arogozhnikov/python3_with_pleasure: A short guide on features of Python 3


58 bookmarks. First posted by xurxosanz january 2018.


Python became a mainstream language for machine learning and other scientific fields that heavily operate with data; it boasts various deep learning frameworks and well-established set of tools for data processing and visualization.
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28 days ago by ronert
Python became a mainstream language for machine learning and other scientific fields that heavily operate with data; it boasts various deep learning frameworks and well-established set of tools for data processing and visualization. via Pocket
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5 weeks ago by regisd
Python became a mainstream language for machine learning and other scientific fields that heavily operate with data; it boasts various deep learning frameworks and well-established set of tools for data processing and visualization.
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february 2018 by linkt
A short guide on features of Python 3 for data scientists
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february 2018 by dimero
Python became a mainstream language for machine learning and other scientific fields that heavily operate with data; it boasts various deep learning frameworks and well-established set of tools for data processing and visualization.
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february 2018 by serkef
👍『Migrating to Python3 with pleasure - arogozhnikov/python3_with_pleasure』
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february 2018 by iany
Migrating to Python 3. With pleasure by via Hacker News http://ift.tt/2sd47jR
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february 2018 by david3smith
python3_with_pleasure - A short guide on features of Python 3
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february 2018 by nezz
python3_with_pleasure - A short guide on features of Python 3
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february 2018 by geetarista
flobosg starred arogozhnikov/python3_with_pleasure
february 2018 by flobosg
Migrating to Python 3 with pleasure - A short guide on features of Python 3 for data scientists
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february 2018 by vscarpenter
Migrating to Python 3 with pleasure

A short guide on features of Python 3 for data scientists

Python became a mainstream language for machine learning and other scientific fields that heavily operate with data; it boasts various deep learning frameworks and well-established set of tools for data processing and visualization.

However, Python ecosystem co-exists in Python 2 and Python 3, and Python 2 is still used among data scientists. By the end of 2019 the scientific stack will stop supporting Python2. As for numpy, after 2018 any new feature releases will only support Python3.

To make the transition less frustrating, I've collected a bunch of Python 3 features that you may find useful.
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february 2018 by euler
Migrating to Python 3 with pleasure
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february 2018 by rukku
Previously different modules used custom ways to point types in doctrings (Hint: pycharm can convert old docstrings to fresh typehinting). In jupyter it is desirable to log each output to a separate file (to track what's happening after you got disconnected), so you can override print now. Actually similar compression (but not speed) is achievable with protocol=2 parameter, but users typically ignore this option (or simply not aware of it). Research and production code should become a bit shorter, more readable, and significantly safer after moving to Python 3-only codebase. And I can't wait for the bright moment when packages drop support for Python 2 and enjoy new language features.
january 2018 by sechilds
joshuarubin starred arogozhnikov/python3_with_pleasure on
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january 2018 by joshuarubin
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january 2018 by tebeka