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Amphi, visual data transformation based on Python

Hacker News

Amphi, visual data transformation based on Python

Hi everyone, I’d like to introduce Amphi, a free and source-available visual data transformation tool. Amphi is a low-code tool designed for data preparation, manipulation and ETL tasks, whether you're working with files or databases, and it supports a wide range of data transformation operations. The main difference from tools like Alteryx or Knime is that Amphi is based on Python and generates native Python code (pandas and DuckDB) that you can export and run anywhere. You also have the flexibility to use any Python libraries and integrate custom code directly into your pipeline. It is available as both a standalone application or a JupyterLab extension (see Github repo for install instructions). If you're interested don't hesitate to give it a try. You can install it via pip (Python and pip need to be installed on your laptop). Don't hesitate to star the repo and open GitHub issues if you encounter any problems or have suggestions. Amphi is still a young project, so there’s a lot that can be improved. I’d really appreciate any feedback!

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, para · Missing: reddit linkedin, podcasting, created
80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: visual, tasks, code · Missing: mac, agents, macos
69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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