, and music, all free to use even for commercial purposes. No attribution is required, which makes it very user-friendly.
  • High-Quality Content
    The platform provides high-resolution images and videos, ensuring that users have access to top-notch media assets suitable for various projects.
  • User-Friendly Interface
    The website is easy to navigate, with a clean layout and efficient search functionality, making it simple for users to find exactly what they are looking for.
  • Diverse Collection
    Pixabay hosts a broad range of media types, including photos, illustrations, vector graphics, videos, and music, catering to a wide array of creative needs.
  • Community-Driven
    The platform is supported by a community of contributors who regularly upload new content, ensuring the repository stays fresh and up-to-date.
  • Safe for Work
    Pixabay has strict guidelines on content that is uploaded, ensuring that the images and videos are safe for work and suitable for all audiences.

Possible disadvantages of Pixabay

  • Limited Niche Content
    While Pixabay offers a wide variety of general content, it may lack more specialized or niche media, which might be available on premium stock websites.
  • Attribution Encouraged
    Although not strictly required, attribution is encouraged. Some users may find this a minor inconvenience if they prefer not to acknowledge the source.
  • Inconsistent Quality
    Due to the community-driven nature of the platform, the quality of user-uploaded content can sometimes be inconsistent, requiring users to sift through lower quality images to find the best ones.
  • Competing Paid Content
    Pixabay often displays sponsored images from paid stock image sites like Shutterstock, which might persuade users to consider premium options even when looking for free content.
  • Legal Ambiguity
    Despite images being free to use, there can occasionally be legal ambiguities with regard to model releases or trademarks, putting a burden on the user to ensure compliance.

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.

Pixabay videos

+ Add

Copyright Free Videos & Images From Pixabay

More videos:

  • Review - Using Pixabay - Royalty Free Images
  • Review - Why I dont trust Google and Pixabay on Free for Commercial Use

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 Pixabay and NumPy)

Pixabay

NumPy

Image Marketplace
100 100%
Image Marketplace
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Photos & Graphics
100 100%
Photos & Graphics
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 Pixabay and NumPy

Pixabay Reviews

12 Best Sites Like Freepik For Downloading Photos
Pixabay is a vibrant community of creatives where thousands of users from all around the world share content. Unlike most sites out there, the content available at Pixabay is 100% safe to use. Not only content shared are copyright free but also released under the Pixabay License. At Pixabay, you get to choose from over 2.6 million+ high-quality stock images, videos, and...
Source: www.devdude.com
Freepik Alternatives: 10 Sites Like Freepik for Free
Freepik similar website, Pixabay, has a talented community that has shared more than 4.2 million excellent stock images, videos, and music for everyone to use. Pixabay is a cheerful group of creative people who share free images, music, videos, and more. Everything on Pixabay can be used without asking or giving credit to the artist, and it’s even okay for some business...
Source: mockey.ai

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, Pixabay should be more popular than NumPy. It has been mentiond 204 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.

Pixabay mentions (204)

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 / 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

What are some alternatives?

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

Unsplash - Unsplash is a website with high-quality free HD images. It has a catalog of more than three hundred thousand striking images that are neatly organized with tags. Read more about Unsplash.

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

Pexels - Find the best free stock images about Browser Home Page. Download all photos and use them even for commercial projects.

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

Shutterstock - Shutterstock is a provider of stock photos, illustrations, and vector art. The website allows individuals to purchase a subscription and download copyrighted art for creative projects. Read more about Shutterstock.

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

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