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dataprep.dev VS Pyspread

Compare dataprep.dev VS Pyspread and see what are their differences

dataprep.dev

100% local, zero uploads. Process millions of rows entirely in your browser. The ultimate privacy-first toolkit for CSV, ecommerce, and marketing data.

Rating
0 reviews
Pricing
Free
Alternatives Is it good?
Pyspread

pyspread is a non-traditional spreadsheet application that is based on and written in the programming language Python.

Rating
0 reviews
Alternatives Is it good?
Eddyter
Build and integrate a WYSIWYG editor in minutes. Eddyter offers fast setup, developer-friendly APIs, and seamless content editing for modern web apps today. sponsored

Which is more popular?

Based on our record, Pyspread seems to be more popular. It has been mentioned 7 times since March 2021.

social mentions
0 vs 7
Analytics popularity
100% vs 0%
alternatives listed
11 vs 11

Base details

Website, pricing, platforms and company facts side by side.

dataprep.dev
Pyspread
Website dataprep.dev pyspread.gitlab.io
Pricing
Free
—
Platforms
Windows Linux Mac Online +1
—
Company Startup from the United States · 1 - 9 employees · 2026 2022
Listed in
Spreadsheets Data Analysis Developer Tools Analytics Productivity Office Suites Databases No Code +5
Spreadsheets Office Suites Project Management Data Analysis +1

About dataprep.dev and Pyspread

In their own words, as submitted to SaaSHub.

dataprep.dev
Pyspread

The Browser Data Toolkit for Privacy-Conscious Professionals dataprep.dev is a 100% local, pure-browser data processing engine designed to solve the biggest headaches in data preparation: Excel crashes and privacy risks. Powered by cutting-edge DuckDB-Wasm technology, our toolkit brings...

Read more about dataprep.dev

pyspread expects Python expressions in its grid cells, which makes a spreadsheet specific language obsolete. Each cell returns a Python object that can be accessed from other cells. These objects can represent anything including lists or matrices. pyspread is free software. It is released under...

Read more about Pyspread

Features and specs

What each product offers, as listed by its team.

dataprep.dev 4 features
Pyspread 4 features
  • Data Privacy
    100% local processing (Zero Uploads). Data never leaves your browser.
  • Core Engine
    Powered by DuckDB-Wasm for database-level speeds without a backend.
  • Key Tools
    SQL on CSV, CSV Merger, GDPR Anonymizer, & JSON Flattening.
  • E-commerce Ready
    Instantly clean and flatten Shopify, Amazon, and Stripe export reports.
  • Python Integration
    Pyspread utilizes Python as its scripting language, allowing users to employ Python functions and libraries directly within the spreadsheet, enabling advanced data manipulation and analysis.
  • Free and Open Source
    Being open-source, Pyspread is free to use and modify, encouraging customization and community-driven improvements without any licensing costs.
  • Matrix Handling
    Pyspread is designed to handle cells as matrices, which can be beneficial for performing mathematical and scientific calculations more efficiently.
  • Cross-platform
    Pyspread is cross-platform, meaning it can run on various operating systems, such as Windows, macOS, and Linux, providing flexibility for users across different systems.

Possible disadvantages

  • Steep Learning Curve
    Due to its integration with Python, Pyspread may have a steep learning curve for users unfamiliar with programming, potentially restricting its accessibility.
  • Limited User Community
    Compared to more established spreadsheet software, Pyspread has a smaller user community, which may result in less available third-party support and resources.
  • Basic User Interface
    The user interface of Pyspread might not be as polished or feature-rich as mainstream spreadsheet applications, which could affect user experience and efficiency.
  • Performance Limitations
    As a lightweight application, Pyspread may struggle with extremely large datasets or complex calculations, resulting in performance bottlenecks.

Videos

Walkthroughs and reviews on video.

dataprep.dev 1 video + Add
Pyspread 0 videos + Add

Instant SQL on CSV in Browser (DuckDB-Wasm) - Zero Uploads

No Pyspread videos yet. You could help us improve this page by suggesting one.

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
dataprep.dev
Pyspread
100% 100%
Analytics
0% 0%
25% 25%
Spreadsheets
75% 75%
0% 0%
Office Suites
100% 100%
100% 100%
Databases
0% 0%

Questions & Answers

As answered by people managing dataprep.dev and Pyspread.

What's the story behind your product?

dataprep.dev's answer

I was trying to clean up a massive, messy Shopify order export. Excel kept freezing, and I absolutely refused to upload sensitive customer emails to random online converters. I got so frustrated that I decided to stop complaining and build a local-first toolkit to solve my own workflow nightmare.

Who are some of the biggest customers of your product?

dataprep.dev's answer

1, Indie hackers and solo founders. 2, Boutique digital marketing agencies. 3, Privacy-conscious data freelancers.

What makes your product unique?

dataprep.dev's answer

Most data tools force you to upload your CSVs to their servers. We don't. We compiled DuckDB into WebAssembly, meaning you get a blazing-fast, database-level engine running entirely inside your local browser tab. It's 100% private and works instantly.

Why should a person choose your product over its competitors?

dataprep.dev's answer

If you try to open a 2GB CSV in Excel, it freezes and crashes. If you use Python Pandas, you have to write code and manage environments. dataprep.dev gives you the power of code (SQL queries, regex, merging 50 files) with a simple drag-and-drop UI, without ever freezing your computer.

How would you describe the primary audience of your product?

dataprep.dev's answer

E-commerce sellers flattening messy Shopify exports, performance marketers cleaning ad reports, and data analysts who need to anonymize PII (GDPR compliance) before feeding datasets to AI models like ChatGPT.

Which are the primary technologies used for building your product?

dataprep.dev's answer

DuckDB-Wasm is the core data engine handling the heavy lifting. The frontend is built with React/Next.js and styled with Tailwind CSS. It's a modern, serverless architecture.

User comments

Share your experience with using dataprep.dev and Pyspread. For example, how are they different and which one is better?

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Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

dataprep.dev 0 mentions
Pyspread 7 mentions

Tracking dataprep.dev since Jul 2026.

  • Make little apps for you and your friends
    For an implementation which pushes that to the extreme see: https://pyspread.gitlab.io/. - Source: Hacker News / over 1 year ago
  • Comparison of Spreadsheet Software
    Curious why Quantrix Financial Modeler wasn't included? https://quantrix.com/products/quantrix-modeler pyspread would be another interesting comparison: https://pyspread.gitlab.io/ Really though, spreadsheets don't so much need features... - Source: Hacker News / over 1 year ago
  • Ask HN: Modern Day Equivalent to HyperCard?
    That sort of thing makes me wonder why pyspread isn't more popula: https://pyspread.gitlab.io/ and if mito will become popular: https://www.trymito.io/ and whether maybe Jupyter Notebooks aren't the... - Source: Hacker News / over 2 years ago

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