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NumPy VS Airtable

Compare NumPy VS Airtable and see what are their differences

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NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Airtable logo Airtable

Airtable works like a spreadsheet but gives you the power of a database to organize anything. Sign up for free.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Airtable Landing page
    Landing page //
    2023-10-19

NumPy

Website
numpy.org
Pricing URL
-
$ Details
Release Date
-
Edit details

Airtable

Website
airtable.com
Pricing URL
Official Airtable Pricing
$ Details
-
Release Date
2013 January
Startup details
Country
United States
State
California
City
San Francisco
Founder(s)
Andrew Ofstad
Employees
250 - 499
Edit details

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.

Airtable features and specs

  • User-Friendly Interface
    Airtable provides an intuitive, visually appealing interface that makes it easy for users to create, manage, and navigate complex data sets without extensive technical knowledge.
  • Customizability
    Airtable offers robust customization options, allowing users to tailor databases and views to their specific needs, including various field types, multiple views, and linked records.
  • Collaboration Features
    Airtable supports real-time collaboration, enabling multiple users to work on the same database simultaneously while tracking changes and maintaining version history.
  • Integrations
    Airtable integrates with various third-party applications and services such as Slack, Google Drive, and Zapier, allowing for seamless workflow automation and enhanced productivity.
  • Templates
    Airtable offers a wide range of pre-built templates for different use cases, which helps users get started quickly without having to build everything from scratch.
  • Mobile App
    Airtable's mobile application allows users to access and manage their databases on the go, ensuring flexibility and continuous productivity.

Possible disadvantages of Airtable

  • Cost
    While Airtable offers a free tier, many of the more advanced features and higher usage limits are locked behind a subscription model, which can become costly for larger teams or extensive use.
  • Performance Issues
    Some users have reported performance issues with Airtable when working with very large databases, including slow load times and laggy interface responsiveness.
  • Limited Offline Access
    Airtable relies heavily on an internet connection, and its offline capabilities are limited, which may be a drawback for users who need consistent access without reliable internet.
  • Data Export Options
    Data export options are somewhat limited compared to other database management tools, making it more difficult to extract data in certain formats for use outside of Airtable.
  • Learning Curve
    Despite its user-friendly interface, the extensive features and customizability of Airtable can present a learning curve for new users, requiring time to explore and understand its full capabilities.
  • Lack of Advanced Features
    Airtable may lack some advanced features found in more specialized or traditional database management systems, making it less suitable for particularly complex or highly specific data management needs.

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 Airtable

Overall verdict

  • Airtable is a highly effective tool for individuals and teams looking for an easy-to-use, versatile platform to manage data and collaborate. It is especially suitable for users who need more functionality than a traditional spreadsheet but don’t require a full-scale database management system.

Why this product is good

  • Ease of use
    Airtable offers a user-friendly interface that combines the simplicity of spreadsheets with the power of a database, making it accessible for users without technical expertise.
  • Flexibility
    It provides flexibility in terms of creating customizable databases, allowing users to adapt it for various use cases such as project management, inventory tracking, and more.
  • Integrations
    It integrates with a wide range of third-party applications and services, enhancing its utility and reach.
  • Collaboration
    Airtable supports real-time collaboration, enabling teams to work together seamlessly and share updates instantly.

Recommended for

  • Small to medium-sized businesses
  • Project managers
  • Creative teams
  • Freelancers
  • Non-profits
  • Product development teams

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

Airtable videos

+ Add

Airtable Review | Features, Pricing & Team Use

More videos:

  • Tutorial - Airtable API Tutorial With cURL and JavaScript
  • Review - Airtable Blocks for Project Management
  • Review - Airtable vs. Google Sheets
  • Review - airtable review

Category Popularity

0-100% (relative to NumPy and Airtable)

NumPy

Airtable

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Project Management
0 0%
Project Management
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
No Code
0 0%
No Code
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 Airtable

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

Airtable Reviews

  1. Sanjana Shah
    · Data Scientist at Boston Institute of Analytics ·
    Airtable: Spreadsheets + Databases = Efficiency

    Airtable is a powerful cloud-based software that combines spreadsheets and databases, offering real-time collaboration and customizable features for efficient task management1.

    🏁 Competitors: monday.com, ClickUp, Smartsheet
    👍 Pros:    Free forever plan and affordable paid options starting at $10 per month.|Visually appealing and user-friendly interface.|Pre-made templates for easy setup and use.|Real-time collaboration and communication.|Customizable features for task management.
    👎 Cons:    Limited project customization without a paid plan.|Top-tier accounts required for gantt tools.|May take time to learn and grasp advanced features.
The Top 7 ClickUp Alternatives You Need to Know in 2025
Benefits:Airtable's ability to integrate various data sources into one platform allows teams to maintain a centralized source of truth while leveraging powerful visualization tools6.
Source: www.ledgerteams.com
Top 10 Notion Alternatives for 2025 and Why Teams Are Choosing Ledger
Airtable blends spreadsheets with database features, offering teams a powerful way to organize structured information. While its flexibility is impressive, it's not purpose-built for communication or team collaboration at scale.
Source: www.ledgerteams.com
25 Best Asana Alternatives & Competitors for Project Management in 2024
Airtable is one of the most popular Asana alternatives. It’s project management tool that helps teams create detailed databases for their work. Users can group and sort data in custom fields with views like Grid to include only the relevant project information.
Source: clickup.com
Top 10 Microsoft Power Apps Alternatives and Competitors 2024
Airtable Pricing: Airtable offers a freemium plan with limited features for individual users. Paid plans start at $10 per user per month for additional features and functionalities. Enterprise plans with custom pricing cater to large-scale deployments.
Source: medium.com
The 10 best Asana alternatives in 2024
If you're looking for a project management app that leans more toward data management, try Airtable. Out of the box, Airtable's default view looks like a spreadsheet. It offers a few project templates based on your team type (such as marketing or sales), or you can build a "base" from scratch. From there, you can add highly customizable fields (or columns) to each row, so...
Source: zapier.com

Social recommendations and mentions

Airtable might be a bit more popular than NumPy. We know about 132 links to it since March 2021 and only 121 links to NumPy. 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 (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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Airtable mentions (132)

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What are some alternatives?

When comparing NumPy and Airtable, 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.

Trello - Infinitely flexible. Incredibly easy to use. Great mobile apps. It's free. Trello keeps track of everything, from the big picture to the minute details.

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

Asana - Asana project management is an effort to re-imagine how we work together, through modern productivity software. Fast and versatile, Asana helps individuals and groups get more done.

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

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