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PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.

PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.

0:00 About PyData and being proficient in Python
4:09 Important features of Python and audience survey
7:40 Data model methods
10:14 __init__ method
11:15 __repr__ method
12:17 __add__ method
15:51 __len__ method
18:42 __call__ method
20:50 Metaclasses and the problem of writing safe library code
27:00 Contraints on the library code using assert
32:33 Compile-time vs run time executable code
34:42 Disassembling class code and using __build_class__
40:26 Constraining library code with the metaclass pattern
47:22 Introducing decorators
49:07 Code inspection
57:36 Writing a timer function
59:42 Decorator syntactic sugar
1:03:00 Writing a ntimes decorator
1:06:32 Wrapping functions into class
1:10:51 Eager execution
1:14:02 __iter__ and __next__ methods
1:17:40 yield and the Generator syntax
1:20:35 Coroutines and interleaving
1:24:35 Context Managers
1:25:30 Setup and teardown of resources
1:28:38 Applying to SQL transactions
1:30:10 __enter__ and __exit__ methods
1:32:30 Links between generators and context managers
1:36:02 Refactoring the SQL query code
1:37:52 Wrapping up
1:40:04 Opinions on writing clear code in Python
1:48:18 Q&A

S/o to https://github.com/mycaule for the video timestamps!

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