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JupySQL – a SQL client for Jupyter (ipython-SQL successor)

Hacker News

JupySQL – a SQL client for Jupyter (ipython-SQL successor)

Hey, HN community! We're stoked to launch JupySQL today! JupySQL is an open-source library that brings a modern SQL experience to Jupyter. JupySQL is compatible with all major databases, such as Snowflake, Redshift, PostgreSQL, MySQL, MariaDB, DuckDB, SQL Server, Clickhouse, Trino, and more! To get started, check out our tutorial: https://jupysql.ploomber.io/en/latest/quick-start.html SQL is the defacto language for data analysis; however, analysis often requires a mix of SQL and Python. JupySQL bridges this gap, allowing users to execute SQL queries seamlessly in Jupyter and continue their analysis in Python. Add %%sql to the top of your cell and start writing SQL. Here are some of JupySQL's main features: - Syntax highlighting - Formatting - Autocompletion - A GUI for connection management - Scalable plotting engine - Integration with pandas and polars - One-click sharing powered by Ploomber Cloud: https://ploomber.io Documentation: https://jupysql.ploomber.io Note that JupySQL is a fork of ipython-sql; which is no longer actively developed. Catherine, ipython-sql's creator, was kind enough to pass the project to us (check out ipython-sql's README). We'd love to learn what you think and what features we can ship for JupySQL to be the best SQL client! Please let us know in the comments!

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, compatible · Missing: supports, reddit linkedin, podcasting
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.
Hacker NewsStrong engagement from HN community · Strong signals: clickhouse, io · Missing: https docs, excited, just released
66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, open · Missing: mac, agents, macos
44%44% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
31%31% 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
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
13%13% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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