Python Crash Course

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Python Crash Course

Over the past two years I've been working on Python Crash Course, which is available now through No Starch Press. The first half of the book is an introduction to Python, and the second half is a series of projects that build on the concepts introduced in the first half. Readers can make a Space Invaders clone with Pygame, complete a series of data visualizations using matplotlib and Pygal, and build a simple web app with Django. Many HN readers already work with Python; I'm introducing it here because my experience in the HN community has been an overwhelmingly positive influence. When I was working on the seventh or eighth draft of a chapter, reading about the hard work others are putting into their projects helped me stay focused. If you're a non-technical reader who's been thinking of learning Python, please take a look at Python Crash Course. If you already know Python, please consider recommending it to people who ask how they can get started with Python. I'm also quite interested in hearing feedback from experienced programmers who look through the book. If you want to order the book, the code CRASHCOURSE will get you 30% off when you order directly through the No Starch Press site. Thanks HN for your support and encouragement, and I'm happy to answer any questions about the book or the process of writing an introductory Python book.

Visit product View on Hacker NewsDec 15, 2015

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best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

70%70% 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 HuntOn track for Day 1 leaderboard · Strong signals: visual, using, code · Missing: mac, agents, macos

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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16%16% 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.

Incorrect prediction on native model

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