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Glide VS NumPy

Compare Glide VS NumPy and see what are their differences

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Glide logo Glide

Send lightning fast video messages, see responses live or whenever it's convenient. Get closer to the ones you love with video communication.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Glide Landing page
    Landing page //
    2023-06-12
  • NumPy Landing page
    Landing page //
    2023-05-13

Glide features and specs

  • Ease of Use
    Glide provides an intuitive interface that allows users to create mobile apps with minimal coding knowledge, making it accessible to a wide range of users.
  • Speed of Development
    The platform significantly reduces development time by allowing users to build functional mobile apps quickly using pre-made templates and drag-and-drop components.
  • Google Sheets Integration
    Glide seamlessly integrates with Google Sheets, enabling users to use existing data for app development without needing to set up a new database.
  • Cost-Effective
    Offering various pricing plans, including a free tier, Glide provides a cost-effective solution for individuals and small businesses needing to develop mobile apps.
  • Multi-Platform Support
    Apps built with Glide are accessible on both iOS and Android devices, allowing for broad user reach without the need for separate development efforts for each platform.

Possible disadvantages of Glide

  • Limited Customization
    While Glide offers a range of templates and components, users with advanced needs may find the customization options limited compared to traditional app development frameworks.
  • Performance
    For complex apps with high-performance requirements, Glide-based apps may not perform as well as natively developed applications due to the constraints of a no-code platform.
  • Dependency on Google Sheets
    The strong reliance on Google Sheets for data handling can be a limitation for users who need more robust database management or who prefer other data storage solutions.
  • Scalability
    As apps grow in complexity and user base, they may encounter scalability issues when built on Glide, making it more suitable for smaller or simpler applications.
  • Feature Limitations
    Certain advanced features and functions that are achievable through traditional coding are not available or are difficult to implement in Glide, limiting the app's capabilities.

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 Glide

Overall verdict

  • Glide is considered a good option for teams and developers who are already using container technology or looking to streamline their deployment workflows. Its features and integrations make it competitive among similar tools, offering a balance of usability and functionality that appeals to many users.

Why this product is good

  • Glide.sh is a software tool aimed at accelerating software delivery through containerization and automating various aspects of deployment processes. It provides an intuitive platform for building, testing, and deploying applications quickly and efficiently, often reducing the complexity involved in managing containerized environments.

Recommended for

  • Development teams looking to improve continuous integration and continuous deployment (CI/CD) processes.
  • Companies seeking to adopt or enhance their containerization strategies.
  • Developers who want to focus on coding by automating deployment and infrastructure management tasks.
  • Organizations prioritizing fast and reliable software delivery lifecycle management.

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.

Glide videos

+ Add

HARLEY-DAVIDSON SPORT GLIDE REVIEW - 2 Years Later

More videos:

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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 Glide and NumPy)

Glide

NumPy

No Code
100 100%
No Code
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Productivity
100 100%
Productivity
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 Glide and NumPy

Glide Reviews

Top 10 Microsoft Power Apps Alternatives and Competitors 2024
Glide Pricing: Glide offers a freemium plan with limited features for building single apps. Paid plans start at $15 per month for unlimited apps and additional features. Enterprise plans with custom pricing cater to large-scale deployments.
Source: medium.com
13 Best Website Builders for Creators and Social Entrepreneurs(2023)
Share and update instantly. Glide makes updating your app as easy as editing a document—changes instantly go live for your users, so you can iterate quickly.
Source: causeartist.com
THE BEST 34 APP DEVELOPMENT SOFTWARE IN 2022 LIST
Create an app from a Google Sheet in five minutes, for free. Glide turns spreadsheets into beautiful, easy-to-use apps. You can create apps visually, without code.
Source: www.edigitalagency.com.au
33+ Best No Code Tools you will love 😍
To accelerate your learning + use case to developing an app, Glide has an amazing template library where you can develop apps for any category. It's super cool! You can also see which templates have been "copied" most to see what others have built using Glide.
Source: www.dansiepen.io
25 No-Code Apps and Tools to help build your next Startup
Glide is the fastest app development around! In just minutes and without writing a line of code, Glide builds easy to use, working applications for a wide range of use cases.
Source: www.ishir.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 more popular. It has been mentiond 121 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.

Glide mentions (0)

We have not tracked any mentions of Glide yet. Tracking of Glide recommendations started around Mar 2021.

NumPy mentions (121)

  • Top 5 GitHub Repositories for Data Science in 2026
    The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, A…. - Source: dev.to / 14 days ago
  • Your 2025 Roadmap to Becoming an AI Engineer for Free for Vue.js Developers
    AI starts with math and coding. You don’t need a PhD—just high school math like algebra and some geometry. Linear algebra (think matrices) and calculus (like slopes) help understand how AI models work. Python is the main language for AI, thanks to tools like TensorFlow and NumPy. If you know JavaScript from Vue.js, Python’s syntax is straightforward. - Source: dev.to / about 2 months ago
  • 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 / 8 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 / about 1 year 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 / about 1 year ago
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What are some alternatives?

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

FlutterFlow - FlutterFlow is an online low-code platform that empowers people to build native mobile apps visually.

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

Bubble.io - Building tech is slow and expensive. Bubble is the most powerful no-code platform for creating digital products.

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

Adalo - Build apps for every platform, without code ✨

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

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