Software Alternatives, Accelerators & Startups

Airtable VS Pandas

Compare Airtable VS Pandas and see what are their differences

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Airtable logo Airtable

Airtable works like a spreadsheet but gives you the power of a database to organize anything. Sign up for free.

Pandas logo Pandas

Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
  • Airtable Landing page
    Landing page //
    2023-10-19
  • Pandas Landing page
    Landing page //
    2023-05-12

Airtable

$ Details
-
Release Date
2013 January
Startup details
Country
United States
State
California
Founder(s)
Andrew Ofstad
Employees
250 - 499

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.

Pandas features and specs

  • Data Wrangling
    Pandas offers robust tools for manipulating, cleaning, and transforming data, making it easier to prepare data for analysis.
  • Flexible Data Structures
    Pandas provides two primary data structures: Series and DataFrame, which are flexible and offer powerful capabilities for handling various types of datasets.
  • Integration with Other Libraries
    Pandas integrates seamlessly with other Python libraries such as NumPy, Matplotlib, and SciPy, facilitating comprehensive data analysis workflows.
  • Performance with Data Size
    For data sizes that fit into memory, Pandas performs excellently with operations and computations being highly optimized.
  • Rich Feature Set
    Pandas provides a wide array of functionalities, including but not limited to group-by operations, merging and joining data sets, time-series functionality, and input/output tools.
  • Community and Documentation
    Pandas has a strong community and extensive documentation, offering a wealth of tutorials, examples, and support for new and experienced users alike.

Possible disadvantages of Pandas

  • Memory Consumption
    Pandas can become memory inefficient with very large datasets because it relies heavily on in-memory operations.
  • Single-threaded
    Many Pandas operations are single-threaded, which can lead to performance bottlenecks when handling very large datasets.
  • Steep Learning Curve
    For users who are new to data analysis or Pandas, there can be a steep learning curve due to its extensive capabilities and complex syntax at times.
  • Less Suitable for Real-time Analytics
    Pandas is not designed for real-time analytics and is better suited for batch processing due to its in-memory operations and single-threaded nature.
  • Error Handling
    Error messages in Pandas can sometimes be cryptic and hard to interpret, making debugging a challenge for users.

Airtable videos

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

Pandas videos

Ozzy Man Reviews: Pandas

More videos:

  • Review - Ozzy Man Reviews: PANDAS Part 2
  • Review - Trash Pandas Review with Sam Healey

Category Popularity

0-100% (relative to Airtable and Pandas)
Project Management
100 100%
0% 0
Data Science And Machine Learning
No Code
100 100%
0% 0
Data Science Tools
0 0%
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 Airtable and Pandas

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.
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.
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

Pandas Reviews

25 Python Frameworks to Master
Pandas is a powerful and flexible open-source library used to perform data analysis in Python. It provides high-performance data structures (i.e., the famous DataFrame) and data analysis tools that make it easy to work with structured data.
Source: kinsta.com
Python & ETL 2020: A List and Comparison of the Top Python ETL Tools
When it comes to ETL, you can do almost anything with Pandas if you're willing to put in the time. Plus, pandas is extraordinarily easy to run. You can set up a simple script to load data from a Postgre table, transform and clean that data, and then write that data to another Postgre table.
Source: www.xplenty.com

Social recommendations and mentions

Based on our record, Pandas should be more popular than Airtable. It has been mentiond 219 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.

Airtable mentions (130)

  • How to Build Internal Tools 100x Faster
    It is possible to speed up the development and delivery process for many internal applications by using no-code or low code tools. These vary in offerings from open source to SaaS, including popular ones like AirTable, BudiBase, Retool, NocoDB and others. These can all greatly help speed up delivery times. - Source: dev.to / 5 months ago
  • Growing a side-project to 100k Unique Visitors in one week
    For the backend, I opted for Airtable as a database. It's a simple, no-code solution that I've used before. It's not the most powerful database, but it's perfect for a project like this. I could easily add, edit, and delete records, and it has an embeddable form functionality that I used for user submissions. - Source: dev.to / about 1 year ago
  • A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev
    Airtable.com — Looks like a spreadsheet, but it's a relational database unlimited bases, 1,200 rows/base, and 1,000 API requests/month. - Source: dev.to / about 1 year ago
  • How to generate links to a record to view it in an Interface?
    The ?XXXXX part of the URL identifies the type of interface page it is. Just copy that and then your formula is just "https://airtable.com.../...?XXXXXX=" & RECORD_ID() I'm not sure it works in every type of interface page (where you've started from a blank page for example). There has to be something to identify the record viewed from the page, if you see what I mean. Source: over 1 year ago
  • Working on a personal app for data tracking. looking for suggestions
    So I started building something on airtable.com that would allow me to easily track updates for each batch. What in your experience would make sense to track that I may be missing? Source: over 1 year ago
View more

Pandas mentions (219)

  • Top Programming Languages for AI Development in 2025
    Libraries for data science and deep learning that are always changing. - Source: dev.to / 7 days ago
  • How to import sample data into a Python notebook on watsonx.ai and other questions…
    # Read the content of nda.txt Try: Import os, types Import pandas as pd From botocore.client import Config Import ibm_boto3 Def __iter__(self): return 0 # @hidden_cell # The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials. # You might want to remove those credentials before you share the notebook. Cos_client = ibm_boto3.client(service_name='s3', ... - Source: dev.to / 23 days ago
  • How I Hacked Uber’s Hidden API to Download 4379 Rides
    As with any web scraping or data processing project, I had to write a fair amount of code to clean this up and shape it into a format I needed for further analysis. I used a combination of Pandas and regular expressions to clean it up (full code here). - Source: dev.to / 27 days ago
  • Must-Know 2025 Developer’s Roadmap and Key Programming Trends
    Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 3 months ago
  • Sample Super Store Analysis Using Python & Pandas
    This tutorial provides a concise and foundational guide to exploring a dataset, specifically the Sample SuperStore dataset. This dataset, which appears to originate from a fictional e-commerce or online marketplace company's annual sales data, serves as an excellent example for learning and how to work with real-world data. The dataset includes a variety of data types, which demonstrate the full range of... - Source: dev.to / 8 months ago
View more

What are some alternatives?

When comparing Airtable and Pandas, you can also consider the following products

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.

NumPy - NumPy is the fundamental package for scientific computing with 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.

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

Basecamp - A simple and elegant project management system.

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