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Adalo VS NumPy

Compare Adalo VS NumPy and see what are their differences

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Build powerful web apps, and create your custom AI apps, no code needed! featured
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  3. » Reviews
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Adalo logo Adalo

Build apps for every platform, without code ✨

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Adalo Landing page
    Landing page //
    2023-09-26
  • NumPy Landing page
    Landing page //
    2023-05-13

Adalo features and specs

  • User-Friendly Interface
    Adalo offers a highly intuitive drag-and-drop interface, making it accessible for users without technical skills.
  • Rapid Prototyping
    The platform allows for quick design and deployment, enabling users to rapidly prototype and test their applications.
  • Integration with Various Services
    Adalo supports integration with a variety of third-party services and APIs, enhancing its functionality and versatility.
  • Customizable Components
    Users can customize pre-built components or create their own, offering flexibility in app design.
  • Cross-Platform Capability
    Adalo enables the development of apps for both iOS and Android platforms, as well as web applications.

Possible disadvantages of Adalo

  • Performance Issues
    Some users report slower loading times and performance issues, especially with complex applications.
  • Limited Scalability
    The platform may not be suitable for highly scalable applications or those requiring advanced functionality.
  • Subscription Costs
    While Adalo offers a free tier, the more useful features are behind a paywall, which can become costly for continued use.
  • Data Storage Constraints
    Users are often limited by the amount of data they can store, which could be a downside for data-intensive applications.
  • Dependency on No-Code Environment
    Relying heavily on a no-code solution can limit technical growth and may not be suitable if custom coding becomes necessary.

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.

Adalo videos

+ Add

No Code Summit at Microsoft NYC: Adalo

More videos:

  • Review - Introducing Adalo | Create Your Own App Without Code
  • Tutorial - Adalo Tutorial

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 Adalo and NumPy)

Adalo

NumPy

Application Builder
100 100%
Application Builder
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
No Code
100 100%
No Code
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 Adalo and NumPy

Adalo Reviews

THE BEST 34 APP DEVELOPMENT SOFTWARE IN 2022 LIST
You can bring your app ideas to life with Adalo. Creating web and mobile apps today requires learning how to code, finding a technical cofounder, or raising a lot of capital. For some, this means spending lots of precious time and money before they can get their idea off the ground. For others, this cost is simply too high and they walk away from their dream. With Adalo app...
Source: www.edigitalagency.com.au
21 Best No Code Tools You Need To Try
Whilst it may seem similar to Glide above, Adalo is focused more on the visual “drag-and-drop” user experience so users can instantly see what they build in real-time.
Source: www.cenario.co
33+ Best No Code Tools you will love 😍
Building No Code Apps has never been easier with Adalo as a platform of choice. You can build functional, advanced apps with Adalo which honestly can replicate custom code apps you may pay thousands for with a developer.
Source: www.dansiepen.io
25 No-Code Apps and Tools to help build your next Startup
Adalo is an amazing and intuitive way to build beautiful applications! Adalo-built apps can work on both the Apple App Store and the Google Play Store. Adalo’s top selling point is the easy to use “drag and drop” feature which allows you to watch the progress and design of your app in real time. The huge community of makers happily share their tips and tricks to great...
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 a lot more popular than Adalo. While we know about 119 links to NumPy, we've tracked only 3 mentions of Adalo. 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.

Adalo mentions (3)

  • Ask HN: Repair and Maintenance App
    Yes, I think no-code solution can work easily for this use case. There are no of solutions you can try and see which one fits best in your use case. https://bubble.io, https://drapcode.com, etc works best for web apps. If you need Mobile Apps, then you can try using https://adalo.com or Thunkable/GlideApps etc. - Source: Hacker News / almost 4 years ago
  • Retool alternative which not charge per user?
    Thanks, but it look so expensive. For mobile app, I still evaluating thunkable.com and adalo.com. Source: almost 4 years ago
  • Amazon Honeycode is here to shake up the no code market
    After dropping several hints in recent months, AWS finally launched the beta version of Amazon Honeycode, the company’s spanking new rendition of a no-code product. For the longest time, customers of the no-code market segment have turned to brands like bubble.io and adalo.com for quick and engaging app development projects. But with Beta Honeycode now around, it’s interesting to see what tricks AWS has up its... Source: almost 4 years ago

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 / 8 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 Adalo and NumPy, you can also consider the following products

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

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

zeroqode - Build your app up to 10x faster with no-code app templates

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

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

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

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