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6.5.1 Itertool functions The following module functions all construct and return iterators. Some provide streams of infinite length, so they should only be accessed by functions or loops that truncate the stream.

PyScripter is a free and open-source Python Integrated Development Environment (IDE) created with the ambition to become competitive in functionality with commercial Windows-based IDEs available for other languages. Being built in a compiled language is rather snappier than some of the other Python IDEs and provides an extensive blend of features that make it a productive Python development environment. 

interpreter:>>>import matplotlib>>>matplotlib.interactive(True)>>>matplotlib.use("WXAgg")>>>from matplotlib.pylab import *>>>plot([1,2,3])>>>xlabel('time (s)') 

PyScripter is a free and open-source Python Integrated Development Environment (IDE) created with the ambition to become competitive in functionality with commercial Windows-based IDEs available for other languages. Being built in a compiled language is rather snappier than some of the other Python IDEs and provides an extensive blend of features that make it a productive Python development environment.

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.

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}

What's the relation between these benefits and Stackless's implementation details? Here's a quick sketch: Continuations are the general-purpose concurrency construct. A continuation represents all the future computations of a particular program. Capturing all this control flow in a single conceptual object makes it programmable: It becomes possible to calculate or reason over the control flow. In particular, there's great scope for optimizing assignment of different calculations to different processes or threads or even hosts.