What idioms should I use to make my code easier to read? Read "The Python Cookbook", especially the first few chapters. It's a great source of well-written Python code examples.

Use function factories to create utility functions. Often, especially if you're using map and filter a lot, you need utility functions that convert other functions or methods to taking a single parameter. In particular, you often want to bind some data to the function once, and then apply it repeatedly to different objects. In the above example, we needed a function that multiplied a particular field of an object by 3, but what we really want is a factory that's able to return for any field name and amount a multiplier function in that family:

Use zip and dict to map fields to names. zip turns a pair of sequences into a list of tuples containing the first, second, etc. values from each sequence. For example, zip('abc', [1,2,3]) == [('a',1),('b',2),('c',3)]. You can use this to save a lot of typing when you have fields in a known order that you want to map to names:

Note that it's more rebust to run methods on the public IPython api.I.e. do ip = ipshell.api and then ip.magic('px import os')You can explore the api interactively by playing with _ip object.

In our last article on compression we showed you how to demonstrate run time encoding in Python. In this article we'll show you how to implement another kind of compression, Huffman encoding, which is useful when dealing with small sets of items, such as character strings.

StatPy: Statistical Computing with Python Welcome to StatPy, a collection of resources to help you do statistical computing with Python, with a special emphasis on astrostatistics (statistics in astronomy). This web site is brand-spanking-new, and still very much under construction; please be patient with our "dust" and check back again frequently as building continues.

> Is there a way to find the number of processors on a machine (on linux/ > windows/macos/cygwin) using python code (using the same code/cross > platform code)? From processing <http://cheeseshop.python.org/pypi/processing/0.34> : def cpuCount():     '''     Returns the number of CPUs in the system     '''     if sys.platform == 'win32':         try:             num = int(os.environ['NUMBER_OF_PROCESSORS'])         except (ValueError, KeyError):             pass     elif sys.platform == 'darwin':         try:             num = int(os.popen('sysctl -n hw.ncpu').read())         except ValueError:             pass     else:         try:             num = os.sysconf('SC_NPROCESSORS_ONLN')         except (ValueError, OSError, AttributeError):             pass     if num >= 1:         return num     else:         raise NotImplementedError --

This is something I always forget how to do, and it's kind of hard to Google or search the Python docs because you can't search for **.The point is, when using **kwargs, you have to use the ** prefix not only in the function definition, but also in the call, prefixed to the variable you want to use as a keyword dictionary.

There are two applications that require using the NumPy array type in C extension modules: Access to numerical libraries: Extension modules can be used to make numerical libraries written in C (or languages linkable to C, such as Fortran) accessible to Python programs. The NumPy array type has the advantage of using the same data layout as arrays in C and Fortran. Mixed-language numerical code: In most numerical applications, only a small part of the total code is CPU time intensive. Only this part should thus be written in C, the rest can be written in Python. NumPy arrays are important for the interface between these two parts, because they provide equally simple access to their contents from Python and from C. This document is a tutorial for using NumPy arrays in C extensions.

 > According to the fact that all Thread run on the same CPU (if i didn't  > understand wrong), i'm asking if python will suffer from the future  > multicore CPU. Will not python use only one core, then a half or a  > quarter of CPU ? It could be a serious problem for the future of python... I agree that it could potentially be a serious hindrance for cpython if "multiple core" CPUs become commonplace. This is in contrast to jython and ironpython, both of which support multiple-cpu parallelism. Although I completely accept the usual arguments offered in defense of the GIL, i.e. that it isn't a problem in the great majority of use cases, I think that position will become more difficult to defend as desktop CPUs sprout more and more execution pipelines. I think that this also fits in with AM Kuchling's recent musing/thesis/prediction that the existing cpython VM may no longer be in use in 5 years, and that it may be superceded by python "interpreters" running on top of other VMs, namely the JVM, the CLR, Smalltalk VM, Parrot, etc, etc, etc. http://www.amk.ca/diary/archives/cat_python.html#003382 I too agree with Andrew's basic position: the Python language needs a period of library consolidation. There is so much duplication of functionality out there, with the situation only getting worse as people re-invent the wheel yet again using newer features such generators, gen-exps and decorators.

SendKeys SendKeys(keys, pause = 0.05, with_spaces = False, with_tabs = False, with_newlines = False, turn_off_numlock = True) Parameters keys : str A string of keys. pause : float The number of seconds to wait between sending each key or key combination. with_spaces : bool Whether to treat spaces as {SPACE}. If False, spaces are ignored. with_tabs : bool Whether to treat tabs as {TAB}. If False, tabs are ignored. with_newlines : bool Whether to treat newlines as {ENTER}. If False, newlines are ignored. turn_off_numlock : bool Whether to turn off NUMLOCK before sending keys.

Key Code BACKSPACE {BACKSPACE}, {BS}, or {BKSP} BREAK {BREAK} CAPS LOCK {CAPSLOCK} or {CAP} DEL or DELETE {DELETE} or {DEL} DOWN ARROW {DOWN} END {END} ENTER {ENTER} or ~ ESC {ESC} HELP {HELP} HOME {HOME} INS or INSERT {INSERT} or {INS} LEFT ARROW {LEFT} NUM LOCK {NUMLOCK} PAGE DOWN {PGDN} PAGE UP {PGUP} PRINT SCREEN {PRTSC} RIGHT ARROW {RIGHT} SCROLL LOCK {SCROLLLOCK} SPACE BAR {PACE} TAB {TAB} UP ARROW {UP} F1 {F1} F2 {F2} F3 {F3} F4 {F4} F5 {F5} F6 {F6} F7 {F7} F8 {F8} F9 {F9} F10 {F10} F11 {F11} F12 {F12} F13 {F13} F14 {F14} F15 {F15} F16 {F16} F17 {F17} F18 {F18} F19 {F19} F20 {F20} F21 {F21} F22 {F22} F23 {F23} F24 {F24} Keypad add {ADD} Keypad subtract {SUBTRACT} Keypad multiply {MULTIPLY} Keypad divide {DIVIDE} Left Windows(R) {LWIN} Right Windows(R) {RWIN}

Charming Python: Using state machinesAlgorithms and programming approaches in Python

Charming Python: SimPy simplifies complex models