Software Alternatives, Accelerators & Startups

Pandas VS Grist

Compare Pandas VS Grist and see what are their differences

Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

Pandas logo Pandas

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

Grist logo Grist

Grist makes it easy to transform spreadsheets into a custom database where data is truly actionable.
  • Pandas Landing page
    Landing page //
    2023-05-12
  • Grist Landing page
    Landing page //
    2023-08-29

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.

Grist features and specs

  • Customizability
    Grist offers flexible data models and allows users to customize data tables, formulas, and views to fit specific business needs.
  • Relational Database Capabilities
    Unlike traditional spreadsheets, Grist supports relational data models, which helps in managing complex data relationships effectively.
  • User-Friendly Interface
    The platform has a clean, intuitive interface that makes it easy for users to navigate, even those who are not technical experts.
  • Collaboration Tools
    Grist facilitates easy collaboration by allowing multiple users to work on the same dataset simultaneously, providing real-time updates.
  • Data Security
    Grist offers robust security features including encryption, access controls, and audit logs to ensure data is protected.

Possible disadvantages of Grist

  • Learning Curve
    While powerful, the advanced features of Grist may require some time for new users to learn and make the most of the platform.
  • Pricing
    For businesses needing more advanced features, the cost can be a consideration as it might be higher than simpler spreadsheet solutions.
  • Limited Pre-built Templates
    Compared to other platforms, Grist offers fewer pre-built templates, requiring users to build custom solutions from scratch more often.
  • Mobile Experience
    The mobile application is not as robust as the desktop version, which might limit its usefulness for users who prefer working on mobile devices.
  • Integration Options
    Grist has fewer native integrations with other software and services compared to some of its competitors, which might be a limitation for some users looking for seamless workflow automation.

Analysis of Pandas

Overall verdict

  • Pandas is highly recommended for tasks involving data manipulation and analysis, especially for those working with tabular data. Its efficiency and ease of use make it a staple in the data science toolkit.

Why this product is good

  • Pandas is widely considered a good library for data manipulation and analysis due to its powerful data structures, like DataFrames and Series, which make it easy to work with structured data. It provides a wide array of functions for data cleaning, transformation, and aggregation, which are essential tasks in data analysis. Furthermore, Pandas seamlessly integrates with other libraries in the Python ecosystem, making it a versatile tool for data scientists and analysts. Its extensive documentation and strong community support also contribute to its reputation as a reliable tool for data analysis tasks.

Recommended for

    Pandas is particularly recommended for data scientists, analysts, and engineers who need to perform data cleaning, transformation, and analysis as part of their work. It is also suitable for academics and researchers dealing with data in various formats and needing powerful tools for their data-driven research.

Analysis of Grist

Overall verdict

  • Grist is a powerful tool for anyone looking to manage data in a more structured and efficient way than traditional spreadsheets allow. Its adaptability and robust feature set make it a strong contender in the workspace and data management tool market.

Why this product is good

  • Grist is considered a good choice for those looking to organize their data effectively because it combines the functionality of spreadsheets with the structure of a database. It offers a user-friendly interface, customizable layouts, and strong collaboration features, making it suitable for small businesses, project management, and data analysis tasks. Furthermore, Grist has capabilities for creating custom dashboards and supports integrations with various tools, enhancing its flexibility and applicability across different use cases.

Recommended for

  • Small to medium-sized businesses looking to streamline data management
  • Teams requiring collaborative features in data handling
  • Professionals needing a flexible platform for creating custom data solutions
  • Users familiar with spreadsheet interfaces but requiring more advanced database capabilities

Pandas videos

Ozzy Man Reviews: Pandas

More videos:

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

Grist videos

Grist ๐Ÿ‘‰๐Ÿผ If Airtable, Excel, and Google Sheets had a baby

More videos:

  • Demo - Grist Labs Overview Demo
  • Review - Brewery Review Tour (Grist House)

Category Popularity

0-100% (relative to Pandas and Grist)
Data Science And Machine Learning
Spreadsheets
0 0%
100% 100
Data Science Tools
100 100%
0% 0
Databases
0 0%
100% 100

User comments

Share your experience with using Pandas and Grist. For example, how are they different and which one is better?
Log in or Post with

Reviews

These are some of the external sources and on-site user reviews we've used to compare Pandas and Grist

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

Grist Reviews

We have no reviews of Grist yet.
Be the first one to post

Social recommendations and mentions

Based on our record, Pandas seems to be a lot more popular than Grist. While we know about 220 links to Pandas, we've tracked only 9 mentions of Grist. 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.

Pandas mentions (220)

  • 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, Aโ€ฆ. - Source: dev.to / 14 days ago
  • Top Programming Languages for AI Development in 2025
    Libraries for data science and deep learning that are always changing. - Source: dev.to / 5 months ago
  • How to import sample data into a Python notebook on watsonx.ai and other questionsโ€ฆ
    # 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 / 6 months ago
  • How I Hacked Uberโ€™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 / 6 months ago
  • Must-Know 2025 Developerโ€™s Roadmap and Key Programming Trends
    Pythonโ€™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 youโ€™re experienced or just starting, Pythonโ€™s clear style makes it a good choice for diving into machine learning. Actionable Tip: If youโ€™re new to Python,... - Source: dev.to / 8 months ago
View more

Grist mentions (9)

  • Ask HN: Who is hiring? (March 2024)
    Grist Labs | Systems Engineer | Full-time | NYC OR REMOTE +/- 3hrs | https://getgrist.com We're looking for someone to make our modern spreadsheet software run everywhere. To apply, there's a puzzle. Just do:. - Source: Hacker News / over 1 year ago
  • Ask HN: What are Airtable alternatives with higher rate limits?
    [Baserow], [APITable], [Grist], and [Rowy] are all open source Airtable alternatives which offer hosted SaaS versions that include API access, though it's a bit difficult to compare the API rate limits across all these products. Self-hosting an app like this would allow you to bypass API rate limits altogether, if you're open to it. All the above products can be self-hosted โ€” and you might want to look at [NocoDB]... - Source: Hacker News / over 2 years ago
  • Retool Database
    There's also Grist (https://getgrist.com) - SQLite based with Excel-like formulae in Python. - Source: Hacker News / over 2 years ago
  • Self-hosted platform for easy access to statistical data
    The only things I have found are Baserow which is basically the best one I've found so far, but it doesn't allow search between columns, importing columns from other tables and I can't restrict users from editing and perhaps corrupting the data. NocoDB doesn't import CSVs and seems to be buggy for some reason. Grist allows restriction for people but it does not have as good filters as Baserow and I can't save my... Source: over 3 years ago
  • Check out Grist, a modern and open spreadsheet-database
    Phenomenal capabilities exceed Excel, Google Sheets, Airtable. Allows app-like views on spreadsheet data, with drag-n-drop configuration. Supports Python-based formulas with familiar Excel functions. Access rules allow sharing a single row or any subset of data. Open-source, and can be self-hosted. https://getgrist.com. Source: over 3 years ago
View more

What are some alternatives?

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

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

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

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

Rows - The spreadsheet where teams work faster

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

NocoDB - The Open Source Airtable alternative