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PyCon US 2014

вт 29 апреля 2014, tags: pythonpyconcanada

PyCon is over. This year the conference was at Montreal, Canada. The country looks pretty much as Russia from the window of airplane and city is very european. In comparison with PyCons I've visited before it was my biggest PyCon. I was impressed with how many people do use iPython[ notebook]. I have learned a lot of new stuff there and met many new people. Hope I will visit PyCon next year again.

All Your Ducks In A Row: Data Structures in the Standard Library and Beyond

A great talk. Brandon explained how does work and what you should expect from standard types and libraries.

There're several notes:

list is an array of references

class is a dict

implementation __slots__ converts an object to the struct (takes less memory)

And also provided a list of libraries what you could know but have never use:



Must have to watch for every pythonista

Rate: 10/10


Cache me if you can: memcached, caching patterns and best practices

As described in title this talk has covered various cashing patterns and best practices.

Tip: use versioning for cashing you data

Rate: 10/10


Import-ant Decisions

Basic example how you can implement your own import system. Watch just for fun.

Rate: 4/10


Enough Machine Learning to Make Hacker News Readable Again

A good example how you can spend 6 months to filter HN clicking on your own "like" button.

Rate: Silicon Valley^hipster/10


How to Get Started with Machine Learning

Description and introduction into things you necessary to know if you want to dive into machine learning.

Rate: Silicon Valley/10


Realtime predictive analytics using scikit-learn & RabbitMQ

Explained on example how to build the system using scikit-learn and RabbitMQ. Can be useful who is joining the rabbitmq + scikit-learn world.

Rate: 8/10


Distributed Computing Is Hard, Lets Go Shopping

Greates talk I've visited on this PyCon. Must have to watch for people is who working on distributed data processing. You will learn what issues you can meet and how to solve it, how to do proper testing and monitoring.


testing: test task function, test single task, test 1 process, test several processes to avoid race condition monitoring: celery - flower, rabbitmq - rabbitmq management

Rate: 100/10


Fan-in and Fan-out: The crucial components of concurrency

import asyncio

Rate: asyncio/10


Track memory leaks in Python

Must have to watch every pythonista. Explained GC in python and techniques of debugging and detecting memory leaks.

objgraph -

memory_profiler -

pytracemalloc -

tracemallocqt -

Tip: keep eyes on object's references

Rate: 10/10


Garbage Collection in Python

If you trying to work with many objects and want to help python handle all manually this talk is good for you.

Tip: it happens rarely but when it happened, you need to be ready

Rate: 10/10


Designing Poetic APIs

Explanation on example how to make your code, API, UI cleaner and better. Must have to watch

Rate: 10/10


Introduction to SQLAlchemy Core

Use cases and examples how to use SQLAlchemy without using ORM.

Tip: use declarative for convinient interaction with existing databases (e.g. migrations or data extract).

Rate: 7/10


Sane schema migrations with Alembic and SQLAlchemy

Migrations for SQLAlchemy.


Rate: 10/10


In Depth PDB

Tips & tricks.

Tip: install pdb++

Rate: 10/10


Python packaging simplified, for end users, app developers, and open source contributors

Described techniques and examples of packaging libraries in Python. Make your work and deploy easier. Must have.

Tip: vagrant

Rate: wheels/10


What is coming in Python packaging

Explained current state and the future of Python packaging systems.

PYPI's hidden gems<name>/json<name>/<ver>/json

Tip: do not use wheels if you have C extensions

Rate: 10/10


Deliver Your Software In An Envelope

Good talk about designing, delivering code and how good documentation is important. Must have to watch.

Rate: 10/10


PostgreSQL is Web Scale (Really :) )

Exaplained how to scale and use classic RDBMS.

Tip: PostgreSQL is enough for 90% of projects.

Rate: 10/10


Set your code free: releasing and maintaining an open-source Python project

Deliver your stuff properly! Must have to watch.

Rate: Github/10


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