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I got tired of writing Pandas scripts just to JOIN two CSV files

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I got tired of writing Pandas scripts just to JOIN two CSV files

Every few weeks I get 2-3 CSV files and need to run a quick JOIN or aggregation. The options are always the same: fire up pandas, import into SQLite by hand, or use some janky web tool. So I built a plugin for Tabularis (my native DB GUI) that treats a folder of CSV files as a database. Drop your files in, every file becomes a table, column types are inferred automatically, and you get a full SQL editor. No code. No imports. Just SQL. Example: 3 CSV files (users, orders, products) → this query works immediately: SELECT u.name, u.country, COUNT(o.id) AS orders, ROUND(SUM(p.price * o.quantity), 2) AS total_spent FROM orders o JOIN users u ON o.user_id = u.id JOIN products p ON o.product_id = p.id GROUP BY u.id ORDER BY total_spent DESC The plugin is ~250 lines of stdlib Python (zero dependencies). It works because Tabularis has an open plugin protocol — JSON-RPC over stdin/stdout — so any data source can be a database. Plugin: https://github.com/debba/tabularis-csv-plugin Tabularis: https://github.com/debba/tabularis

Visit productView on Hacker NewsFeb 25, 2026
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
53%53% 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.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, code, open · Missing: mac, agents, macos
32%32% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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