Weekly Pandas exercises based on current events and public data

Hacker News

I've just launched Bamboo Weekly ( https://www.bambooweekly.com/ ), a newsletter that improves your Pandas skills, one week at a time. Every Wednesday, I take a topic from the news, point you to a public data set on that topic, and ask some questions. The next day, I show you how I would answer those questions with Pandas. Paid subscribers can comment, share code and ideas, and (of course) correct me when I'm wrong. I'm very excited about this, and am looking forward to help lots of Pandas users to improve their fluency in data analytics. I'm open to suggestions about topics, data sets, and Pandas features to explore, as well as the length, timing, and pricing.

Visit product View on Hacker NewsFeb 26, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, code · Missing: mac, agents, macos

62%62% 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

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, io · Missing: https docs, just released, exist

45%45% 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 · 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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · 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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