Mercurial > cpython-lru_cache-weakref
annotate Doc/tutorial/datastructures.rst @ 80661:94de77bd0b4b 2.7
Issue #16194: document imp.load_dynamic problems
when read several extension modules from the same binary object file.
| author | Andrew Svetlov <andrew.svetlov@gmail.com> |
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| date | Thu, 29 Nov 2012 19:51:28 +0200 |
| parents | 2e2e7f398864 |
| children | c2e4ab7ed26e |
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1 .. _tut-structures: |
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2 |
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3 *************** |
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4 Data Structures |
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5 *************** |
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6 |
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7 This chapter describes some things you've learned about already in more detail, |
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8 and adds some new things as well. |
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9 |
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10 |
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11 .. _tut-morelists: |
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12 |
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13 More on Lists |
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14 ============= |
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15 |
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16 The list data type has some more methods. Here are all of the methods of list |
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17 objects: |
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18 |
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19 |
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20 .. method:: list.append(x) |
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21 :noindex: |
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22 |
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23 Add an item to the end of the list; equivalent to ``a[len(a):] = [x]``. |
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24 |
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25 |
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26 .. method:: list.extend(L) |
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27 :noindex: |
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28 |
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29 Extend the list by appending all the items in the given list; equivalent to |
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30 ``a[len(a):] = L``. |
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31 |
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32 |
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33 .. method:: list.insert(i, x) |
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34 :noindex: |
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35 |
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36 Insert an item at a given position. The first argument is the index of the |
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37 element before which to insert, so ``a.insert(0, x)`` inserts at the front of |
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38 the list, and ``a.insert(len(a), x)`` is equivalent to ``a.append(x)``. |
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39 |
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40 |
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41 .. method:: list.remove(x) |
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42 :noindex: |
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43 |
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44 Remove the first item from the list whose value is *x*. It is an error if there |
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45 is no such item. |
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46 |
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47 |
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48 .. method:: list.pop([i]) |
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49 :noindex: |
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50 |
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51 Remove the item at the given position in the list, and return it. If no index |
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52 is specified, ``a.pop()`` removes and returns the last item in the list. (The |
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53 square brackets around the *i* in the method signature denote that the parameter |
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54 is optional, not that you should type square brackets at that position. You |
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55 will see this notation frequently in the Python Library Reference.) |
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56 |
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57 |
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58 .. method:: list.index(x) |
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59 :noindex: |
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60 |
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61 Return the index in the list of the first item whose value is *x*. It is an |
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62 error if there is no such item. |
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63 |
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64 |
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65 .. method:: list.count(x) |
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66 :noindex: |
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67 |
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68 Return the number of times *x* appears in the list. |
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69 |
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70 |
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71 .. method:: list.sort() |
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72 :noindex: |
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73 |
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74 Sort the items of the list, in place. |
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75 |
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76 |
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77 .. method:: list.reverse() |
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78 :noindex: |
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79 |
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80 Reverse the elements of the list, in place. |
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81 |
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82 An example that uses most of the list methods:: |
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83 |
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84 >>> a = [66.25, 333, 333, 1, 1234.5] |
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85 >>> print a.count(333), a.count(66.25), a.count('x') |
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86 2 1 0 |
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87 >>> a.insert(2, -1) |
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88 >>> a.append(333) |
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89 >>> a |
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90 [66.25, 333, -1, 333, 1, 1234.5, 333] |
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91 >>> a.index(333) |
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92 1 |
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93 >>> a.remove(333) |
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94 >>> a |
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95 [66.25, -1, 333, 1, 1234.5, 333] |
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96 >>> a.reverse() |
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97 >>> a |
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98 [333, 1234.5, 1, 333, -1, 66.25] |
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99 >>> a.sort() |
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100 >>> a |
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101 [-1, 1, 66.25, 333, 333, 1234.5] |
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102 |
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103 |
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104 .. _tut-lists-as-stacks: |
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105 |
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106 Using Lists as Stacks |
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107 --------------------- |
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108 |
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109 .. sectionauthor:: Ka-Ping Yee <ping@lfw.org> |
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110 |
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111 |
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112 The list methods make it very easy to use a list as a stack, where the last |
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113 element added is the first element retrieved ("last-in, first-out"). To add an |
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114 item to the top of the stack, use :meth:`append`. To retrieve an item from the |
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115 top of the stack, use :meth:`pop` without an explicit index. For example:: |
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116 |
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117 >>> stack = [3, 4, 5] |
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118 >>> stack.append(6) |
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119 >>> stack.append(7) |
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120 >>> stack |
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121 [3, 4, 5, 6, 7] |
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122 >>> stack.pop() |
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123 7 |
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124 >>> stack |
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125 [3, 4, 5, 6] |
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126 >>> stack.pop() |
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127 6 |
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128 >>> stack.pop() |
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129 5 |
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130 >>> stack |
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131 [3, 4] |
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132 |
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133 |
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134 .. _tut-lists-as-queues: |
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135 |
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136 Using Lists as Queues |
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137 --------------------- |
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138 |
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139 .. sectionauthor:: Ka-Ping Yee <ping@lfw.org> |
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140 |
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141 It is also possible to use a list as a queue, where the first element added is |
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142 the first element retrieved ("first-in, first-out"); however, lists are not |
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143 efficient for this purpose. While appends and pops from the end of list are |
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144 fast, doing inserts or pops from the beginning of a list is slow (because all |
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145 of the other elements have to be shifted by one). |
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146 |
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147 To implement a queue, use :class:`collections.deque` which was designed to |
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148 have fast appends and pops from both ends. For example:: |
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149 |
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150 >>> from collections import deque |
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151 >>> queue = deque(["Eric", "John", "Michael"]) |
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152 >>> queue.append("Terry") # Terry arrives |
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153 >>> queue.append("Graham") # Graham arrives |
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154 >>> queue.popleft() # The first to arrive now leaves |
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155 'Eric' |
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156 >>> queue.popleft() # The second to arrive now leaves |
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157 'John' |
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158 >>> queue # Remaining queue in order of arrival |
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159 deque(['Michael', 'Terry', 'Graham']) |
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160 |
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161 |
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162 .. _tut-functional: |
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163 |
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164 Functional Programming Tools |
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165 ---------------------------- |
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166 |
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167 There are three built-in functions that are very useful when used with lists: |
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168 :func:`filter`, :func:`map`, and :func:`reduce`. |
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169 |
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170 ``filter(function, sequence)`` returns a sequence consisting of those items from |
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171 the sequence for which ``function(item)`` is true. If *sequence* is a |
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172 :class:`string` or :class:`tuple`, the result will be of the same type; |
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173 otherwise, it is always a :class:`list`. For example, to compute a sequence of |
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174 numbers not divisible by 2 and 3:: |
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175 |
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176 >>> def f(x): return x % 2 != 0 and x % 3 != 0 |
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177 ... |
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178 >>> filter(f, range(2, 25)) |
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179 [5, 7, 11, 13, 17, 19, 23] |
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180 |
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181 ``map(function, sequence)`` calls ``function(item)`` for each of the sequence's |
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182 items and returns a list of the return values. For example, to compute some |
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183 cubes:: |
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184 |
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185 >>> def cube(x): return x*x*x |
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186 ... |
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187 >>> map(cube, range(1, 11)) |
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188 [1, 8, 27, 64, 125, 216, 343, 512, 729, 1000] |
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189 |
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190 More than one sequence may be passed; the function must then have as many |
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191 arguments as there are sequences and is called with the corresponding item from |
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192 each sequence (or ``None`` if some sequence is shorter than another). For |
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193 example:: |
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194 |
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195 >>> seq = range(8) |
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196 >>> def add(x, y): return x+y |
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197 ... |
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198 >>> map(add, seq, seq) |
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199 [0, 2, 4, 6, 8, 10, 12, 14] |
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200 |
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201 ``reduce(function, sequence)`` returns a single value constructed by calling the |
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202 binary function *function* on the first two items of the sequence, then on the |
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203 result and the next item, and so on. For example, to compute the sum of the |
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204 numbers 1 through 10:: |
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205 |
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206 >>> def add(x,y): return x+y |
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207 ... |
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208 >>> reduce(add, range(1, 11)) |
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209 55 |
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210 |
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211 If there's only one item in the sequence, its value is returned; if the sequence |
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212 is empty, an exception is raised. |
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213 |
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214 A third argument can be passed to indicate the starting value. In this case the |
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215 starting value is returned for an empty sequence, and the function is first |
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216 applied to the starting value and the first sequence item, then to the result |
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217 and the next item, and so on. For example, :: |
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218 |
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219 >>> def sum(seq): |
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220 ... def add(x,y): return x+y |
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221 ... return reduce(add, seq, 0) |
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222 ... |
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223 >>> sum(range(1, 11)) |
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224 55 |
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225 >>> sum([]) |
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226 0 |
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227 |
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228 Don't use this example's definition of :func:`sum`: since summing numbers is |
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229 such a common need, a built-in function ``sum(sequence)`` is already provided, |
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230 and works exactly like this. |
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231 |
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232 .. _tut-listcomps: |
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233 |
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234 List Comprehensions |
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235 ------------------- |
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236 |
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237 List comprehensions provide a concise way to create lists. |
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238 Common applications are to make new lists where each element is the result of |
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239 some operations applied to each member of another sequence or iterable, or to |
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240 create a subsequence of those elements that satisfy a certain condition. |
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241 |
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242 For example, assume we want to create a list of squares, like:: |
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243 |
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244 >>> squares = [] |
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245 >>> for x in range(10): |
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246 ... squares.append(x**2) |
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247 ... |
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248 >>> squares |
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249 [0, 1, 4, 9, 16, 25, 36, 49, 64, 81] |
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250 |
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251 We can obtain the same result with:: |
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252 |
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253 squares = [x**2 for x in range(10)] |
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254 |
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255 This is also equivalent to ``squares = map(lambda x: x**2, range(10))``, |
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256 but it's more concise and readable. |
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257 |
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258 A list comprehension consists of brackets containing an expression followed |
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259 by a :keyword:`for` clause, then zero or more :keyword:`for` or :keyword:`if` |
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260 clauses. The result will be a new list resulting from evaluating the expression |
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261 in the context of the :keyword:`for` and :keyword:`if` clauses which follow it. |
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262 For example, this listcomp combines the elements of two lists if they are not |
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263 equal:: |
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264 |
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265 >>> [(x, y) for x in [1,2,3] for y in [3,1,4] if x != y] |
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266 [(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)] |
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267 |
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268 and it's equivalent to: |
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269 |
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270 >>> combs = [] |
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271 >>> for x in [1,2,3]: |
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272 ... for y in [3,1,4]: |
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273 ... if x != y: |
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274 ... combs.append((x, y)) |
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275 ... |
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276 >>> combs |
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277 [(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)] |
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278 |
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279 Note how the order of the :keyword:`for` and :keyword:`if` statements is the |
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280 same in both these snippets. |
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281 |
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282 If the expression is a tuple (e.g. the ``(x, y)`` in the previous example), |
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283 it must be parenthesized. :: |
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284 |
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285 >>> vec = [-4, -2, 0, 2, 4] |
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286 >>> # create a new list with the values doubled |
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287 >>> [x*2 for x in vec] |
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288 [-8, -4, 0, 4, 8] |
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289 >>> # filter the list to exclude negative numbers |
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290 >>> [x for x in vec if x >= 0] |
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291 [0, 2, 4] |
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292 >>> # apply a function to all the elements |
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293 >>> [abs(x) for x in vec] |
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294 [4, 2, 0, 2, 4] |
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295 >>> # call a method on each element |
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296 >>> freshfruit = [' banana', ' loganberry ', 'passion fruit '] |
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297 >>> [weapon.strip() for weapon in freshfruit] |
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298 ['banana', 'loganberry', 'passion fruit'] |
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299 >>> # create a list of 2-tuples like (number, square) |
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300 >>> [(x, x**2) for x in range(6)] |
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301 [(0, 0), (1, 1), (2, 4), (3, 9), (4, 16), (5, 25)] |
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302 >>> # the tuple must be parenthesized, otherwise an error is raised |
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303 >>> [x, x**2 for x in range(6)] |
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304 File "<stdin>", line 1 |
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305 [x, x**2 for x in range(6)] |
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306 ^ |
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307 SyntaxError: invalid syntax |
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308 >>> # flatten a list using a listcomp with two 'for' |
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309 >>> vec = [[1,2,3], [4,5,6], [7,8,9]] |
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310 >>> [num for elem in vec for num in elem] |
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311 [1, 2, 3, 4, 5, 6, 7, 8, 9] |
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312 |
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313 List comprehensions can contain complex expressions and nested functions:: |
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314 |
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315 >>> from math import pi |
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316 >>> [str(round(pi, i)) for i in range(1, 6)] |
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317 ['3.1', '3.14', '3.142', '3.1416', '3.14159'] |
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318 |
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319 |
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320 Nested List Comprehensions |
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321 '''''''''''''''''''''''''' |
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322 |
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323 The initial expression in a list comprehension can be any arbitrary expression, |
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324 including another list comprehension. |
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325 |
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326 Consider the following example of a 3x4 matrix implemented as a list of |
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327 3 lists of length 4:: |
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328 |
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329 >>> matrix = [ |
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330 ... [1, 2, 3, 4], |
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331 ... [5, 6, 7, 8], |
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332 ... [9, 10, 11, 12], |
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333 ... ] |
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334 |
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335 The following list comprehension will transpose rows and columns:: |
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336 |
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337 >>> [[row[i] for row in matrix] for i in range(4)] |
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338 [[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]] |
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339 |
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340 As we saw in the previous section, the nested listcomp is evaluated in |
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341 the context of the :keyword:`for` that follows it, so this example is |
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342 equivalent to:: |
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343 |
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344 >>> transposed = [] |
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345 >>> for i in range(4): |
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346 ... transposed.append([row[i] for row in matrix]) |
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347 ... |
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348 >>> transposed |
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349 [[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]] |
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350 |
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351 which, in turn, is the same as:: |
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352 |
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353 >>> transposed = [] |
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354 >>> for i in range(4): |
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355 ... # the following 3 lines implement the nested listcomp |
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356 ... transposed_row = [] |
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357 ... for row in matrix: |
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358 ... transposed_row.append(row[i]) |
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359 ... transposed.append(transposed_row) |
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360 ... |
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361 >>> transposed |
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362 [[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]] |
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363 |
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364 |
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365 In the real world, you should prefer built-in functions to complex flow statements. |
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366 The :func:`zip` function would do a great job for this use case:: |
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367 |
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368 >>> zip(*matrix) |
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369 [(1, 5, 9), (2, 6, 10), (3, 7, 11), (4, 8, 12)] |
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370 |
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371 See :ref:`tut-unpacking-arguments` for details on the asterisk in this line. |
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372 |
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373 .. _tut-del: |
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374 |
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375 The :keyword:`del` statement |
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376 ============================ |
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377 |
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378 There is a way to remove an item from a list given its index instead of its |
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379 value: the :keyword:`del` statement. This differs from the :meth:`pop` method |
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380 which returns a value. The :keyword:`del` statement can also be used to remove |
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381 slices from a list or clear the entire list (which we did earlier by assignment |
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382 of an empty list to the slice). For example:: |
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383 |
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384 >>> a = [-1, 1, 66.25, 333, 333, 1234.5] |
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385 >>> del a[0] |
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386 >>> a |
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387 [1, 66.25, 333, 333, 1234.5] |
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388 >>> del a[2:4] |
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389 >>> a |
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390 [1, 66.25, 1234.5] |
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391 >>> del a[:] |
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392 >>> a |
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393 [] |
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394 |
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395 :keyword:`del` can also be used to delete entire variables:: |
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396 |
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397 >>> del a |
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398 |
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399 Referencing the name ``a`` hereafter is an error (at least until another value |
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400 is assigned to it). We'll find other uses for :keyword:`del` later. |
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401 |
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402 |
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403 .. _tut-tuples: |
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404 |
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405 Tuples and Sequences |
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406 ==================== |
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407 |
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408 We saw that lists and strings have many common properties, such as indexing and |
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409 slicing operations. They are two examples of *sequence* data types (see |
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410 :ref:`typesseq`). Since Python is an evolving language, other sequence data |
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411 types may be added. There is also another standard sequence data type: the |
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412 *tuple*. |
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413 |
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414 A tuple consists of a number of values separated by commas, for instance:: |
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415 |
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416 >>> t = 12345, 54321, 'hello!' |
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417 >>> t[0] |
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418 12345 |
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419 >>> t |
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420 (12345, 54321, 'hello!') |
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421 >>> # Tuples may be nested: |
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422 ... u = t, (1, 2, 3, 4, 5) |
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423 >>> u |
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424 ((12345, 54321, 'hello!'), (1, 2, 3, 4, 5)) |
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425 >>> # Tuples are immutable: |
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426 ... t[0] = 88888 |
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427 Traceback (most recent call last): |
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428 File "<stdin>", line 1, in <module> |
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429 TypeError: 'tuple' object does not support item assignment |
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430 >>> # but they can contain mutable objects: |
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431 ... v = ([1, 2, 3], [3, 2, 1]) |
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432 >>> v |
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433 ([1, 2, 3], [3, 2, 1]) |
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434 |
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435 |
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436 As you see, on output tuples are always enclosed in parentheses, so that nested |
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437 tuples are interpreted correctly; they may be input with or without surrounding |
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438 parentheses, although often parentheses are necessary anyway (if the tuple is |
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439 part of a larger expression). It is not possible to assign to the individual |
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440 items of a tuple, however it is possible to create tuples which contain mutable |
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441 objects, such as lists. |
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442 |
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443 Though tuples may seem similar to lists, they are often used in different |
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444 situations and for different purposes. |
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445 Tuples are :term:`immutable`, and usually contain an heterogeneous sequence of |
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446 elements that are accessed via unpacking (see later in this section) or indexing |
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447 (or even by attribute in the case of :func:`namedtuples <collections.namedtuple>`). |
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448 Lists are :term:`mutable`, and their elements are usually homogeneous and are |
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449 accessed by iterating over the list. |
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450 |
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451 A special problem is the construction of tuples containing 0 or 1 items: the |
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452 syntax has some extra quirks to accommodate these. Empty tuples are constructed |
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453 by an empty pair of parentheses; a tuple with one item is constructed by |
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454 following a value with a comma (it is not sufficient to enclose a single value |
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455 in parentheses). Ugly, but effective. For example:: |
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456 |
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457 >>> empty = () |
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458 >>> singleton = 'hello', # <-- note trailing comma |
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459 >>> len(empty) |
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460 0 |
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461 >>> len(singleton) |
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462 1 |
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463 >>> singleton |
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464 ('hello',) |
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465 |
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466 The statement ``t = 12345, 54321, 'hello!'`` is an example of *tuple packing*: |
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467 the values ``12345``, ``54321`` and ``'hello!'`` are packed together in a tuple. |
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468 The reverse operation is also possible:: |
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469 |
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470 >>> x, y, z = t |
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471 |
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472 This is called, appropriately enough, *sequence unpacking* and works for any |
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473 sequence on the right-hand side. Sequence unpacking requires the list of |
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474 variables on the left to have the same number of elements as the length of the |
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475 sequence. Note that multiple assignment is really just a combination of tuple |
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476 packing and sequence unpacking. |
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477 |
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478 |
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479 .. _tut-sets: |
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480 |
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481 Sets |
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482 ==== |
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483 |
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484 Python also includes a data type for *sets*. A set is an unordered collection |
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485 with no duplicate elements. Basic uses include membership testing and |
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486 eliminating duplicate entries. Set objects also support mathematical operations |
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487 like union, intersection, difference, and symmetric difference. |
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488 |
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489 Curly braces or the :func:`set` function can be used to create sets. Note: to |
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490 create an empty set you have to use ``set()``, not ``{}``; the latter creates an |
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491 empty dictionary, a data structure that we discuss in the next section. |
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492 |
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493 Here is a brief demonstration:: |
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494 |
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495 >>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana'] |
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496 >>> fruit = set(basket) # create a set without duplicates |
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497 >>> fruit |
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498 set(['orange', 'pear', 'apple', 'banana']) |
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499 >>> 'orange' in fruit # fast membership testing |
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500 True |
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501 >>> 'crabgrass' in fruit |
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502 False |
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503 |
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504 >>> # Demonstrate set operations on unique letters from two words |
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505 ... |
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506 >>> a = set('abracadabra') |
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507 >>> b = set('alacazam') |
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508 >>> a # unique letters in a |
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509 set(['a', 'r', 'b', 'c', 'd']) |
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510 >>> a - b # letters in a but not in b |
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511 set(['r', 'd', 'b']) |
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512 >>> a | b # letters in either a or b |
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513 set(['a', 'c', 'r', 'd', 'b', 'm', 'z', 'l']) |
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514 >>> a & b # letters in both a and b |
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515 set(['a', 'c']) |
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516 >>> a ^ b # letters in a or b but not both |
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517 set(['r', 'd', 'b', 'm', 'z', 'l']) |
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518 |
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519 Similarly to :ref:`list comprehensions <tut-listcomps>`, set comprehensions |
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520 are also supported:: |
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521 |
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522 >>> a = {x for x in 'abracadabra' if x not in 'abc'} |
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523 >>> a |
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524 set(['r', 'd']) |
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525 |
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526 |
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527 .. _tut-dictionaries: |
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528 |
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529 Dictionaries |
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530 ============ |
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531 |
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532 Another useful data type built into Python is the *dictionary* (see |
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533 :ref:`typesmapping`). Dictionaries are sometimes found in other languages as |
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534 "associative memories" or "associative arrays". Unlike sequences, which are |
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535 indexed by a range of numbers, dictionaries are indexed by *keys*, which can be |
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536 any immutable type; strings and numbers can always be keys. Tuples can be used |
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537 as keys if they contain only strings, numbers, or tuples; if a tuple contains |
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538 any mutable object either directly or indirectly, it cannot be used as a key. |
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539 You can't use lists as keys, since lists can be modified in place using index |
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540 assignments, slice assignments, or methods like :meth:`append` and |
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541 :meth:`extend`. |
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542 |
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543 It is best to think of a dictionary as an unordered set of *key: value* pairs, |
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544 with the requirement that the keys are unique (within one dictionary). A pair of |
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545 braces creates an empty dictionary: ``{}``. Placing a comma-separated list of |
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546 key:value pairs within the braces adds initial key:value pairs to the |
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547 dictionary; this is also the way dictionaries are written on output. |
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548 |
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549 The main operations on a dictionary are storing a value with some key and |
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550 extracting the value given the key. It is also possible to delete a key:value |
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551 pair with ``del``. If you store using a key that is already in use, the old |
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552 value associated with that key is forgotten. It is an error to extract a value |
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553 using a non-existent key. |
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554 |
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555 The :meth:`keys` method of a dictionary object returns a list of all the keys |
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556 used in the dictionary, in arbitrary order (if you want it sorted, just apply |
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557 the :func:`sorted` function to it). To check whether a single key is in the |
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558 dictionary, use the :keyword:`in` keyword. |
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559 |
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560 Here is a small example using a dictionary:: |
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561 |
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562 >>> tel = {'jack': 4098, 'sape': 4139} |
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563 >>> tel['guido'] = 4127 |
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564 >>> tel |
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565 {'sape': 4139, 'guido': 4127, 'jack': 4098} |
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566 >>> tel['jack'] |
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567 4098 |
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568 >>> del tel['sape'] |
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569 >>> tel['irv'] = 4127 |
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570 >>> tel |
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571 {'guido': 4127, 'irv': 4127, 'jack': 4098} |
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572 >>> tel.keys() |
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573 ['guido', 'irv', 'jack'] |
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574 >>> 'guido' in tel |
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575 True |
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576 |
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577 The :func:`dict` constructor builds dictionaries directly from sequences of |
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578 key-value pairs:: |
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579 |
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580 >>> dict([('sape', 4139), ('guido', 4127), ('jack', 4098)]) |
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581 {'sape': 4139, 'jack': 4098, 'guido': 4127} |
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583 In addition, dict comprehensions can be used to create dictionaries from |
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584 arbitrary key and value expressions:: |
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585 |
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586 >>> {x: x**2 for x in (2, 4, 6)} |
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587 {2: 4, 4: 16, 6: 36} |
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588 |
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589 When the keys are simple strings, it is sometimes easier to specify pairs using |
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590 keyword arguments:: |
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591 |
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592 >>> dict(sape=4139, guido=4127, jack=4098) |
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593 {'sape': 4139, 'jack': 4098, 'guido': 4127} |
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594 |
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595 |
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596 .. _tut-loopidioms: |
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597 |
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598 Looping Techniques |
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599 ================== |
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600 |
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601 When looping through a sequence, the position index and corresponding value can |
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602 be retrieved at the same time using the :func:`enumerate` function. :: |
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603 |
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604 >>> for i, v in enumerate(['tic', 'tac', 'toe']): |
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605 ... print i, v |
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606 ... |
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607 0 tic |
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608 1 tac |
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609 2 toe |
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610 |
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611 To loop over two or more sequences at the same time, the entries can be paired |
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612 with the :func:`zip` function. :: |
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613 |
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614 >>> questions = ['name', 'quest', 'favorite color'] |
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615 >>> answers = ['lancelot', 'the holy grail', 'blue'] |
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616 >>> for q, a in zip(questions, answers): |
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617 ... print 'What is your {0}? It is {1}.'.format(q, a) |
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618 ... |
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619 What is your name? It is lancelot. |
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620 What is your quest? It is the holy grail. |
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621 What is your favorite color? It is blue. |
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622 |
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623 To loop over a sequence in reverse, first specify the sequence in a forward |
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624 direction and then call the :func:`reversed` function. :: |
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625 |
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626 >>> for i in reversed(xrange(1,10,2)): |
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627 ... print i |
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628 ... |
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629 9 |
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630 7 |
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631 5 |
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632 3 |
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633 1 |
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634 |
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635 To loop over a sequence in sorted order, use the :func:`sorted` function which |
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636 returns a new sorted list while leaving the source unaltered. :: |
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637 |
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638 >>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana'] |
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639 >>> for f in sorted(set(basket)): |
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640 ... print f |
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641 ... |
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642 apple |
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643 banana |
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644 orange |
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645 pear |
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646 |
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647 When looping through dictionaries, the key and corresponding value can be |
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648 retrieved at the same time using the :meth:`iteritems` method. :: |
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649 |
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650 >>> knights = {'gallahad': 'the pure', 'robin': 'the brave'} |
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651 >>> for k, v in knights.iteritems(): |
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652 ... print k, v |
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653 ... |
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654 gallahad the pure |
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655 robin the brave |
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656 |
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657 To change a sequence you are iterating over while inside the loop (for |
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658 example to duplicate certain items), it is recommended that you first make |
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659 a copy. Looping over a sequence does not implicitly make a copy. The slice |
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660 notation makes this especially convenient:: |
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661 |
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662 >>> words = ['cat', 'window', 'defenestrate'] |
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663 >>> for w in words[:]: # Loop over a slice copy of the entire list. |
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664 ... if len(w) > 6: |
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665 ... words.insert(0, w) |
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666 ... |
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667 >>> words |
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668 ['defenestrate', 'cat', 'window', 'defenestrate'] |
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669 |
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670 |
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671 .. _tut-conditions: |
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672 |
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673 More on Conditions |
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674 ================== |
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675 |
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676 The conditions used in ``while`` and ``if`` statements can contain any |
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677 operators, not just comparisons. |
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678 |
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679 The comparison operators ``in`` and ``not in`` check whether a value occurs |
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680 (does not occur) in a sequence. The operators ``is`` and ``is not`` compare |
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681 whether two objects are really the same object; this only matters for mutable |
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682 objects like lists. All comparison operators have the same priority, which is |
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683 lower than that of all numerical operators. |
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684 |
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685 Comparisons can be chained. For example, ``a < b == c`` tests whether ``a`` is |
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686 less than ``b`` and moreover ``b`` equals ``c``. |
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687 |
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688 Comparisons may be combined using the Boolean operators ``and`` and ``or``, and |
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689 the outcome of a comparison (or of any other Boolean expression) may be negated |
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690 with ``not``. These have lower priorities than comparison operators; between |
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691 them, ``not`` has the highest priority and ``or`` the lowest, so that ``A and |
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692 not B or C`` is equivalent to ``(A and (not B)) or C``. As always, parentheses |
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693 can be used to express the desired composition. |
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694 |
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695 The Boolean operators ``and`` and ``or`` are so-called *short-circuit* |
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696 operators: their arguments are evaluated from left to right, and evaluation |
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697 stops as soon as the outcome is determined. For example, if ``A`` and ``C`` are |
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698 true but ``B`` is false, ``A and B and C`` does not evaluate the expression |
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699 ``C``. When used as a general value and not as a Boolean, the return value of a |
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700 short-circuit operator is the last evaluated argument. |
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701 |
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702 It is possible to assign the result of a comparison or other Boolean expression |
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703 to a variable. For example, :: |
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704 |
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705 >>> string1, string2, string3 = '', 'Trondheim', 'Hammer Dance' |
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706 >>> non_null = string1 or string2 or string3 |
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707 >>> non_null |
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708 'Trondheim' |
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709 |
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710 Note that in Python, unlike C, assignment cannot occur inside expressions. C |
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711 programmers may grumble about this, but it avoids a common class of problems |
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712 encountered in C programs: typing ``=`` in an expression when ``==`` was |
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713 intended. |
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714 |
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715 |
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716 .. _tut-comparing: |
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717 |
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718 Comparing Sequences and Other Types |
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719 =================================== |
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720 |
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721 Sequence objects may be compared to other objects with the same sequence type. |
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722 The comparison uses *lexicographical* ordering: first the first two items are |
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723 compared, and if they differ this determines the outcome of the comparison; if |
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724 they are equal, the next two items are compared, and so on, until either |
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725 sequence is exhausted. If two items to be compared are themselves sequences of |
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726 the same type, the lexicographical comparison is carried out recursively. If |
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727 all items of two sequences compare equal, the sequences are considered equal. |
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728 If one sequence is an initial sub-sequence of the other, the shorter sequence is |
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729 the smaller (lesser) one. Lexicographical ordering for strings uses the ASCII |
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730 ordering for individual characters. Some examples of comparisons between |
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731 sequences of the same type:: |
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732 |
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733 (1, 2, 3) < (1, 2, 4) |
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734 [1, 2, 3] < [1, 2, 4] |
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735 'ABC' < 'C' < 'Pascal' < 'Python' |
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736 (1, 2, 3, 4) < (1, 2, 4) |
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737 (1, 2) < (1, 2, -1) |
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738 (1, 2, 3) == (1.0, 2.0, 3.0) |
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739 (1, 2, ('aa', 'ab')) < (1, 2, ('abc', 'a'), 4) |
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740 |
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741 Note that comparing objects of different types is legal. The outcome is |
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742 deterministic but arbitrary: the types are ordered by their name. Thus, a list |
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743 is always smaller than a string, a string is always smaller than a tuple, etc. |
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744 [#]_ Mixed numeric types are compared according to their numeric value, so 0 |
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745 equals 0.0, etc. |
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746 |
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747 |
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748 .. rubric:: Footnotes |
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749 |
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750 .. [#] The rules for comparing objects of different types should not be relied upon; |
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751 they may change in a future version of the language. |
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752 |
