Pandera – An open source data testing framework for data science and ML

Hacker News

Hi HN, I'm excited to share Pandera, an open source framework for data testing, built for data scientists and ML engineers: https://www.union.ai/pandera I’ve been working with data and building models for a decade, and one of the biggest pain points for me is working with low-quality data. I got burned by incorrect data types and unexpected values so many times that I built a Pandera to help you safeguard your pipelines from silent data bugs. You can create schemas for your dataframe-like objects, which can be validated at run-time, in your unit tests via property-based testing, or in your type-linting hooks via mypy. Give it a try here: https://pandera.readthedocs.io/en/stable/try_pandera.html

Visit product View on Hacker NewsOct 5, 2022

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, open · Missing: mac, agents, macos

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, open source, pipe · Missing: https docs, just released, exist

68%68% 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 · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% 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.

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