Mercurial > cpython-lru_cache-weakref
annotate Doc/tutorial/stdlib2.rst @ 49955:878d40468c4e legacy-trunk
Issue #4384: Added logging integration with warnings module using captureWarnings(). This change includes a NullHandler which does nothing; it will be of use to library developers who want to avoid the "No handlers could be found for logger XXX" message which can appear if the library user doesn't configure logging.
| author | Vinay Sajip <vinay_sajip@yahoo.co.uk> |
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| date | Wed, 03 Dec 2008 23:22:58 +0000 |
| parents | 38d3a50defec |
| children | ee9ced92843a |
| rev | line source |
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1 .. _tut-brieftourtwo: |
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2 |
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3 ********************************************* |
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4 Brief Tour of the Standard Library -- Part II |
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5 ********************************************* |
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6 |
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7 This second tour covers more advanced modules that support professional |
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8 programming needs. These modules rarely occur in small scripts. |
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9 |
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10 |
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11 .. _tut-output-formatting: |
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12 |
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13 Output Formatting |
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14 ================= |
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15 |
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16 The :mod:`repr` module provides a version of :func:`repr` customized for |
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17 abbreviated displays of large or deeply nested containers:: |
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18 |
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19 >>> import repr |
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20 >>> repr.repr(set('supercalifragilisticexpialidocious')) |
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21 "set(['a', 'c', 'd', 'e', 'f', 'g', ...])" |
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22 |
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23 The :mod:`pprint` module offers more sophisticated control over printing both |
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24 built-in and user defined objects in a way that is readable by the interpreter. |
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25 When the result is longer than one line, the "pretty printer" adds line breaks |
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26 and indentation to more clearly reveal data structure:: |
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27 |
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28 >>> import pprint |
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29 >>> t = [[[['black', 'cyan'], 'white', ['green', 'red']], [['magenta', |
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30 ... 'yellow'], 'blue']]] |
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31 ... |
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32 >>> pprint.pprint(t, width=30) |
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33 [[[['black', 'cyan'], |
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34 'white', |
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35 ['green', 'red']], |
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36 [['magenta', 'yellow'], |
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37 'blue']]] |
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38 |
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39 The :mod:`textwrap` module formats paragraphs of text to fit a given screen |
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40 width:: |
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41 |
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42 >>> import textwrap |
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43 >>> doc = """The wrap() method is just like fill() except that it returns |
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44 ... a list of strings instead of one big string with newlines to separate |
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45 ... the wrapped lines.""" |
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46 ... |
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47 >>> print textwrap.fill(doc, width=40) |
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48 The wrap() method is just like fill() |
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49 except that it returns a list of strings |
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50 instead of one big string with newlines |
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51 to separate the wrapped lines. |
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52 |
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53 The :mod:`locale` module accesses a database of culture specific data formats. |
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54 The grouping attribute of locale's format function provides a direct way of |
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55 formatting numbers with group separators:: |
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56 |
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57 >>> import locale |
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58 >>> locale.setlocale(locale.LC_ALL, 'English_United States.1252') |
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59 'English_United States.1252' |
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60 >>> conv = locale.localeconv() # get a mapping of conventions |
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61 >>> x = 1234567.8 |
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62 >>> locale.format("%d", x, grouping=True) |
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63 '1,234,567' |
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64 >>> locale.format("%s%.*f", (conv['currency_symbol'], |
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65 ... conv['frac_digits'], x), grouping=True) |
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66 '$1,234,567.80' |
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67 |
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68 |
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69 .. _tut-templating: |
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70 |
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71 Templating |
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72 ========== |
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73 |
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74 The :mod:`string` module includes a versatile :class:`Template` class with a |
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75 simplified syntax suitable for editing by end-users. This allows users to |
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76 customize their applications without having to alter the application. |
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77 |
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78 The format uses placeholder names formed by ``$`` with valid Python identifiers |
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79 (alphanumeric characters and underscores). Surrounding the placeholder with |
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80 braces allows it to be followed by more alphanumeric letters with no intervening |
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81 spaces. Writing ``$$`` creates a single escaped ``$``:: |
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82 |
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83 >>> from string import Template |
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84 >>> t = Template('${village}folk send $$10 to $cause.') |
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85 >>> t.substitute(village='Nottingham', cause='the ditch fund') |
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86 'Nottinghamfolk send $10 to the ditch fund.' |
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87 |
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88 The :meth:`substitute` method raises a :exc:`KeyError` when a placeholder is not |
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89 supplied in a dictionary or a keyword argument. For mail-merge style |
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90 applications, user supplied data may be incomplete and the |
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91 :meth:`safe_substitute` method may be more appropriate --- it will leave |
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92 placeholders unchanged if data is missing:: |
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93 |
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94 >>> t = Template('Return the $item to $owner.') |
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95 >>> d = dict(item='unladen swallow') |
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96 >>> t.substitute(d) |
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97 Traceback (most recent call last): |
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98 . . . |
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99 KeyError: 'owner' |
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100 >>> t.safe_substitute(d) |
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101 'Return the unladen swallow to $owner.' |
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102 |
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103 Template subclasses can specify a custom delimiter. For example, a batch |
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104 renaming utility for a photo browser may elect to use percent signs for |
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105 placeholders such as the current date, image sequence number, or file format:: |
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106 |
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107 >>> import time, os.path |
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108 >>> photofiles = ['img_1074.jpg', 'img_1076.jpg', 'img_1077.jpg'] |
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109 >>> class BatchRename(Template): |
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110 ... delimiter = '%' |
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111 >>> fmt = raw_input('Enter rename style (%d-date %n-seqnum %f-format): ') |
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112 Enter rename style (%d-date %n-seqnum %f-format): Ashley_%n%f |
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113 |
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114 >>> t = BatchRename(fmt) |
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115 >>> date = time.strftime('%d%b%y') |
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116 >>> for i, filename in enumerate(photofiles): |
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117 ... base, ext = os.path.splitext(filename) |
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118 ... newname = t.substitute(d=date, n=i, f=ext) |
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119 ... print '{0} --> {1}'.format(filename, newname) |
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120 |
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121 img_1074.jpg --> Ashley_0.jpg |
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122 img_1076.jpg --> Ashley_1.jpg |
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123 img_1077.jpg --> Ashley_2.jpg |
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124 |
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125 Another application for templating is separating program logic from the details |
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126 of multiple output formats. This makes it possible to substitute custom |
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127 templates for XML files, plain text reports, and HTML web reports. |
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128 |
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129 |
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130 .. _tut-binary-formats: |
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131 |
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132 Working with Binary Data Record Layouts |
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133 ======================================= |
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134 |
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135 The :mod:`struct` module provides :func:`pack` and :func:`unpack` functions for |
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136 working with variable length binary record formats. The following example shows |
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137 how to loop through header information in a ZIP file without using the |
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138 :mod:`zipfile` module. Pack codes ``"H"`` and ``"I"`` represent two and four |
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139 byte unsigned numbers respectively. The ``"<"`` indicates that they are |
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140 standard size and in little-endian byte order:: |
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141 |
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142 import struct |
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143 |
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144 data = open('myfile.zip', 'rb').read() |
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145 start = 0 |
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146 for i in range(3): # show the first 3 file headers |
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147 start += 14 |
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148 fields = struct.unpack('<IIIHH', data[start:start+16]) |
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149 crc32, comp_size, uncomp_size, filenamesize, extra_size = fields |
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150 |
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151 start += 16 |
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152 filename = data[start:start+filenamesize] |
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153 start += filenamesize |
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154 extra = data[start:start+extra_size] |
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155 print filename, hex(crc32), comp_size, uncomp_size |
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156 |
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157 start += extra_size + comp_size # skip to the next header |
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158 |
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159 |
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160 .. _tut-multi-threading: |
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161 |
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162 Multi-threading |
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163 =============== |
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164 |
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165 Threading is a technique for decoupling tasks which are not sequentially |
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166 dependent. Threads can be used to improve the responsiveness of applications |
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167 that accept user input while other tasks run in the background. A related use |
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168 case is running I/O in parallel with computations in another thread. |
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169 |
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170 The following code shows how the high level :mod:`threading` module can run |
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171 tasks in background while the main program continues to run:: |
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172 |
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173 import threading, zipfile |
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174 |
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175 class AsyncZip(threading.Thread): |
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176 def __init__(self, infile, outfile): |
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177 threading.Thread.__init__(self) |
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178 self.infile = infile |
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179 self.outfile = outfile |
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180 def run(self): |
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181 f = zipfile.ZipFile(self.outfile, 'w', zipfile.ZIP_DEFLATED) |
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182 f.write(self.infile) |
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183 f.close() |
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184 print 'Finished background zip of: ', self.infile |
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185 |
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186 background = AsyncZip('mydata.txt', 'myarchive.zip') |
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187 background.start() |
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188 print 'The main program continues to run in foreground.' |
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189 |
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190 background.join() # Wait for the background task to finish |
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191 print 'Main program waited until background was done.' |
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192 |
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193 The principal challenge of multi-threaded applications is coordinating threads |
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194 that share data or other resources. To that end, the threading module provides |
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195 a number of synchronization primitives including locks, events, condition |
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196 variables, and semaphores. |
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197 |
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198 While those tools are powerful, minor design errors can result in problems that |
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199 are difficult to reproduce. So, the preferred approach to task coordination is |
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200 to concentrate all access to a resource in a single thread and then use the |
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201 :mod:`Queue` module to feed that thread with requests from other threads. |
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202 Applications using :class:`Queue.Queue` objects for inter-thread communication |
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203 and coordination are easier to design, more readable, and more reliable. |
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204 |
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205 |
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206 .. _tut-logging: |
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207 |
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208 Logging |
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209 ======= |
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210 |
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211 The :mod:`logging` module offers a full featured and flexible logging system. |
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212 At its simplest, log messages are sent to a file or to ``sys.stderr``:: |
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213 |
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214 import logging |
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215 logging.debug('Debugging information') |
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216 logging.info('Informational message') |
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217 logging.warning('Warning:config file %s not found', 'server.conf') |
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218 logging.error('Error occurred') |
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219 logging.critical('Critical error -- shutting down') |
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220 |
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221 This produces the following output:: |
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222 |
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223 WARNING:root:Warning:config file server.conf not found |
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224 ERROR:root:Error occurred |
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225 CRITICAL:root:Critical error -- shutting down |
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226 |
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227 By default, informational and debugging messages are suppressed and the output |
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228 is sent to standard error. Other output options include routing messages |
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229 through email, datagrams, sockets, or to an HTTP Server. New filters can select |
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230 different routing based on message priority: :const:`DEBUG`, :const:`INFO`, |
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231 :const:`WARNING`, :const:`ERROR`, and :const:`CRITICAL`. |
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232 |
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233 The logging system can be configured directly from Python or can be loaded from |
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234 a user editable configuration file for customized logging without altering the |
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235 application. |
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236 |
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237 |
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238 .. _tut-weak-references: |
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239 |
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240 Weak References |
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241 =============== |
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242 |
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243 Python does automatic memory management (reference counting for most objects and |
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244 :term:`garbage collection` to eliminate cycles). The memory is freed shortly |
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245 after the last reference to it has been eliminated. |
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246 |
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247 This approach works fine for most applications but occasionally there is a need |
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248 to track objects only as long as they are being used by something else. |
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249 Unfortunately, just tracking them creates a reference that makes them permanent. |
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250 The :mod:`weakref` module provides tools for tracking objects without creating a |
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251 reference. When the object is no longer needed, it is automatically removed |
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252 from a weakref table and a callback is triggered for weakref objects. Typical |
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253 applications include caching objects that are expensive to create:: |
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254 |
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255 >>> import weakref, gc |
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256 >>> class A: |
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257 ... def __init__(self, value): |
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258 ... self.value = value |
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259 ... def __repr__(self): |
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260 ... return str(self.value) |
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261 ... |
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262 >>> a = A(10) # create a reference |
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263 >>> d = weakref.WeakValueDictionary() |
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264 >>> d['primary'] = a # does not create a reference |
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265 >>> d['primary'] # fetch the object if it is still alive |
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266 10 |
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267 >>> del a # remove the one reference |
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268 >>> gc.collect() # run garbage collection right away |
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269 0 |
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270 >>> d['primary'] # entry was automatically removed |
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271 Traceback (most recent call last): |
| 45430 | 272 File "<stdin>", line 1, in <module> |
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273 d['primary'] # entry was automatically removed |
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274 File "C:/python26/lib/weakref.py", line 46, in __getitem__ |
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275 o = self.data[key]() |
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276 KeyError: 'primary' |
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277 |
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278 |
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279 .. _tut-list-tools: |
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280 |
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281 Tools for Working with Lists |
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282 ============================ |
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283 |
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284 Many data structure needs can be met with the built-in list type. However, |
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285 sometimes there is a need for alternative implementations with different |
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286 performance trade-offs. |
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287 |
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288 The :mod:`array` module provides an :class:`array()` object that is like a list |
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289 that stores only homogeneous data and stores it more compactly. The following |
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290 example shows an array of numbers stored as two byte unsigned binary numbers |
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291 (typecode ``"H"``) rather than the usual 16 bytes per entry for regular lists of |
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292 python int objects:: |
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293 |
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294 >>> from array import array |
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295 >>> a = array('H', [4000, 10, 700, 22222]) |
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296 >>> sum(a) |
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297 26932 |
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298 >>> a[1:3] |
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299 array('H', [10, 700]) |
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300 |
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301 The :mod:`collections` module provides a :class:`deque()` object that is like a |
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302 list with faster appends and pops from the left side but slower lookups in the |
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303 middle. These objects are well suited for implementing queues and breadth first |
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304 tree searches:: |
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305 |
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306 >>> from collections import deque |
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307 >>> d = deque(["task1", "task2", "task3"]) |
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308 >>> d.append("task4") |
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309 >>> print "Handling", d.popleft() |
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310 Handling task1 |
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311 |
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312 unsearched = deque([starting_node]) |
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313 def breadth_first_search(unsearched): |
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314 node = unsearched.popleft() |
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315 for m in gen_moves(node): |
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316 if is_goal(m): |
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317 return m |
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318 unsearched.append(m) |
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319 |
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320 In addition to alternative list implementations, the library also offers other |
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321 tools such as the :mod:`bisect` module with functions for manipulating sorted |
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322 lists:: |
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323 |
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324 >>> import bisect |
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325 >>> scores = [(100, 'perl'), (200, 'tcl'), (400, 'lua'), (500, 'python')] |
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326 >>> bisect.insort(scores, (300, 'ruby')) |
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327 >>> scores |
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328 [(100, 'perl'), (200, 'tcl'), (300, 'ruby'), (400, 'lua'), (500, 'python')] |
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329 |
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330 The :mod:`heapq` module provides functions for implementing heaps based on |
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331 regular lists. The lowest valued entry is always kept at position zero. This |
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332 is useful for applications which repeatedly access the smallest element but do |
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333 not want to run a full list sort:: |
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334 |
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335 >>> from heapq import heapify, heappop, heappush |
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336 >>> data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0] |
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337 >>> heapify(data) # rearrange the list into heap order |
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338 >>> heappush(data, -5) # add a new entry |
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339 >>> [heappop(data) for i in range(3)] # fetch the three smallest entries |
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340 [-5, 0, 1] |
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341 |
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342 |
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343 .. _tut-decimal-fp: |
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344 |
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345 Decimal Floating Point Arithmetic |
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346 ================================= |
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347 |
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348 The :mod:`decimal` module offers a :class:`Decimal` datatype for decimal |
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349 floating point arithmetic. Compared to the built-in :class:`float` |
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350 implementation of binary floating point, the new class is especially helpful for |
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351 financial applications and other uses which require exact decimal |
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352 representation, control over precision, control over rounding to meet legal or |
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353 regulatory requirements, tracking of significant decimal places, or for |
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354 applications where the user expects the results to match calculations done by |
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355 hand. |
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356 |
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357 For example, calculating a 5% tax on a 70 cent phone charge gives different |
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358 results in decimal floating point and binary floating point. The difference |
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359 becomes significant if the results are rounded to the nearest cent:: |
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360 |
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361 >>> from decimal import * |
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362 >>> Decimal('0.70') * Decimal('1.05') |
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363 Decimal("0.7350") |
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364 >>> .70 * 1.05 |
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365 0.73499999999999999 |
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366 |
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367 The :class:`Decimal` result keeps a trailing zero, automatically inferring four |
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368 place significance from multiplicands with two place significance. Decimal |
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369 reproduces mathematics as done by hand and avoids issues that can arise when |
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370 binary floating point cannot exactly represent decimal quantities. |
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371 |
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372 Exact representation enables the :class:`Decimal` class to perform modulo |
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373 calculations and equality tests that are unsuitable for binary floating point:: |
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374 |
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375 >>> Decimal('1.00') % Decimal('.10') |
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376 Decimal("0.00") |
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377 >>> 1.00 % 0.10 |
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378 0.09999999999999995 |
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379 |
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380 >>> sum([Decimal('0.1')]*10) == Decimal('1.0') |
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381 True |
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382 >>> sum([0.1]*10) == 1.0 |
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383 False |
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384 |
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385 The :mod:`decimal` module provides arithmetic with as much precision as needed:: |
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386 |
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387 >>> getcontext().prec = 36 |
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388 >>> Decimal(1) / Decimal(7) |
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389 Decimal("0.142857142857142857142857142857142857") |
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390 |
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391 |
