Python

From YYiki

• http://www.python.org/
• http://www.cheat-sheets.org/saved-copy/PQRC-2.4-A4-latest.pdf

Python is a very useful programming language in many occasions except when you need really fast program.

IPython provides very nice functionalities such as IPython notebook.

There are several implementations of Python. CPython is currently the official implementation, but PyPy (written in Python, see also RPython) is catching up very fast and it is now the fastest implementation of Python.

You may want to install pre-packaged scientific python distribution such as enthought python because it is a pain to install all scientific packages.

• Python syntax and semantics

Contents 1 Modules & Libraries 1.1 Database 1.2 Documentation 1.3 Time stamps 1.4 File system 1.5 Etc 1.6 Profiling 1.7 Option parser 1.8 Parallel computing 1.9 plotting 1.10 Social media 1.11 Data analysis and Machine learning 1.11.1 Location data 1.11.2 Etc 1.12 Web mining 1.13 Web application framework 1.14 Testing 2 Topics and tips 3 Links 4 Python guides 5 Articles

Modules & Libraries

• http://pythonpackages.com/ - What is your favorite Python package?
• perky:파이썬 모듈 관광 1회: ezpyinline
  • 파이썬 모듈 관광 2회: imputil
  • 파이썬 모듈 관광 3회: durus
  • 파이썬 모듈 관광 4회: 기본제공 자료구조 겉핥기!
• pip

Database

• SQLAlchemy
• atbr - large-scale in-memory key-value pair store for Python

Documentation

• Sphinx (documentation generator)

Time stamps

• http://labix.org/python-dateutil

File system

• http://packages.python.org/watchdog/ - monitor file system events. can be used for automatic testing

Etc

• select module

Profiling

• profile, cProfile, and pstats – Performance analysis of Python programs

Option parser

• https://github.com/halst/docopt - generating option parser from the docstring.

Parallel computing

• Python/multiprocessing
• execnet

plotting

• matplotlib

Social media

• Tweepy (Twitter)

Data analysis and Machine learning

• Machine Learning Libraries in Python
• http://fonnesbeck.github.com/ScipySuperpack/ - numpy + scipy + matplotlib + ipython + pandas + pyMC
• numpy
• Python/Pandas
• StatsModel
• Orange (python)
• http://pybrain.org/
• scikit-learn
• mlpy
• pyml
• Shogun - A Large Scale Machine Learning Toolbox
• Modular toolkit for data processing
• MILK: MACHINE LEARNING TOOLKIT

Location data

• Geocoding libraries

Etc

• DendroPy - Python library for phylogenetic computing.

Web mining

Web application framework

Testing

• Python/nose
• Tox - http://tox.readthedocs.org/en/latest/index.html
• attest - Modern Test Automation for Python

Topics and tips

• general:
  • Google style guide
  • Code Like a Pythonista: Idiomatic Python by David Goodger
  • hidden features: http://stackoverflow.com/questions/101268/hidden-features-of-python/143636
  • perky:구글코리아 테크토크: 파이썬 - 상상력에 날개 달기
  • perky:구글에서는 파이썬을 어디에 쓰나요
  • python idioms

• datetime: http://stackoverflow.com/questions/1060279/iterating-through-a-range-of-dates-in-python
  • “Eppur si muove!”* – Dealing with Timezones in Python

• defaultdict: http://docs.python.org/library/collections.html#defaultdict-examples Large defaultdict is slow. Using defaultdict is sometimes dangerous.

• flatten: from Tkinter import _flatten or http://rightfootin.blogspot.com/2006/09/more-on-python-flatten.html

• generator:
  • http://blog.dahlia.pe.kr/articles/2009/09/15/python-제너레이터반복자의-마법

• Information visualization:
  • vpython - 3d drawing

• JSON: If JSON string is simple and contains only ascii characters, you can simply do following: a = eval(somejson) If your JSON contains unicode characters, you should use json module. json.loads('["foo", {"bar":["baz", null, 1.0, 2]}]')

• logging:
  • http://plumberjack.blogspot.com/2010/12/colorizing-logging-output-in-terminals.html

• low-level:
  • perky:파이썬 디스어셈블러를 이용해서 동작 이해하기

• main(): Python main() functions

• Optimization:
  • 혀나미:Python과 C
  • 파이썬 속도에 절망감을 느끼면서

    보여주신 코드는 대량의 계산이 발생하는 부분인데 그런 것은 numpy같은 것으로 행렬연산으로 처리하지 않는 한은 C 속도의 500분의 1정도 밖에 나오지 않습니다. 행렬연산으로 처리하면 거의 같은 속도가 나오니까 별 걱정하실 필요는 없습니다. 행렬로 처리가 안 되는 경우에는 보통 pyrex, cython 같은 것으로 C 자료형으로 확장하거나, C로 일부분을 짠 다음에 SWIG, ctypes 등으로 불러다가 쓰는 방법이 많이 쓰입니다. 파이썬은 모든것을 다 최적으로 할 수 있는 언어가 아니라 특정 일부 작업을 빨리 처리할 수 있는 언어입니다. 파이썬 초창기에 모든 작업을 파이썬으로 해야한다는 순혈주의(?)가 종종 일부 극단적인 분들에게 있었지만, 파이썬은 종교가 아닙니다. 적재적소에 필요한 만큼 쓰는게 중요합니다. 특히 수치연산은 numpy나 scipy같은 수치처리 모듈이나 C와 파이썬을 같이 쓰는 방법을 알아두시면 매우 도움이 됩니다. --perky

  • psyco: "Psyco is a Python extension module which can greatly speed up the execution of any Python code."
    • The Ultimate Psyco Guide

• Packaging & distribution: easy_install and .egg were used as python package installer and package format respectively. Pip replaces easy_install.
  • The Hitchhiker’s Guide to Packaging
  • How To Package Your Python Code

• Parallel programming and GlL (Global Interpreter Lock):
  • perky:여러 프로세스로 분산해 주는 제너레이터
  • http://us.pycon.org/media/2010/talkdata/PyCon2010/076/UnderstandingGIL.pdf
  • perky:"파이썬은 멀티코어 줘도 쓰잘데기가 없나요?"에 대한 파이썬 2.6의 대답

• type: 변수가 정수인지 리스트인지 알고 싶습니다.

• Universal newline support: the newline character differs in Windows/Unix. Regardless of newline character type, you can open files with 'U' or 'rU'. sample = open(filename, 'rU')

• Vim:
  • http://stackoverflow.com/questions/501585/how-can-you-use-python-in-vim

• Web server
  • python -m SimpleHTTPServer

• with: http://effbot.org/zone/python-with-statement.htm

Links

• 파이썬 마을 게시판
• http://www.pythonchallenge.com/
• http://openbookproject.net/py4fun/

Python guides

• python by example
• guido's 파이썬 자습서
• alan gould:파이썬 프로그래밍 연습
• 컴퓨터 과학자 같이 생각하는 법
• 파이썬에 뛰어들기
• Learn Python The Hard Way
• http://www.pythontutor.com - LEARN programming by visualizing code execution
• http://www.pythonforbeginners.com

Articles

• 10 Reasons Python Rocks for Research
• Writing forwards compatible python code
• PEP 20 (The Zen of Python) by example
• Python is Cool (and Perl is not), Especially for C/C++ Programmers
• reddit:What would someone have to know to be classified as a 'python expert'?
• Python Iteration