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Merged revisions 78207 via svnmerge from svn+ssh://pythondev@svn.python.org/python/trunk ........ r78207 | ezio.melotti | 2010-02-17 01:26:09 +0200 (Wed, 17 Feb 2010) | 1 line #7930: fix stripid ........
author Ezio Melotti <ezio.melotti@gmail.com>
date Tue, 16 Feb 2010 23:29:44 +0000
parents 197f243ddab5
children 38e9b9da0164
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1 :tocdepth: 2
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3 ===============
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4 Programming FAQ
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5 ===============
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7 .. contents::
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9 General Questions
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10 =================
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12 Is there a source code level debugger with breakpoints, single-stepping, etc.?
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13 ------------------------------------------------------------------------------
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15 Yes.
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17 The pdb module is a simple but adequate console-mode debugger for Python. It is
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18 part of the standard Python library, and is :mod:`documented in the Library
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19 Reference Manual <pdb>`. You can also write your own debugger by using the code
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20 for pdb as an example.
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22 The IDLE interactive development environment, which is part of the standard
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23 Python distribution (normally available as Tools/scripts/idle), includes a
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24 graphical debugger. There is documentation for the IDLE debugger at
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25 http://www.python.org/idle/doc/idle2.html#Debugger.
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27 PythonWin is a Python IDE that includes a GUI debugger based on pdb. The
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28 Pythonwin debugger colors breakpoints and has quite a few cool features such as
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29 debugging non-Pythonwin programs. Pythonwin is available as part of the `Python
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30 for Windows Extensions <http://sourceforge.net/projects/pywin32/>`__ project and
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31 as a part of the ActivePython distribution (see
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32 http://www.activestate.com/Products/ActivePython/index.html).
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34 `Boa Constructor <http://boa-constructor.sourceforge.net/>`_ is an IDE and GUI
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35 builder that uses wxWidgets. It offers visual frame creation and manipulation,
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36 an object inspector, many views on the source like object browsers, inheritance
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37 hierarchies, doc string generated html documentation, an advanced debugger,
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38 integrated help, and Zope support.
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39
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40 `Eric <http://www.die-offenbachs.de/eric/index.html>`_ is an IDE built on PyQt
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41 and the Scintilla editing component.
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43 Pydb is a version of the standard Python debugger pdb, modified for use with DDD
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44 (Data Display Debugger), a popular graphical debugger front end. Pydb can be
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45 found at http://bashdb.sourceforge.net/pydb/ and DDD can be found at
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46 http://www.gnu.org/software/ddd.
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48 There are a number of commercial Python IDEs that include graphical debuggers.
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49 They include:
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50
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51 * Wing IDE (http://wingware.com/)
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52 * Komodo IDE (http://www.activestate.com/Products/Komodo)
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55 Is there a tool to help find bugs or perform static analysis?
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56 -------------------------------------------------------------
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58 Yes.
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60 PyChecker is a static analysis tool that finds bugs in Python source code and
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61 warns about code complexity and style. You can get PyChecker from
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62 http://pychecker.sf.net.
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63
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64 `Pylint <http://www.logilab.org/projects/pylint>`_ is another tool that checks
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65 if a module satisfies a coding standard, and also makes it possible to write
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66 plug-ins to add a custom feature. In addition to the bug checking that
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67 PyChecker performs, Pylint offers some additional features such as checking line
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68 length, whether variable names are well-formed according to your coding
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69 standard, whether declared interfaces are fully implemented, and more.
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70 http://www.logilab.org/card/pylint_manual provides a full list of Pylint's
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71 features.
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72
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73
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74 How can I create a stand-alone binary from a Python script?
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75 -----------------------------------------------------------
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76
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77 You don't need the ability to compile Python to C code if all you want is a
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78 stand-alone program that users can download and run without having to install
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79 the Python distribution first. There are a number of tools that determine the
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80 set of modules required by a program and bind these modules together with a
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81 Python binary to produce a single executable.
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82
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83 One is to use the freeze tool, which is included in the Python source tree as
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84 ``Tools/freeze``. It converts Python byte code to C arrays; a C compiler you can
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85 embed all your modules into a new program, which is then linked with the
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86 standard Python modules.
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87
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88 It works by scanning your source recursively for import statements (in both
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89 forms) and looking for the modules in the standard Python path as well as in the
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90 source directory (for built-in modules). It then turns the bytecode for modules
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91 written in Python into C code (array initializers that can be turned into code
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92 objects using the marshal module) and creates a custom-made config file that
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93 only contains those built-in modules which are actually used in the program. It
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94 then compiles the generated C code and links it with the rest of the Python
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95 interpreter to form a self-contained binary which acts exactly like your script.
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96
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97 Obviously, freeze requires a C compiler. There are several other utilities
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98 which don't. One is Thomas Heller's py2exe (Windows only) at
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99
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100 http://www.py2exe.org/
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101
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102 Another is Christian Tismer's `SQFREEZE <http://starship.python.net/crew/pirx>`_
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103 which appends the byte code to a specially-prepared Python interpreter that can
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104 find the byte code in the executable.
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105
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106 Other tools include Fredrik Lundh's `Squeeze
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107 <http://www.pythonware.com/products/python/squeeze>`_ and Anthony Tuininga's
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108 `cx_Freeze <http://starship.python.net/crew/atuining/cx_Freeze/index.html>`_.
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109
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110
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111 Are there coding standards or a style guide for Python programs?
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112 ----------------------------------------------------------------
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113
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114 Yes. The coding style required for standard library modules is documented as
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115 :pep:`8`.
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116
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117
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118 My program is too slow. How do I speed it up?
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119 ---------------------------------------------
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120
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121 That's a tough one, in general. There are many tricks to speed up Python code;
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122 consider rewriting parts in C as a last resort.
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123
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124 In some cases it's possible to automatically translate Python to C or x86
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125 assembly language, meaning that you don't have to modify your code to gain
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126 increased speed.
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127
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128 .. XXX seems to have overlap with other questions!
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129
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130 `Pyrex <http://www.cosc.canterbury.ac.nz/~greg/python/Pyrex/>`_ can compile a
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131 slightly modified version of Python code into a C extension, and can be used on
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132 many different platforms.
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133
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134 `Psyco <http://psyco.sourceforge.net>`_ is a just-in-time compiler that
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135 translates Python code into x86 assembly language. If you can use it, Psyco can
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136 provide dramatic speedups for critical functions.
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137
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138 The rest of this answer will discuss various tricks for squeezing a bit more
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139 speed out of Python code. *Never* apply any optimization tricks unless you know
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140 you need them, after profiling has indicated that a particular function is the
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141 heavily executed hot spot in the code. Optimizations almost always make the
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142 code less clear, and you shouldn't pay the costs of reduced clarity (increased
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143 development time, greater likelihood of bugs) unless the resulting performance
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144 benefit is worth it.
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145
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146 There is a page on the wiki devoted to `performance tips
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147 <http://wiki.python.org/moin/PythonSpeed/PerformanceTips>`_.
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148
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149 Guido van Rossum has written up an anecdote related to optimization at
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150 http://www.python.org/doc/essays/list2str.html.
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151
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152 One thing to notice is that function and (especially) method calls are rather
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153 expensive; if you have designed a purely OO interface with lots of tiny
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154 functions that don't do much more than get or set an instance variable or call
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155 another method, you might consider using a more direct way such as directly
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156 accessing instance variables. Also see the standard module :mod:`profile` which
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157 makes it possible to find out where your program is spending most of its time
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158 (if you have some patience -- the profiling itself can slow your program down by
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159 an order of magnitude).
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160
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161 Remember that many standard optimization heuristics you may know from other
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162 programming experience may well apply to Python. For example it may be faster
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163 to send output to output devices using larger writes rather than smaller ones in
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164 order to reduce the overhead of kernel system calls. Thus CGI scripts that
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165 write all output in "one shot" may be faster than those that write lots of small
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166 pieces of output.
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167
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168 Also, be sure to use Python's core features where appropriate. For example,
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169 slicing allows programs to chop up lists and other sequence objects in a single
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170 tick of the interpreter's mainloop using highly optimized C implementations.
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171 Thus to get the same effect as::
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172
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173 L2 = []
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174 for i in range[3]:
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175 L2.append(L1[i])
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176
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177 it is much shorter and far faster to use ::
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178
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179 L2 = list(L1[:3]) # "list" is redundant if L1 is a list.
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180
58796
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181 Note that the functionally-oriented built-in functions such as :func:`map`,
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182 :func:`zip`, and friends can be a convenient accelerator for loops that
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183 perform a single task. For example to pair the elements of two lists
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184 together::
56824
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185
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186 >>> zip([1,2,3], [4,5,6])
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187 [(1, 4), (2, 5), (3, 6)]
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188
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189 or to compute a number of sines::
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190
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191 >>> map( math.sin, (1,2,3,4))
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192 [0.841470984808, 0.909297426826, 0.14112000806, -0.756802495308]
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193
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194 The operation completes very quickly in such cases.
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195
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196 Other examples include the ``join()`` and ``split()`` methods of string objects.
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197 For example if s1..s7 are large (10K+) strings then
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198 ``"".join([s1,s2,s3,s4,s5,s6,s7])`` may be far faster than the more obvious
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199 ``s1+s2+s3+s4+s5+s6+s7``, since the "summation" will compute many
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200 subexpressions, whereas ``join()`` does all the copying in one pass. For
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201 manipulating strings, use the ``replace()`` method on string objects. Use
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202 regular expressions only when you're not dealing with constant string patterns.
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203 Consider using the string formatting operations ``string % tuple`` and ``string
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204 % dictionary``.
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205
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206 Be sure to use the :meth:`list.sort` built-in method to do sorting, and see the
56824
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207 `sorting mini-HOWTO <http://wiki.python.org/moin/HowTo/Sorting>`_ for examples
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208 of moderately advanced usage. :meth:`list.sort` beats other techniques for
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209 sorting in all but the most extreme circumstances.
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210
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211 Another common trick is to "push loops into functions or methods." For example
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212 suppose you have a program that runs slowly and you use the profiler to
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213 determine that a Python function ``ff()`` is being called lots of times. If you
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214 notice that ``ff ()``::
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215
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216 def ff(x):
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217 ... # do something with x computing result...
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218 return result
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219
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220 tends to be called in loops like::
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221
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222 list = map(ff, oldlist)
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223
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224 or::
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225
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226 for x in sequence:
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227 value = ff(x)
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228 ... # do something with value...
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229
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230 then you can often eliminate function call overhead by rewriting ``ff()`` to::
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231
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232 def ffseq(seq):
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233 resultseq = []
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234 for x in seq:
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235 ... # do something with x computing result...
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236 resultseq.append(result)
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237 return resultseq
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238
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239 and rewrite the two examples to ``list = ffseq(oldlist)`` and to::
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240
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241 for value in ffseq(sequence):
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242 ... # do something with value...
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243
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244 Single calls to ``ff(x)`` translate to ``ffseq([x])[0]`` with little penalty.
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245 Of course this technique is not always appropriate and there are other variants
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246 which you can figure out.
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247
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248 You can gain some performance by explicitly storing the results of a function or
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249 method lookup into a local variable. A loop like::
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250
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251 for key in token:
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252 dict[key] = dict.get(key, 0) + 1
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253
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254 resolves ``dict.get`` every iteration. If the method isn't going to change, a
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255 slightly faster implementation is::
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256
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257 dict_get = dict.get # look up the method once
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258 for key in token:
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259 dict[key] = dict_get(key, 0) + 1
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260
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261 Default arguments can be used to determine values once, at compile time instead
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262 of at run time. This can only be done for functions or objects which will not
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263 be changed during program execution, such as replacing ::
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264
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265 def degree_sin(deg):
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266 return math.sin(deg * math.pi / 180.0)
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267
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268 with ::
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269
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270 def degree_sin(deg, factor=math.pi/180.0, sin=math.sin):
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271 return sin(deg * factor)
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272
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273 Because this trick uses default arguments for terms which should not be changed,
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274 it should only be used when you are not concerned with presenting a possibly
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275 confusing API to your users.
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276
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277
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278 Core Language
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279 =============
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280
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281 Why am I getting an UnboundLocalError when the variable has a value?
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282 --------------------------------------------------------------------
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283
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284 It can be a surprise to get the UnboundLocalError in previously working
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285 code when it is modified by adding an assignment statement somewhere in
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286 the body of a function.
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287
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288 This code:
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289
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290 >>> x = 10
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291 >>> def bar():
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292 ... print x
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293 >>> bar()
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294 10
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295
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296 works, but this code:
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297
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298 >>> x = 10
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299 >>> def foo():
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300 ... print x
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301 ... x += 1
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302
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303 results in an UnboundLocalError:
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304
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305 >>> foo()
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306 Traceback (most recent call last):
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307 ...
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308 UnboundLocalError: local variable 'x' referenced before assignment
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309
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310 This is because when you make an assignment to a variable in a scope, that
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311 variable becomes local to that scope and shadows any similarly named variable
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312 in the outer scope. Since the last statement in foo assigns a new value to
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313 ``x``, the compiler recognizes it as a local variable. Consequently when the
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314 earlier ``print x`` attempts to print the uninitialized local variable and
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315 an error results.
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316
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317 In the example above you can access the outer scope variable by declaring it
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318 global:
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319
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320 >>> x = 10
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321 >>> def foobar():
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322 ... global x
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323 ... print x
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324 ... x += 1
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325 >>> foobar()
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326 10
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327
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328 This explicit declaration is required in order to remind you that (unlike the
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329 superficially analogous situation with class and instance variables) you are
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330 actually modifying the value of the variable in the outer scope:
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331
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332 >>> print x
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333 11
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334
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335 In Python3, you can do a similar thing in a nested scope using the
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336 :keyword:`nonlocal` keyword:
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337
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338 .. doctest::
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339 :options: +SKIP
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340
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341 >>> def foo():
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342 ... x = 10
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343 ... def bar():
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344 ... nonlocal x
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345 ... print x
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346 ... x += 1
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347 ... bar()
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348 ... print x
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349 >>> foo()
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350 10
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351 11
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352
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353
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354 What are the rules for local and global variables in Python?
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355 ------------------------------------------------------------
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356
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357 In Python, variables that are only referenced inside a function are implicitly
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358 global. If a variable is assigned a new value anywhere within the function's
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359 body, it's assumed to be a local. If a variable is ever assigned a new value
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360 inside the function, the variable is implicitly local, and you need to
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361 explicitly declare it as 'global'.
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362
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363 Though a bit surprising at first, a moment's consideration explains this. On
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364 one hand, requiring :keyword:`global` for assigned variables provides a bar
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365 against unintended side-effects. On the other hand, if ``global`` was required
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366 for all global references, you'd be using ``global`` all the time. You'd have
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367 to declare as global every reference to a built-in function or to a component of
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368 an imported module. This clutter would defeat the usefulness of the ``global``
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369 declaration for identifying side-effects.
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370
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371
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372 How do I share global variables across modules?
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373 ------------------------------------------------
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374
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375 The canonical way to share information across modules within a single program is
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376 to create a special module (often called config or cfg). Just import the config
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377 module in all modules of your application; the module then becomes available as
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378 a global name. Because there is only one instance of each module, any changes
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379 made to the module object get reflected everywhere. For example:
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380
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381 config.py::
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382
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383 x = 0 # Default value of the 'x' configuration setting
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384
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385 mod.py::
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386
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387 import config
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388 config.x = 1
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389
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390 main.py::
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391
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392 import config
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393 import mod
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394 print config.x
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395
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396 Note that using a module is also the basis for implementing the Singleton design
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397 pattern, for the same reason.
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398
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399
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400 What are the "best practices" for using import in a module?
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401 -----------------------------------------------------------
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402
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403 In general, don't use ``from modulename import *``. Doing so clutters the
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404 importer's namespace. Some people avoid this idiom even with the few modules
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405 that were designed to be imported in this manner. Modules designed in this
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406 manner include :mod:`Tkinter`, and :mod:`threading`.
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407
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408 Import modules at the top of a file. Doing so makes it clear what other modules
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409 your code requires and avoids questions of whether the module name is in scope.
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410 Using one import per line makes it easy to add and delete module imports, but
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411 using multiple imports per line uses less screen space.
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412
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413 It's good practice if you import modules in the following order:
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414
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415 1. standard library modules -- e.g. ``sys``, ``os``, ``getopt``, ``re``)
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416 2. third-party library modules (anything installed in Python's site-packages
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417 directory) -- e.g. mx.DateTime, ZODB, PIL.Image, etc.
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418 3. locally-developed modules
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419
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420 Never use relative package imports. If you're writing code that's in the
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421 ``package.sub.m1`` module and want to import ``package.sub.m2``, do not just
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422 write ``import m2``, even though it's legal. Write ``from package.sub import
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423 m2`` instead. Relative imports can lead to a module being initialized twice,
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424 leading to confusing bugs.
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425
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426 It is sometimes necessary to move imports to a function or class to avoid
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427 problems with circular imports. Gordon McMillan says:
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428
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429 Circular imports are fine where both modules use the "import <module>" form
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430 of import. They fail when the 2nd module wants to grab a name out of the
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431 first ("from module import name") and the import is at the top level. That's
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432 because names in the 1st are not yet available, because the first module is
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433 busy importing the 2nd.
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434
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435 In this case, if the second module is only used in one function, then the import
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436 can easily be moved into that function. By the time the import is called, the
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437 first module will have finished initializing, and the second module can do its
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438 import.
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439
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440 It may also be necessary to move imports out of the top level of code if some of
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441 the modules are platform-specific. In that case, it may not even be possible to
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442 import all of the modules at the top of the file. In this case, importing the
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443 correct modules in the corresponding platform-specific code is a good option.
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444
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445 Only move imports into a local scope, such as inside a function definition, if
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446 it's necessary to solve a problem such as avoiding a circular import or are
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447 trying to reduce the initialization time of a module. This technique is
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448 especially helpful if many of the imports are unnecessary depending on how the
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449 program executes. You may also want to move imports into a function if the
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450 modules are only ever used in that function. Note that loading a module the
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451 first time may be expensive because of the one time initialization of the
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452 module, but loading a module multiple times is virtually free, costing only a
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453 couple of dictionary lookups. Even if the module name has gone out of scope,
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454 the module is probably available in :data:`sys.modules`.
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455
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456 If only instances of a specific class use a module, then it is reasonable to
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457 import the module in the class's ``__init__`` method and then assign the module
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458 to an instance variable so that the module is always available (via that
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459 instance variable) during the life of the object. Note that to delay an import
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460 until the class is instantiated, the import must be inside a method. Putting
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461 the import inside the class but outside of any method still causes the import to
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462 occur when the module is initialized.
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463
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464
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465 How can I pass optional or keyword parameters from one function to another?
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466 ---------------------------------------------------------------------------
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467
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468 Collect the arguments using the ``*`` and ``**`` specifiers in the function's
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469 parameter list; this gives you the positional arguments as a tuple and the
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470 keyword arguments as a dictionary. You can then pass these arguments when
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471 calling another function by using ``*`` and ``**``::
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472
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473 def f(x, *args, **kwargs):
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474 ...
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475 kwargs['width'] = '14.3c'
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476 ...
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477 g(x, *args, **kwargs)
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478
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479 In the unlikely case that you care about Python versions older than 2.0, use
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480 :func:`apply`::
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481
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482 def f(x, *args, **kwargs):
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483 ...
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484 kwargs['width'] = '14.3c'
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485 ...
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486 apply(g, (x,)+args, kwargs)
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487
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488
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489 How do I write a function with output parameters (call by reference)?
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490 ---------------------------------------------------------------------
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491
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492 Remember that arguments are passed by assignment in Python. Since assignment
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493 just creates references to objects, there's no alias between an argument name in
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494 the caller and callee, and so no call-by-reference per se. You can achieve the
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495 desired effect in a number of ways.
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496
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497 1) By returning a tuple of the results::
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498
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499 def func2(a, b):
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500 a = 'new-value' # a and b are local names
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501 b = b + 1 # assigned to new objects
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502 return a, b # return new values
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503
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504 x, y = 'old-value', 99
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505 x, y = func2(x, y)
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506 print x, y # output: new-value 100
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507
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508 This is almost always the clearest solution.
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509
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510 2) By using global variables. This isn't thread-safe, and is not recommended.
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511
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512 3) By passing a mutable (changeable in-place) object::
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513
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514 def func1(a):
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515 a[0] = 'new-value' # 'a' references a mutable list
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516 a[1] = a[1] + 1 # changes a shared object
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517
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518 args = ['old-value', 99]
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519 func1(args)
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520 print args[0], args[1] # output: new-value 100
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521
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522 4) By passing in a dictionary that gets mutated::
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523
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524 def func3(args):
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525 args['a'] = 'new-value' # args is a mutable dictionary
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526 args['b'] = args['b'] + 1 # change it in-place
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527
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528 args = {'a':' old-value', 'b': 99}
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529 func3(args)
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530 print args['a'], args['b']
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531
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532 5) Or bundle up values in a class instance::
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533
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534 class callByRef:
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535 def __init__(self, **args):
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536 for (key, value) in args.items():
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537 setattr(self, key, value)
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538
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539 def func4(args):
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540 args.a = 'new-value' # args is a mutable callByRef
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541 args.b = args.b + 1 # change object in-place
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542
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543 args = callByRef(a='old-value', b=99)
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544 func4(args)
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545 print args.a, args.b
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546
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547
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548 There's almost never a good reason to get this complicated.
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549
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550 Your best choice is to return a tuple containing the multiple results.
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551
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552
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553 How do you make a higher order function in Python?
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554 --------------------------------------------------
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555
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556 You have two choices: you can use nested scopes or you can use callable objects.
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557 For example, suppose you wanted to define ``linear(a,b)`` which returns a
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558 function ``f(x)`` that computes the value ``a*x+b``. Using nested scopes::
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559
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560 def linear(a, b):
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561 def result(x):
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562 return a * x + b
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563 return result
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564
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565 Or using a callable object::
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566
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567 class linear:
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568
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569 def __init__(self, a, b):
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570 self.a, self.b = a, b
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571
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572 def __call__(self, x):
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573 return self.a * x + self.b
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574
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575 In both cases, ::
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576
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577 taxes = linear(0.3, 2)
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578
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579 gives a callable object where ``taxes(10e6) == 0.3 * 10e6 + 2``.
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580
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581 The callable object approach has the disadvantage that it is a bit slower and
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582 results in slightly longer code. However, note that a collection of callables
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583 can share their signature via inheritance::
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584
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585 class exponential(linear):
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586 # __init__ inherited
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587 def __call__(self, x):
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588 return self.a * (x ** self.b)
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589
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590 Object can encapsulate state for several methods::
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591
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592 class counter:
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593
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594 value = 0
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595
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596 def set(self, x):
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597 self.value = x
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598
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599 def up(self):
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600 self.value = self.value + 1
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601
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602 def down(self):
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603 self.value = self.value - 1
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604
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605 count = counter()
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606 inc, dec, reset = count.up, count.down, count.set
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607
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608 Here ``inc()``, ``dec()`` and ``reset()`` act like functions which share the
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609 same counting variable.
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610
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611
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612 How do I copy an object in Python?
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613 ----------------------------------
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614
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615 In general, try :func:`copy.copy` or :func:`copy.deepcopy` for the general case.
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616 Not all objects can be copied, but most can.
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617
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618 Some objects can be copied more easily. Dictionaries have a :meth:`~dict.copy`
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619 method::
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620
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621 newdict = olddict.copy()
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622
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623 Sequences can be copied by slicing::
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624
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625 new_l = l[:]
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626
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627
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628 How can I find the methods or attributes of an object?
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629 ------------------------------------------------------
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630
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631 For an instance x of a user-defined class, ``dir(x)`` returns an alphabetized
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632 list of the names containing the instance attributes and methods and attributes
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633 defined by its class.
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634
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635
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636 How can my code discover the name of an object?
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637 -----------------------------------------------
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638
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639 Generally speaking, it can't, because objects don't really have names.
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640 Essentially, assignment always binds a name to a value; The same is true of
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641 ``def`` and ``class`` statements, but in that case the value is a
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642 callable. Consider the following code::
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643
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644 class A:
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645 pass
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646
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647 B = A
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648
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649 a = B()
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650 b = a
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651 print b
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652 <__main__.A instance at 016D07CC>
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653 print a
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654 <__main__.A instance at 016D07CC>
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655
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656 Arguably the class has a name: even though it is bound to two names and invoked
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657 through the name B the created instance is still reported as an instance of
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658 class A. However, it is impossible to say whether the instance's name is a or
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659 b, since both names are bound to the same value.
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660
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661 Generally speaking it should not be necessary for your code to "know the names"
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662 of particular values. Unless you are deliberately writing introspective
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663 programs, this is usually an indication that a change of approach might be
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664 beneficial.
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665
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666 In comp.lang.python, Fredrik Lundh once gave an excellent analogy in answer to
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667 this question:
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668
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669 The same way as you get the name of that cat you found on your porch: the cat
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670 (object) itself cannot tell you its name, and it doesn't really care -- so
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671 the only way to find out what it's called is to ask all your neighbours
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672 (namespaces) if it's their cat (object)...
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673
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674 ....and don't be surprised if you'll find that it's known by many names, or
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675 no name at all!
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676
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677
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678 What's up with the comma operator's precedence?
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679 -----------------------------------------------
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680
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681 Comma is not an operator in Python. Consider this session::
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682
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683 >>> "a" in "b", "a"
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684 (False, '1')
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685
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686 Since the comma is not an operator, but a separator between expressions the
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687 above is evaluated as if you had entered::
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688
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689 >>> ("a" in "b"), "a"
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690
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691 not::
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692
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693 >>> "a" in ("5", "a")
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694
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695 The same is true of the various assignment operators (``=``, ``+=`` etc). They
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696 are not truly operators but syntactic delimiters in assignment statements.
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697
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698
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699 Is there an equivalent of C's "?:" ternary operator?
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700 ----------------------------------------------------
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701
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702 Yes, this feature was added in Python 2.5. The syntax would be as follows::
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703
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704 [on_true] if [expression] else [on_false]
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705
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706 x, y = 50, 25
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707
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708 small = x if x < y else y
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709
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710 For versions previous to 2.5 the answer would be 'No'.
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711
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712 .. XXX remove rest?
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713
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714 In many cases you can mimic ``a ? b : c`` with ``a and b or c``, but there's a
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715 flaw: if *b* is zero (or empty, or ``None`` -- anything that tests false) then
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716 *c* will be selected instead. In many cases you can prove by looking at the
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717 code that this can't happen (e.g. because *b* is a constant or has a type that
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718 can never be false), but in general this can be a problem.
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719
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720 Tim Peters (who wishes it was Steve Majewski) suggested the following solution:
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721 ``(a and [b] or [c])[0]``. Because ``[b]`` is a singleton list it is never
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722 false, so the wrong path is never taken; then applying ``[0]`` to the whole
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723 thing gets the *b* or *c* that you really wanted. Ugly, but it gets you there
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724 in the rare cases where it is really inconvenient to rewrite your code using
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725 'if'.
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726
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727 The best course is usually to write a simple ``if...else`` statement. Another
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728 solution is to implement the ``?:`` operator as a function::
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729
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730 def q(cond, on_true, on_false):
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731 if cond:
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732 if not isfunction(on_true):
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733 return on_true
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734 else:
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735 return apply(on_true)
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736 else:
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737 if not isfunction(on_false):
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738 return on_false
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739 else:
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740 return apply(on_false)
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741
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742 In most cases you'll pass b and c directly: ``q(a, b, c)``. To avoid evaluating
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743 b or c when they shouldn't be, encapsulate them within a lambda function, e.g.:
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744 ``q(a, lambda: b, lambda: c)``.
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745
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746 It has been asked *why* Python has no if-then-else expression. There are
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747 several answers: many languages do just fine without one; it can easily lead to
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748 less readable code; no sufficiently "Pythonic" syntax has been discovered; a
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749 search of the standard library found remarkably few places where using an
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750 if-then-else expression would make the code more understandable.
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751
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752 In 2002, :pep:`308` was written proposing several possible syntaxes and the
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753 community was asked to vote on the issue. The vote was inconclusive. Most
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754 people liked one of the syntaxes, but also hated other syntaxes; many votes
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755 implied that people preferred no ternary operator rather than having a syntax
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756 they hated.
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757
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758
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759 Is it possible to write obfuscated one-liners in Python?
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760 --------------------------------------------------------
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761
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762 Yes. Usually this is done by nesting :keyword:`lambda` within
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763 :keyword:`lambda`. See the following three examples, due to Ulf Bartelt::
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764
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765 # Primes < 1000
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766 print filter(None,map(lambda y:y*reduce(lambda x,y:x*y!=0,
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767 map(lambda x,y=y:y%x,range(2,int(pow(y,0.5)+1))),1),range(2,1000)))
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768
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769 # First 10 Fibonacci numbers
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770 print map(lambda x,f=lambda x,f:(x<=1) or (f(x-1,f)+f(x-2,f)): f(x,f),
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771 range(10))
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772
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773 # Mandelbrot set
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774 print (lambda Ru,Ro,Iu,Io,IM,Sx,Sy:reduce(lambda x,y:x+y,map(lambda y,
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775 Iu=Iu,Io=Io,Ru=Ru,Ro=Ro,Sy=Sy,L=lambda yc,Iu=Iu,Io=Io,Ru=Ru,Ro=Ro,i=IM,
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776 Sx=Sx,Sy=Sy:reduce(lambda x,y:x+y,map(lambda x,xc=Ru,yc=yc,Ru=Ru,Ro=Ro,
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777 i=i,Sx=Sx,F=lambda xc,yc,x,y,k,f=lambda xc,yc,x,y,k,f:(k<=0)or (x*x+y*y
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778 >=4.0) or 1+f(xc,yc,x*x-y*y+xc,2.0*x*y+yc,k-1,f):f(xc,yc,x,y,k,f):chr(
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779 64+F(Ru+x*(Ro-Ru)/Sx,yc,0,0,i)),range(Sx))):L(Iu+y*(Io-Iu)/Sy),range(Sy
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780 ))))(-2.1, 0.7, -1.2, 1.2, 30, 80, 24)
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781 # \___ ___/ \___ ___/ | | |__ lines on screen
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782 # V V | |______ columns on screen
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783 # | | |__________ maximum of "iterations"
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784 # | |_________________ range on y axis
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785 # |____________________________ range on x axis
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786
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787 Don't try this at home, kids!
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788
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789
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790 Numbers and strings
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791 ===================
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792
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793 How do I specify hexadecimal and octal integers?
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794 ------------------------------------------------
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795
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796 To specify an octal digit, precede the octal value with a zero. For example, to
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797 set the variable "a" to the octal value "10" (8 in decimal), type::
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798
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799 >>> a = 010
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800 >>> a
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801 8
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802
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803 Hexadecimal is just as easy. Simply precede the hexadecimal number with a zero,
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804 and then a lower or uppercase "x". Hexadecimal digits can be specified in lower
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805 or uppercase. For example, in the Python interpreter::
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806
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807 >>> a = 0xa5
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808 >>> a
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809 165
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810 >>> b = 0XB2
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811 >>> b
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812 178
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813
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814
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815 Why does -22 / 10 return -3?
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816 ----------------------------
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817
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818 It's primarily driven by the desire that ``i % j`` have the same sign as ``j``.
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819 If you want that, and also want::
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820
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821 i == (i / j) * j + (i % j)
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822
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823 then integer division has to return the floor. C also requires that identity to
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824 hold, and then compilers that truncate ``i / j`` need to make ``i % j`` have the
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825 same sign as ``i``.
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826
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827 There are few real use cases for ``i % j`` when ``j`` is negative. When ``j``
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828 is positive, there are many, and in virtually all of them it's more useful for
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829 ``i % j`` to be ``>= 0``. If the clock says 10 now, what did it say 200 hours
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830 ago? ``-190 % 12 == 2`` is useful; ``-190 % 12 == -10`` is a bug waiting to
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831 bite.
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832
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833
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834 How do I convert a string to a number?
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835 --------------------------------------
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836
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837 For integers, use the built-in :func:`int` type constructor, e.g. ``int('144')
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838 == 144``. Similarly, :func:`float` converts to floating-point,
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839 e.g. ``float('144') == 144.0``.
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840
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841 By default, these interpret the number as decimal, so that ``int('0144') ==
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842 144`` and ``int('0x144')`` raises :exc:`ValueError`. ``int(string, base)`` takes
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843 the base to convert from as a second optional argument, so ``int('0x144', 16) ==
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844 324``. If the base is specified as 0, the number is interpreted using Python's
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845 rules: a leading '0' indicates octal, and '0x' indicates a hex number.
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846
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847 Do not use the built-in function :func:`eval` if all you need is to convert
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848 strings to numbers. :func:`eval` will be significantly slower and it presents a
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849 security risk: someone could pass you a Python expression that might have
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850 unwanted side effects. For example, someone could pass
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851 ``__import__('os').system("rm -rf $HOME")`` which would erase your home
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852 directory.
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853
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854 :func:`eval` also has the effect of interpreting numbers as Python expressions,
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855 so that e.g. ``eval('09')`` gives a syntax error because Python regards numbers
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856 starting with '0' as octal (base 8).
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857
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858
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859 How do I convert a number to a string?
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860 --------------------------------------
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861
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862 To convert, e.g., the number 144 to the string '144', use the built-in type
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863 constructor :func:`str`. If you want a hexadecimal or octal representation, use
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864 the built-in functions ``hex()`` or ``oct()``. For fancy formatting, use
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865 :ref:`the % operator <string-formatting>` on strings, e.g. ``"%04d" % 144``
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866 yields ``'0144'`` and ``"%.3f" % (1/3.0)`` yields ``'0.333'``. See the library
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867 reference manual for details.
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868
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869
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870 How do I modify a string in place?
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871 ----------------------------------
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872
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873 You can't, because strings are immutable. If you need an object with this
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874 ability, try converting the string to a list or use the array module::
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875
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876 >>> s = "Hello, world"
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877 >>> a = list(s)
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878 >>> print a
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879 ['H', 'e', 'l', 'l', 'o', ',', ' ', 'w', 'o', 'r', 'l', 'd']
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880 >>> a[7:] = list("there!")
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881 >>> ''.join(a)
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882 'Hello, there!'
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883
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884 >>> import array
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885 >>> a = array.array('c', s)
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886 >>> print a
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887 array('c', 'Hello, world')
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888 >>> a[0] = 'y' ; print a
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889 array('c', 'yello world')
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890 >>> a.tostring()
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891 'yello, world'
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892
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893
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894 How do I use strings to call functions/methods?
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895 -----------------------------------------------
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896
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897 There are various techniques.
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898
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899 * The best is to use a dictionary that maps strings to functions. The primary
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900 advantage of this technique is that the strings do not need to match the names
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901 of the functions. This is also the primary technique used to emulate a case
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902 construct::
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903
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904 def a():
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905 pass
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906
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907 def b():
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908 pass
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909
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910 dispatch = {'go': a, 'stop': b} # Note lack of parens for funcs
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911
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912 dispatch[get_input()]() # Note trailing parens to call function
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913
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914 * Use the built-in function :func:`getattr`::
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915
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916 import foo
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917 getattr(foo, 'bar')()
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918
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919 Note that :func:`getattr` works on any object, including classes, class
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920 instances, modules, and so on.
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921
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922 This is used in several places in the standard library, like this::
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923
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924 class Foo:
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925 def do_foo(self):
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926 ...
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927
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928 def do_bar(self):
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929 ...
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930
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931 f = getattr(foo_instance, 'do_' + opname)
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932 f()
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933
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934
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935 * Use :func:`locals` or :func:`eval` to resolve the function name::
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936
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937 def myFunc():
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938 print "hello"
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939
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940 fname = "myFunc"
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941
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942 f = locals()[fname]
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943 f()
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944
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945 f = eval(fname)
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946 f()
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947
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948 Note: Using :func:`eval` is slow and dangerous. If you don't have absolute
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949 control over the contents of the string, someone could pass a string that
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950 resulted in an arbitrary function being executed.
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951
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952 Is there an equivalent to Perl's chomp() for removing trailing newlines from strings?
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953 -------------------------------------------------------------------------------------
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954
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955 Starting with Python 2.2, you can use ``S.rstrip("\r\n")`` to remove all
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956 occurences of any line terminator from the end of the string ``S`` without
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957 removing other trailing whitespace. If the string ``S`` represents more than
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958 one line, with several empty lines at the end, the line terminators for all the
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959 blank lines will be removed::
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960
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961 >>> lines = ("line 1 \r\n"
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962 ... "\r\n"
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963 ... "\r\n")
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964 >>> lines.rstrip("\n\r")
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965 "line 1 "
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966
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967 Since this is typically only desired when reading text one line at a time, using
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968 ``S.rstrip()`` this way works well.
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969
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970 For older versions of Python, There are two partial substitutes:
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971
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972 - If you want to remove all trailing whitespace, use the ``rstrip()`` method of
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973 string objects. This removes all trailing whitespace, not just a single
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974 newline.
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975
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976 - Otherwise, if there is only one line in the string ``S``, use
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977 ``S.splitlines()[0]``.
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978
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979
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980 Is there a scanf() or sscanf() equivalent?
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981 ------------------------------------------
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982
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983 Not as such.
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984
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985 For simple input parsing, the easiest approach is usually to split the line into
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986 whitespace-delimited words using the :meth:`~str.split` method of string objects
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987 and then convert decimal strings to numeric values using :func:`int` or
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988 :func:`float`. ``split()`` supports an optional "sep" parameter which is useful
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989 if the line uses something other than whitespace as a separator.
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990
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991 For more complicated input parsing, regular expressions more powerful than C's
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992 :cfunc:`sscanf` and better suited for the task.
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993
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994
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995 What does 'UnicodeError: ASCII [decoding,encoding] error: ordinal not in range(128)' mean?
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996 ------------------------------------------------------------------------------------------
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997
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998 This error indicates that your Python installation can handle only 7-bit ASCII
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999 strings. There are a couple ways to fix or work around the problem.
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1000
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1001 If your programs must handle data in arbitrary character set encodings, the
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1002 environment the application runs in will generally identify the encoding of the
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1003 data it is handing you. You need to convert the input to Unicode data using
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1004 that encoding. For example, a program that handles email or web input will
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1005 typically find character set encoding information in Content-Type headers. This
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1006 can then be used to properly convert input data to Unicode. Assuming the string
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1007 referred to by ``value`` is encoded as UTF-8::
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1008
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1009 value = unicode(value, "utf-8")
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1010
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1011 will return a Unicode object. If the data is not correctly encoded as UTF-8,
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1012 the above call will raise a :exc:`UnicodeError` exception.
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1013
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1014 If you only want strings converted to Unicode which have non-ASCII data, you can
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1015 try converting them first assuming an ASCII encoding, and then generate Unicode
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1016 objects if that fails::
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1017
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1018 try:
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1019 x = unicode(value, "ascii")
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1020 except UnicodeError:
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1021 value = unicode(value, "utf-8")
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1022 else:
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1023 # value was valid ASCII data
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1024 pass
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1025
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1026 It's possible to set a default encoding in a file called ``sitecustomize.py``
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1027 that's part of the Python library. However, this isn't recommended because
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1028 changing the Python-wide default encoding may cause third-party extension
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1029 modules to fail.
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1030
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1031 Note that on Windows, there is an encoding known as "mbcs", which uses an
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1032 encoding specific to your current locale. In many cases, and particularly when
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1033 working with COM, this may be an appropriate default encoding to use.
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1034
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1035
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1036 Sequences (Tuples/Lists)
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1037 ========================
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1038
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1039 How do I convert between tuples and lists?
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1040 ------------------------------------------
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1041
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1042 The type constructor ``tuple(seq)`` converts any sequence (actually, any
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1043 iterable) into a tuple with the same items in the same order.
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1044
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1045 For example, ``tuple([1, 2, 3])`` yields ``(1, 2, 3)`` and ``tuple('abc')``
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1046 yields ``('a', 'b', 'c')``. If the argument is a tuple, it does not make a copy
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1047 but returns the same object, so it is cheap to call :func:`tuple` when you
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1048 aren't sure that an object is already a tuple.
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1049
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1050 The type constructor ``list(seq)`` converts any sequence or iterable into a list
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1051 with the same items in the same order. For example, ``list((1, 2, 3))`` yields
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1052 ``[1, 2, 3]`` and ``list('abc')`` yields ``['a', 'b', 'c']``. If the argument
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1053 is a list, it makes a copy just like ``seq[:]`` would.
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1054
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1055
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1056 What's a negative index?
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1057 ------------------------
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1058
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1059 Python sequences are indexed with positive numbers and negative numbers. For
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1060 positive numbers 0 is the first index 1 is the second index and so forth. For
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1061 negative indices -1 is the last index and -2 is the penultimate (next to last)
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1062 index and so forth. Think of ``seq[-n]`` as the same as ``seq[len(seq)-n]``.
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1063
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1064 Using negative indices can be very convenient. For example ``S[:-1]`` is all of
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1065 the string except for its last character, which is useful for removing the
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1066 trailing newline from a string.
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1067
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1068
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1069 How do I iterate over a sequence in reverse order?
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1070 --------------------------------------------------
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1071
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1072 Use the :func:`reversed` built-in function, which is new in Python 2.4::
56824
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1073
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1074 for x in reversed(sequence):
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1075 ... # do something with x...
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1076
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1077 This won't touch your original sequence, but build a new copy with reversed
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1078 order to iterate over.
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1079
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1080 With Python 2.3, you can use an extended slice syntax::
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1081
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1082 for x in sequence[::-1]:
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1083 ... # do something with x...
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1084
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1085
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1086 How do you remove duplicates from a list?
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1087 -----------------------------------------
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1088
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1089 See the Python Cookbook for a long discussion of many ways to do this:
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1090
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1091 http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/52560
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1092
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1093 If you don't mind reordering the list, sort it and then scan from the end of the
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1094 list, deleting duplicates as you go::
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1095
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1096 if List:
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1097 List.sort()
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1098 last = List[-1]
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1099 for i in range(len(List)-2, -1, -1):
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1100 if last == List[i]:
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1101 del List[i]
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1102 else:
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1103 last = List[i]
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1104
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1105 If all elements of the list may be used as dictionary keys (i.e. they are all
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1106 hashable) this is often faster ::
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1107
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1108 d = {}
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1109 for x in List:
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1110 d[x] = x
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1111 List = d.values()
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1112
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1113 In Python 2.5 and later, the following is possible instead::
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1114
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1115 List = list(set(List))
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1116
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1117 This converts the list into a set, thereby removing duplicates, and then back
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1118 into a list.
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1119
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1120
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1121 How do you make an array in Python?
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1122 -----------------------------------
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1123
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1124 Use a list::
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1125
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1126 ["this", 1, "is", "an", "array"]
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1127
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1128 Lists are equivalent to C or Pascal arrays in their time complexity; the primary
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1129 difference is that a Python list can contain objects of many different types.
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1130
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1131 The ``array`` module also provides methods for creating arrays of fixed types
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1132 with compact representations, but they are slower to index than lists. Also
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1133 note that the Numeric extensions and others define array-like structures with
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1134 various characteristics as well.
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1135
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1136 To get Lisp-style linked lists, you can emulate cons cells using tuples::
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1137
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1138 lisp_list = ("like", ("this", ("example", None) ) )
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1139
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1140 If mutability is desired, you could use lists instead of tuples. Here the
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1141 analogue of lisp car is ``lisp_list[0]`` and the analogue of cdr is
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1142 ``lisp_list[1]``. Only do this if you're sure you really need to, because it's
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1143 usually a lot slower than using Python lists.
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1144
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1145
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1146 How do I create a multidimensional list?
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1147 ----------------------------------------
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1148
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1149 You probably tried to make a multidimensional array like this::
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1150
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1151 A = [[None] * 2] * 3
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1152
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1153 This looks correct if you print it::
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1154
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1155 >>> A
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1156 [[None, None], [None, None], [None, None]]
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1157
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1158 But when you assign a value, it shows up in multiple places:
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1159
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1160 >>> A[0][0] = 5
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1161 >>> A
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1162 [[5, None], [5, None], [5, None]]
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1163
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1164 The reason is that replicating a list with ``*`` doesn't create copies, it only
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1165 creates references to the existing objects. The ``*3`` creates a list
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1166 containing 3 references to the same list of length two. Changes to one row will
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1167 show in all rows, which is almost certainly not what you want.
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1168
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1169 The suggested approach is to create a list of the desired length first and then
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1170 fill in each element with a newly created list::
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1171
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1172 A = [None] * 3
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1173 for i in range(3):
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1174 A[i] = [None] * 2
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1175
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1176 This generates a list containing 3 different lists of length two. You can also
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1177 use a list comprehension::
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1178
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1179 w, h = 2, 3
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1180 A = [[None] * w for i in range(h)]
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1181
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1182 Or, you can use an extension that provides a matrix datatype; `Numeric Python
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1183 <http://numpy.scipy.org/>`_ is the best known.
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1184
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1185
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1186 How do I apply a method to a sequence of objects?
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1187 -------------------------------------------------
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1188
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1189 Use a list comprehension::
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1190
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1191 result = [obj.method() for obj in List]
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1192
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1193 More generically, you can try the following function::
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1194
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1195 def method_map(objects, method, arguments):
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1196 """method_map([a,b], "meth", (1,2)) gives [a.meth(1,2), b.meth(1,2)]"""
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1197 nobjects = len(objects)
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1198 methods = map(getattr, objects, [method]*nobjects)
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1199 return map(apply, methods, [arguments]*nobjects)
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1200
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1201
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1202 Dictionaries
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1203 ============
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1204
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1205 How can I get a dictionary to display its keys in a consistent order?
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1206 ---------------------------------------------------------------------
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1207
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1208 You can't. Dictionaries store their keys in an unpredictable order, so the
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1209 display order of a dictionary's elements will be similarly unpredictable.
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1210
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1211 This can be frustrating if you want to save a printable version to a file, make
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1212 some changes and then compare it with some other printed dictionary. In this
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1213 case, use the ``pprint`` module to pretty-print the dictionary; the items will
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1214 be presented in order sorted by the key.
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1215
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1216 A more complicated solution is to subclass ``UserDict.UserDict`` to create a
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1217 ``SortedDict`` class that prints itself in a predictable order. Here's one
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1218 simpleminded implementation of such a class::
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1219
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1220 import UserDict, string
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1221
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1222 class SortedDict(UserDict.UserDict):
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1223 def __repr__(self):
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1224 result = []
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1225 append = result.append
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1226 keys = self.data.keys()
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1227 keys.sort()
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1228 for k in keys:
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1229 append("%s: %s" % (`k`, `self.data[k]`))
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1230 return "{%s}" % string.join(result, ", ")
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1231
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1232 __str__ = __repr__
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1233
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1234 This will work for many common situations you might encounter, though it's far
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1235 from a perfect solution. The largest flaw is that if some values in the
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1236 dictionary are also dictionaries, their values won't be presented in any
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1237 particular order.
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1238
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1239
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1240 I want to do a complicated sort: can you do a Schwartzian Transform in Python?
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1241 ------------------------------------------------------------------------------
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1242
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1243 The technique, attributed to Randal Schwartz of the Perl community, sorts the
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1244 elements of a list by a metric which maps each element to its "sort value". In
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1245 Python, just use the ``key`` argument for the ``sort()`` method::
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1246
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1247 Isorted = L[:]
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1248 Isorted.sort(key=lambda s: int(s[10:15]))
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1249
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1250 The ``key`` argument is new in Python 2.4, for older versions this kind of
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1251 sorting is quite simple to do with list comprehensions. To sort a list of
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1252 strings by their uppercase values::
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1253
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1254 tmp1 = [(x.upper(), x) for x in L] # Schwartzian transform
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1255 tmp1.sort()
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1256 Usorted = [x[1] for x in tmp1]
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1257
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1258 To sort by the integer value of a subfield extending from positions 10-15 in
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1259 each string::
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1260
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1261 tmp2 = [(int(s[10:15]), s) for s in L] # Schwartzian transform
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1262 tmp2.sort()
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1263 Isorted = [x[1] for x in tmp2]
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1264
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1265 Note that Isorted may also be computed by ::
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1266
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1267 def intfield(s):
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1268 return int(s[10:15])
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1269
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1270 def Icmp(s1, s2):
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1271 return cmp(intfield(s1), intfield(s2))
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1272
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1273 Isorted = L[:]
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1274 Isorted.sort(Icmp)
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1275
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1276 but since this method calls ``intfield()`` many times for each element of L, it
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1277 is slower than the Schwartzian Transform.
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1278
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1279
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1280 How can I sort one list by values from another list?
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1281 ----------------------------------------------------
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1282
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1283 Merge them into a single list of tuples, sort the resulting list, and then pick
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1284 out the element you want. ::
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1285
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1286 >>> list1 = ["what", "I'm", "sorting", "by"]
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1287 >>> list2 = ["something", "else", "to", "sort"]
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1288 >>> pairs = zip(list1, list2)
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1289 >>> pairs
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1290 [('what', 'something'), ("I'm", 'else'), ('sorting', 'to'), ('by', 'sort')]
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1291 >>> pairs.sort()
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1292 >>> result = [ x[1] for x in pairs ]
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1293 >>> result
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1294 ['else', 'sort', 'to', 'something']
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1295
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1296 An alternative for the last step is::
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1297
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1298 result = []
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1299 for p in pairs: result.append(p[1])
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1300
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1301 If you find this more legible, you might prefer to use this instead of the final
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1302 list comprehension. However, it is almost twice as slow for long lists. Why?
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1303 First, the ``append()`` operation has to reallocate memory, and while it uses
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1304 some tricks to avoid doing that each time, it still has to do it occasionally,
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1305 and that costs quite a bit. Second, the expression "result.append" requires an
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1306 extra attribute lookup, and third, there's a speed reduction from having to make
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1307 all those function calls.
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1308
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1309
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1310 Objects
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1311 =======
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1312
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1313 What is a class?
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1314 ----------------
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1315
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1316 A class is the particular object type created by executing a class statement.
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1317 Class objects are used as templates to create instance objects, which embody
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1318 both the data (attributes) and code (methods) specific to a datatype.
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1319
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1320 A class can be based on one or more other classes, called its base class(es). It
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1321 then inherits the attributes and methods of its base classes. This allows an
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1322 object model to be successively refined by inheritance. You might have a
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1323 generic ``Mailbox`` class that provides basic accessor methods for a mailbox,
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1324 and subclasses such as ``MboxMailbox``, ``MaildirMailbox``, ``OutlookMailbox``
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1325 that handle various specific mailbox formats.
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1326
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1327
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1328 What is a method?
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1329 -----------------
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1330
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1331 A method is a function on some object ``x`` that you normally call as
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1332 ``x.name(arguments...)``. Methods are defined as functions inside the class
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1333 definition::
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1334
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1335 class C:
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1336 def meth (self, arg):
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1337 return arg * 2 + self.attribute
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1338
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1339
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1340 What is self?
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1341 -------------
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1342
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1343 Self is merely a conventional name for the first argument of a method. A method
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1344 defined as ``meth(self, a, b, c)`` should be called as ``x.meth(a, b, c)`` for
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1345 some instance ``x`` of the class in which the definition occurs; the called
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1346 method will think it is called as ``meth(x, a, b, c)``.
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1347
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1348 See also :ref:`why-self`.
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1349
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1350
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1351 How do I check if an object is an instance of a given class or of a subclass of it?
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1352 -----------------------------------------------------------------------------------
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1353
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1354 Use the built-in function ``isinstance(obj, cls)``. You can check if an object
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1355 is an instance of any of a number of classes by providing a tuple instead of a
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1356 single class, e.g. ``isinstance(obj, (class1, class2, ...))``, and can also
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1357 check whether an object is one of Python's built-in types, e.g.
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1358 ``isinstance(obj, str)`` or ``isinstance(obj, (int, long, float, complex))``.
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1359
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1360 Note that most programs do not use :func:`isinstance` on user-defined classes
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1361 very often. If you are developing the classes yourself, a more proper
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1362 object-oriented style is to define methods on the classes that encapsulate a
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1363 particular behaviour, instead of checking the object's class and doing a
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1364 different thing based on what class it is. For example, if you have a function
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1365 that does something::
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1366
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1367 def search (obj):
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1368 if isinstance(obj, Mailbox):
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1369 # ... code to search a mailbox
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1370 elif isinstance(obj, Document):
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1371 # ... code to search a document
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1372 elif ...
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1373
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1374 A better approach is to define a ``search()`` method on all the classes and just
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1375 call it::
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1376
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1377 class Mailbox:
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1378 def search(self):
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1379 # ... code to search a mailbox
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1380
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1381 class Document:
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1382 def search(self):
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1383 # ... code to search a document
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1384
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1385 obj.search()
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1386
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1387
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1388 What is delegation?
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1389 -------------------
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1390
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1391 Delegation is an object oriented technique (also called a design pattern).
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1392 Let's say you have an object ``x`` and want to change the behaviour of just one
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1393 of its methods. You can create a new class that provides a new implementation
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1394 of the method you're interested in changing and delegates all other methods to
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1395 the corresponding method of ``x``.
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1396
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1397 Python programmers can easily implement delegation. For example, the following
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1398 class implements a class that behaves like a file but converts all written data
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1399 to uppercase::
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1400
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1401 class UpperOut:
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1402
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1403 def __init__(self, outfile):
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1404 self._outfile = outfile
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1405
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1406 def write(self, s):
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1407 self._outfile.write(s.upper())
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1408
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1409 def __getattr__(self, name):
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1410 return getattr(self._outfile, name)
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1411
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1412 Here the ``UpperOut`` class redefines the ``write()`` method to convert the
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1413 argument string to uppercase before calling the underlying
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1414 ``self.__outfile.write()`` method. All other methods are delegated to the
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1415 underlying ``self.__outfile`` object. The delegation is accomplished via the
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1416 ``__getattr__`` method; consult :ref:`the language reference <attribute-access>`
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1417 for more information about controlling attribute access.
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1418
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1419 Note that for more general cases delegation can get trickier. When attributes
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1420 must be set as well as retrieved, the class must define a :meth:`__setattr__`
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1421 method too, and it must do so carefully. The basic implementation of
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1422 :meth:`__setattr__` is roughly equivalent to the following::
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1423
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1424 class X:
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1425 ...
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1426 def __setattr__(self, name, value):
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1427 self.__dict__[name] = value
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1428 ...
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1429
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1430 Most :meth:`__setattr__` implementations must modify ``self.__dict__`` to store
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1431 local state for self without causing an infinite recursion.
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1432
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1433
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1434 How do I call a method defined in a base class from a derived class that overrides it?
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1435 --------------------------------------------------------------------------------------
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1436
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1437 If you're using new-style classes, use the built-in :func:`super` function::
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1438
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1439 class Derived(Base):
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1440 def meth (self):
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1441 super(Derived, self).meth()
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1442
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1443 If you're using classic classes: For a class definition such as ``class
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1444 Derived(Base): ...`` you can call method ``meth()`` defined in ``Base`` (or one
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1445 of ``Base``'s base classes) as ``Base.meth(self, arguments...)``. Here,
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1446 ``Base.meth`` is an unbound method, so you need to provide the ``self``
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1447 argument.
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1448
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1449
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1450 How can I organize my code to make it easier to change the base class?
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1451 ----------------------------------------------------------------------
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1452
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1453 You could define an alias for the base class, assign the real base class to it
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1454 before your class definition, and use the alias throughout your class. Then all
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1455 you have to change is the value assigned to the alias. Incidentally, this trick
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1456 is also handy if you want to decide dynamically (e.g. depending on availability
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1457 of resources) which base class to use. Example::
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1458
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1459 BaseAlias = <real base class>
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1460
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1461 class Derived(BaseAlias):
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1462 def meth(self):
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1463 BaseAlias.meth(self)
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1464 ...
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1465
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1466
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1467 How do I create static class data and static class methods?
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1468 -----------------------------------------------------------
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1469
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1470 Static data (in the sense of C++ or Java) is easy; static methods (again in the
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1471 sense of C++ or Java) are not supported directly.
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1472
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1473 For static data, simply define a class attribute. To assign a new value to the
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1474 attribute, you have to explicitly use the class name in the assignment::
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1475
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1476 class C:
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1477 count = 0 # number of times C.__init__ called
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1478
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1479 def __init__(self):
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1480 C.count = C.count + 1
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1481
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1482 def getcount(self):
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1483 return C.count # or return self.count
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1484
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1485 ``c.count`` also refers to ``C.count`` for any ``c`` such that ``isinstance(c,
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1486 C)`` holds, unless overridden by ``c`` itself or by some class on the base-class
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1487 search path from ``c.__class__`` back to ``C``.
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1488
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1489 Caution: within a method of C, an assignment like ``self.count = 42`` creates a
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1490 new and unrelated instance vrbl named "count" in ``self``'s own dict. Rebinding
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1491 of a class-static data name must always specify the class whether inside a
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1492 method or not::
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1493
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1494 C.count = 314
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1495
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1496 Static methods are possible since Python 2.2::
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1497
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1498 class C:
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1499 def static(arg1, arg2, arg3):
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1500 # No 'self' parameter!
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1501 ...
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1502 static = staticmethod(static)
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1503
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1504 With Python 2.4's decorators, this can also be written as ::
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1505
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1506 class C:
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1507 @staticmethod
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1508 def static(arg1, arg2, arg3):
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1509 # No 'self' parameter!
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1510 ...
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1511
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1512 However, a far more straightforward way to get the effect of a static method is
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1513 via a simple module-level function::
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1514
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1515 def getcount():
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1516 return C.count
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1517
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1518 If your code is structured so as to define one class (or tightly related class
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1519 hierarchy) per module, this supplies the desired encapsulation.
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1520
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1521
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1522 How can I overload constructors (or methods) in Python?
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1523 -------------------------------------------------------
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1524
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1525 This answer actually applies to all methods, but the question usually comes up
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1526 first in the context of constructors.
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1527
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1528 In C++ you'd write
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1529
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1530 .. code-block:: c
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1531
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1532 class C {
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1533 C() { cout << "No arguments\n"; }
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1534 C(int i) { cout << "Argument is " << i << "\n"; }
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1535 }
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1536
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1537 In Python you have to write a single constructor that catches all cases using
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1538 default arguments. For example::
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1539
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1540 class C:
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1541 def __init__(self, i=None):
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1542 if i is None:
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1543 print "No arguments"
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1544 else:
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1545 print "Argument is", i
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1546
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1547 This is not entirely equivalent, but close enough in practice.
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1548
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1549 You could also try a variable-length argument list, e.g. ::
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1550
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1551 def __init__(self, *args):
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1552 ...
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1553
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1554 The same approach works for all method definitions.
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1555
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1556
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1557 I try to use __spam and I get an error about _SomeClassName__spam.
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1558 ------------------------------------------------------------------
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1559
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1560 Variable names with double leading underscores are "mangled" to provide a simple
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1561 but effective way to define class private variables. Any identifier of the form
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1562 ``__spam`` (at least two leading underscores, at most one trailing underscore)
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1563 is textually replaced with ``_classname__spam``, where ``classname`` is the
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1564 current class name with any leading underscores stripped.
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1565
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1566 This doesn't guarantee privacy: an outside user can still deliberately access
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1567 the "_classname__spam" attribute, and private values are visible in the object's
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1568 ``__dict__``. Many Python programmers never bother to use private variable
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1569 names at all.
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1570
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1571
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1572 My class defines __del__ but it is not called when I delete the object.
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1573 -----------------------------------------------------------------------
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1574
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1575 There are several possible reasons for this.
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1576
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1577 The del statement does not necessarily call :meth:`__del__` -- it simply
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1578 decrements the object's reference count, and if this reaches zero
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1579 :meth:`__del__` is called.
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1580
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1581 If your data structures contain circular links (e.g. a tree where each child has
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1582 a parent reference and each parent has a list of children) the reference counts
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1583 will never go back to zero. Once in a while Python runs an algorithm to detect
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1584 such cycles, but the garbage collector might run some time after the last
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1585 reference to your data structure vanishes, so your :meth:`__del__` method may be
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1586 called at an inconvenient and random time. This is inconvenient if you're trying
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1587 to reproduce a problem. Worse, the order in which object's :meth:`__del__`
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1588 methods are executed is arbitrary. You can run :func:`gc.collect` to force a
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1589 collection, but there *are* pathological cases where objects will never be
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1590 collected.
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1591
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1592 Despite the cycle collector, it's still a good idea to define an explicit
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1593 ``close()`` method on objects to be called whenever you're done with them. The
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1594 ``close()`` method can then remove attributes that refer to subobjecs. Don't
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1595 call :meth:`__del__` directly -- :meth:`__del__` should call ``close()`` and
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1596 ``close()`` should make sure that it can be called more than once for the same
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1597 object.
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1598
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1599 Another way to avoid cyclical references is to use the :mod:`weakref` module,
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1600 which allows you to point to objects without incrementing their reference count.
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1601 Tree data structures, for instance, should use weak references for their parent
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1602 and sibling references (if they need them!).
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1603
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1604 If the object has ever been a local variable in a function that caught an
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1605 expression in an except clause, chances are that a reference to the object still
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1606 exists in that function's stack frame as contained in the stack trace.
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1607 Normally, calling :func:`sys.exc_clear` will take care of this by clearing the
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1608 last recorded exception.
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1609
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1610 Finally, if your :meth:`__del__` method raises an exception, a warning message
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1611 is printed to :data:`sys.stderr`.
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1612
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1613
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1614 How do I get a list of all instances of a given class?
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1615 ------------------------------------------------------
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1616
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1617 Python does not keep track of all instances of a class (or of a built-in type).
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1618 You can program the class's constructor to keep track of all instances by
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1619 keeping a list of weak references to each instance.
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1620
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1621
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1622 Modules
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1623 =======
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1624
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1625 How do I create a .pyc file?
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1626 ----------------------------
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1627
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1628 When a module is imported for the first time (or when the source is more recent
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1629 than the current compiled file) a ``.pyc`` file containing the compiled code
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1630 should be created in the same directory as the ``.py`` file.
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1631
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1632 One reason that a ``.pyc`` file may not be created is permissions problems with
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1633 the directory. This can happen, for example, if you develop as one user but run
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1634 as another, such as if you are testing with a web server. Creation of a .pyc
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1635 file is automatic if you're importing a module and Python has the ability
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1636 (permissions, free space, etc...) to write the compiled module back to the
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1637 directory.
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1638
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1639 Running Python on a top level script is not considered an import and no ``.pyc``
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1640 will be created. For example, if you have a top-level module ``abc.py`` that
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1641 imports another module ``xyz.py``, when you run abc, ``xyz.pyc`` will be created
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1642 since xyz is imported, but no ``abc.pyc`` file will be created since ``abc.py``
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1643 isn't being imported.
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1644
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1645 If you need to create abc.pyc -- that is, to create a .pyc file for a module
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1646 that is not imported -- you can, using the :mod:`py_compile` and
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1647 :mod:`compileall` modules.
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1648
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1649 The :mod:`py_compile` module can manually compile any module. One way is to use
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1650 the ``compile()`` function in that module interactively::
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1651
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1652 >>> import py_compile
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1653 >>> py_compile.compile('abc.py')
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1654
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1655 This will write the ``.pyc`` to the same location as ``abc.py`` (or you can
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1656 override that with the optional parameter ``cfile``).
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1657
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1658 You can also automatically compile all files in a directory or directories using
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1659 the :mod:`compileall` module. You can do it from the shell prompt by running
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1660 ``compileall.py`` and providing the path of a directory containing Python files
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1661 to compile::
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1662
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1663 python -m compileall .
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1664
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1665
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1666 How do I find the current module name?
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1667 --------------------------------------
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1668
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1669 A module can find out its own module name by looking at the predefined global
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1670 variable ``__name__``. If this has the value ``'__main__'``, the program is
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1671 running as a script. Many modules that are usually used by importing them also
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1672 provide a command-line interface or a self-test, and only execute this code
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1673 after checking ``__name__``::
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1674
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1675 def main():
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1676 print 'Running test...'
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1677 ...
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1678
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1679 if __name__ == '__main__':
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1680 main()
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1681
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1682
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1683 How can I have modules that mutually import each other?
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1684 -------------------------------------------------------
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1685
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1686 Suppose you have the following modules:
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1687
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1688 foo.py::
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1689
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1690 from bar import bar_var
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1691 foo_var = 1
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1692
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1693 bar.py::
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1694
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1695 from foo import foo_var
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1696 bar_var = 2
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1697
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1698 The problem is that the interpreter will perform the following steps:
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1699
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1700 * main imports foo
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1701 * Empty globals for foo are created
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1702 * foo is compiled and starts executing
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1703 * foo imports bar
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1704 * Empty globals for bar are created
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1705 * bar is compiled and starts executing
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1706 * bar imports foo (which is a no-op since there already is a module named foo)
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1707 * bar.foo_var = foo.foo_var
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1708
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1709 The last step fails, because Python isn't done with interpreting ``foo`` yet and
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1710 the global symbol dictionary for ``foo`` is still empty.
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1711
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1712 The same thing happens when you use ``import foo``, and then try to access
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1713 ``foo.foo_var`` in global code.
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1714
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1715 There are (at least) three possible workarounds for this problem.
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1716
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1717 Guido van Rossum recommends avoiding all uses of ``from <module> import ...``,
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1718 and placing all code inside functions. Initializations of global variables and
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1719 class variables should use constants or built-in functions only. This means
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1720 everything from an imported module is referenced as ``<module>.<name>``.
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1721
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1722 Jim Roskind suggests performing steps in the following order in each module:
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1723
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1724 * exports (globals, functions, and classes that don't need imported base
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1725 classes)
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1726 * ``import`` statements
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1727 * active code (including globals that are initialized from imported values).
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1728
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1729 van Rossum doesn't like this approach much because the imports appear in a
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1730 strange place, but it does work.
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1731
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1732 Matthias Urlichs recommends restructuring your code so that the recursive import
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1733 is not necessary in the first place.
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1734
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1735 These solutions are not mutually exclusive.
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1736
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1737
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1738 __import__('x.y.z') returns <module 'x'>; how do I get z?
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1739 ---------------------------------------------------------
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1740
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1741 Try::
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1742
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1743 __import__('x.y.z').y.z
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1744
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1745 For more realistic situations, you may have to do something like ::
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1746
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1747 m = __import__(s)
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1748 for i in s.split(".")[1:]:
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1749 m = getattr(m, i)
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1750
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1751 See :mod:`importlib` for a convenience function called
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1752 :func:`~importlib.import_module`.
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1753
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1754
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1755
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1756 When I edit an imported module and reimport it, the changes don't show up. Why does this happen?
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1757 -------------------------------------------------------------------------------------------------
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1758
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1759 For reasons of efficiency as well as consistency, Python only reads the module
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1760 file on the first time a module is imported. If it didn't, in a program
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1761 consisting of many modules where each one imports the same basic module, the
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1762 basic module would be parsed and re-parsed many times. To force rereading of a
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1763 changed module, do this::
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1764
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1765 import modname
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1766 reload(modname)
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1767
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1768 Warning: this technique is not 100% fool-proof. In particular, modules
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1769 containing statements like ::
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1770
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1771 from modname import some_objects
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1772
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1773 will continue to work with the old version of the imported objects. If the
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1774 module contains class definitions, existing class instances will *not* be
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1775 updated to use the new class definition. This can result in the following
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1776 paradoxical behaviour:
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1777
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1778 >>> import cls
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1779 >>> c = cls.C() # Create an instance of C
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1780 >>> reload(cls)
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1781 <module 'cls' from 'cls.pyc'>
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1782 >>> isinstance(c, cls.C) # isinstance is false?!?
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1783 False
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1784
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1785 The nature of the problem is made clear if you print out the class objects:
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1786
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1787 >>> c.__class__
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1788 <class cls.C at 0x7352a0>
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1789 >>> cls.C
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1790 <class cls.C at 0x4198d0>
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1791