Mercurial > cpython-lru_cache-weakref
annotate Doc/tutorial/datastructures.rst @ 72990:8564d2b240b6
default - Fix closes Issue12529 - cgi.parse_header failure on double quotes and
semicolons. Patch by Ben Darnell and Petri Lehtinen.
| author | Senthil Kumaran <senthil@uthcode.com> |
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| date | Thu, 20 Oct 2011 01:06:59 +0800 |
| parents | 352d075839f7 |
| children | 37094a1454ab |
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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 .. _tut-morelists: |
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11 |
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12 More on Lists |
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13 ============= |
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14 |
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15 The list data type has some more methods. Here are all of the methods of list |
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16 objects: |
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17 |
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18 |
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19 .. method:: list.append(x) |
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20 :noindex: |
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21 |
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22 Add an item to the end of the list. Equivalent to ``a[len(a):] = [x]``. |
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23 |
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24 |
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25 .. method:: list.extend(L) |
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26 :noindex: |
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27 |
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28 Extend the list by appending all the items in the given list. Equivalent to |
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29 ``a[len(a):] = L``. |
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30 |
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31 |
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32 .. method:: list.insert(i, x) |
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33 :noindex: |
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34 |
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35 Insert an item at a given position. The first argument is the index of the |
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36 element before which to insert, so ``a.insert(0, x)`` inserts at the front of |
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37 the list, and ``a.insert(len(a), x)`` is equivalent to ``a.append(x)``. |
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38 |
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39 |
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40 .. method:: list.remove(x) |
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41 :noindex: |
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42 |
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43 Remove the first item from the list whose value is *x*. It is an error if |
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44 there is no such item. |
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45 |
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46 |
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47 .. method:: list.pop([i]) |
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48 :noindex: |
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49 |
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50 Remove the item at the given position in the list, and return it. If no index |
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51 is specified, ``a.pop()`` removes and returns the last item in the list. (The |
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52 square brackets around the *i* in the method signature denote that the parameter |
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53 is optional, not that you should type square brackets at that position. You |
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54 will see this notation frequently in the Python Library Reference.) |
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55 |
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56 |
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57 .. method:: list.index(x) |
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58 :noindex: |
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59 |
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60 Return the index in the list of the first item whose value is *x*. It is an |
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61 error if there is no such item. |
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62 |
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63 |
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64 .. method:: list.count(x) |
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65 :noindex: |
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66 |
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67 Return the number of times *x* appears in the list. |
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68 |
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69 |
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70 .. method:: list.sort() |
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71 :noindex: |
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72 |
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73 Sort the items of the list in place. |
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74 |
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75 |
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76 .. method:: list.reverse() |
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77 :noindex: |
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78 |
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79 Reverse the elements of the list in place. |
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80 |
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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 You might have noticed that methods like ``insert``, ``remove`` or ``sort`` that |
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104 modify the list have no return value printed -- they return ``None``. [1]_ This |
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105 is a design principle for all mutable data structures in Python. |
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106 |
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107 |
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108 .. _tut-lists-as-stacks: |
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109 |
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110 Using Lists as Stacks |
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111 --------------------- |
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112 |
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113 .. sectionauthor:: Ka-Ping Yee <ping@lfw.org> |
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114 |
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115 |
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116 The list methods make it very easy to use a list as a stack, where the last |
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117 element added is the first element retrieved ("last-in, first-out"). To add an |
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118 item to the top of the stack, use :meth:`append`. To retrieve an item from the |
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119 top of the stack, use :meth:`pop` without an explicit index. For example:: |
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120 |
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121 >>> stack = [3, 4, 5] |
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122 >>> stack.append(6) |
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123 >>> stack.append(7) |
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124 >>> stack |
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125 [3, 4, 5, 6, 7] |
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126 >>> stack.pop() |
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127 7 |
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128 >>> stack |
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129 [3, 4, 5, 6] |
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130 >>> stack.pop() |
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131 6 |
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132 >>> stack.pop() |
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133 5 |
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134 >>> stack |
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135 [3, 4] |
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136 |
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137 |
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138 .. _tut-lists-as-queues: |
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139 |
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140 Using Lists as Queues |
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141 --------------------- |
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142 |
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143 .. sectionauthor:: Ka-Ping Yee <ping@lfw.org> |
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144 |
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145 It is also possible to use a list as a queue, where the first element added is |
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146 the first element retrieved ("first-in, first-out"); however, lists are not |
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147 efficient for this purpose. While appends and pops from the end of list are |
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148 fast, doing inserts or pops from the beginning of a list is slow (because all |
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149 of the other elements have to be shifted by one). |
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150 |
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151 To implement a queue, use :class:`collections.deque` which was designed to |
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152 have fast appends and pops from both ends. For example:: |
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153 |
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154 >>> from collections import deque |
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155 >>> queue = deque(["Eric", "John", "Michael"]) |
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156 >>> queue.append("Terry") # Terry arrives |
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157 >>> queue.append("Graham") # Graham arrives |
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158 >>> queue.popleft() # The first to arrive now leaves |
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159 'Eric' |
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160 >>> queue.popleft() # The second to arrive now leaves |
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161 'John' |
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162 >>> queue # Remaining queue in order of arrival |
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163 deque(['Michael', 'Terry', 'Graham']) |
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164 |
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165 |
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166 .. _tut-listcomps: |
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167 |
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168 List Comprehensions |
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169 ------------------- |
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170 |
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171 List comprehensions provide a concise way to create lists from sequences. |
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172 Common applications are to make lists where each element is the result of |
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173 some operations applied to each member of the sequence, or to create a |
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174 subsequence of those elements that satisfy a certain condition. |
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175 |
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176 A list comprehension consists of brackets containing an expression followed |
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177 by a :keyword:`for` clause, then zero or more :keyword:`for` or :keyword:`if` |
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178 clauses. The result will be a list resulting from evaluating the expression in |
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179 the context of the :keyword:`for` and :keyword:`if` clauses which follow it. If |
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180 the expression would evaluate to a tuple, it must be parenthesized. |
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181 |
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182 Here we take a list of numbers and return a list of three times each number:: |
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183 |
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184 >>> vec = [2, 4, 6] |
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185 >>> [3*x for x in vec] |
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186 [6, 12, 18] |
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187 |
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188 Now we get a little fancier:: |
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189 |
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190 >>> [[x, x**2] for x in vec] |
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191 [[2, 4], [4, 16], [6, 36]] |
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192 |
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193 Here we apply a method call to each item in a sequence:: |
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194 |
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195 >>> freshfruit = [' banana', ' loganberry ', 'passion fruit '] |
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196 >>> [weapon.strip() for weapon in freshfruit] |
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197 ['banana', 'loganberry', 'passion fruit'] |
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198 |
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199 Using the :keyword:`if` clause we can filter the stream:: |
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200 |
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201 >>> [3*x for x in vec if x > 3] |
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202 [12, 18] |
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203 >>> [3*x for x in vec if x < 2] |
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204 [] |
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205 |
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206 Tuples can often be created without their parentheses, but not here:: |
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207 |
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208 >>> [x, x**2 for x in vec] # error - parens required for tuples |
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209 File "<stdin>", line 1, in ? |
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210 [x, x**2 for x in vec] |
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211 ^ |
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212 SyntaxError: invalid syntax |
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213 >>> [(x, x**2) for x in vec] |
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214 [(2, 4), (4, 16), (6, 36)] |
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215 |
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216 Here are some nested for loops and other fancy behavior:: |
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217 |
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218 >>> vec1 = [2, 4, 6] |
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219 >>> vec2 = [4, 3, -9] |
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220 >>> [x*y for x in vec1 for y in vec2] |
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221 [8, 6, -18, 16, 12, -36, 24, 18, -54] |
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222 >>> [x+y for x in vec1 for y in vec2] |
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223 [6, 5, -7, 8, 7, -5, 10, 9, -3] |
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224 >>> [vec1[i]*vec2[i] for i in range(len(vec1))] |
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225 [8, 12, -54] |
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226 |
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227 List comprehensions can be applied to complex expressions and nested functions:: |
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228 |
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229 >>> [str(round(355/113, i)) for i in range(1, 6)] |
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230 ['3.1', '3.14', '3.142', '3.1416', '3.14159'] |
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231 |
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232 |
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233 Nested List Comprehensions |
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234 -------------------------- |
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235 |
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236 If you've got the stomach for it, list comprehensions can be nested. They are a |
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237 powerful tool but -- like all powerful tools -- they need to be used carefully, |
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238 if at all. |
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239 |
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240 Consider the following example of a 3x3 matrix held as a list containing three |
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241 lists, one list per row:: |
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242 |
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243 >>> mat = [ |
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244 ... [1, 2, 3], |
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245 ... [4, 5, 6], |
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246 ... [7, 8, 9], |
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247 ... ] |
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248 |
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249 Now, if you wanted to swap rows and columns, you could use a list |
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250 comprehension:: |
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251 |
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252 >>> print([[row[i] for row in mat] for i in [0, 1, 2]]) |
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253 [[1, 4, 7], [2, 5, 8], [3, 6, 9]] |
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254 |
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255 Special care has to be taken for the *nested* list comprehension: |
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256 |
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257 To avoid apprehension when nesting list comprehensions, read from right to |
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258 left. |
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259 |
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260 A more verbose version of this snippet shows the flow explicitly:: |
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261 |
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262 for i in [0, 1, 2]: |
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263 for row in mat: |
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264 print(row[i], end="") |
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265 print() |
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266 |
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267 In real world, you should prefer built-in functions to complex flow statements. |
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268 The :func:`zip` function would do a great job for this use case:: |
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269 |
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270 >>> list(zip(*mat)) |
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271 [(1, 4, 7), (2, 5, 8), (3, 6, 9)] |
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272 |
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273 See :ref:`tut-unpacking-arguments` for details on the asterisk in this line. |
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274 |
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275 .. _tut-del: |
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276 |
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277 The :keyword:`del` statement |
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278 ============================ |
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279 |
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280 There is a way to remove an item from a list given its index instead of its |
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281 value: the :keyword:`del` statement. This differs from the :meth:`pop` method |
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282 which returns a value. The :keyword:`del` statement can also be used to remove |
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283 slices from a list or clear the entire list (which we did earlier by assignment |
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284 of an empty list to the slice). For example:: |
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285 |
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286 >>> a = [-1, 1, 66.25, 333, 333, 1234.5] |
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287 >>> del a[0] |
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288 >>> a |
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289 [1, 66.25, 333, 333, 1234.5] |
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290 >>> del a[2:4] |
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291 >>> a |
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292 [1, 66.25, 1234.5] |
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293 >>> del a[:] |
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294 >>> a |
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295 [] |
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296 |
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297 :keyword:`del` can also be used to delete entire variables:: |
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298 |
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299 >>> del a |
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300 |
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301 Referencing the name ``a`` hereafter is an error (at least until another value |
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302 is assigned to it). We'll find other uses for :keyword:`del` later. |
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303 |
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304 |
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306 |
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307 Tuples and Sequences |
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308 ==================== |
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309 |
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310 We saw that lists and strings have many common properties, such as indexing and |
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311 slicing operations. They are two examples of *sequence* data types (see |
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312 :ref:`typesseq`). Since Python is an evolving language, other sequence data |
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313 types may be added. There is also another standard sequence data type: the |
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314 *tuple*. |
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315 |
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316 A tuple consists of a number of values separated by commas, for instance:: |
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317 |
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318 >>> t = 12345, 54321, 'hello!' |
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319 >>> t[0] |
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320 12345 |
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321 >>> t |
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322 (12345, 54321, 'hello!') |
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323 >>> # Tuples may be nested: |
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324 ... u = t, (1, 2, 3, 4, 5) |
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325 >>> u |
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326 ((12345, 54321, 'hello!'), (1, 2, 3, 4, 5)) |
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327 |
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328 As you see, on output tuples are always enclosed in parentheses, so that nested |
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329 tuples are interpreted correctly; they may be input with or without surrounding |
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330 parentheses, although often parentheses are necessary anyway (if the tuple is |
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331 part of a larger expression). |
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332 |
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333 Tuples have many uses. For example: (x, y) coordinate pairs, employee records |
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334 from a database, etc. Tuples, like strings, are immutable: it is not possible |
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335 to assign to the individual items of a tuple (you can simulate much of the same |
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336 effect with slicing and concatenation, though). It is also possible to create |
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337 tuples which contain mutable objects, such as lists. |
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338 |
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339 A special problem is the construction of tuples containing 0 or 1 items: the |
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340 syntax has some extra quirks to accommodate these. Empty tuples are constructed |
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341 by an empty pair of parentheses; a tuple with one item is constructed by |
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342 following a value with a comma (it is not sufficient to enclose a single value |
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343 in parentheses). Ugly, but effective. For example:: |
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344 |
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345 >>> empty = () |
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346 >>> singleton = 'hello', # <-- note trailing comma |
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347 >>> len(empty) |
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348 0 |
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349 >>> len(singleton) |
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350 1 |
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351 >>> singleton |
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352 ('hello',) |
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353 |
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354 The statement ``t = 12345, 54321, 'hello!'`` is an example of *tuple packing*: |
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355 the values ``12345``, ``54321`` and ``'hello!'`` are packed together in a tuple. |
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356 The reverse operation is also possible:: |
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357 |
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358 >>> x, y, z = t |
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359 |
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360 This is called, appropriately enough, *sequence unpacking* and works for any |
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361 sequence on the right-hand side. Sequence unpacking requires that there are as |
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362 many variables on the left side of the equals sign as there are elements in the |
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363 sequence. Note that multiple assignment is really just a combination of tuple |
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364 packing and sequence unpacking. |
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366 .. XXX Add a bit on the difference between tuples and lists. |
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367 |
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368 |
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369 .. _tut-sets: |
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370 |
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371 Sets |
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372 ==== |
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373 |
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374 Python also includes a data type for *sets*. A set is an unordered collection |
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375 with no duplicate elements. Basic uses include membership testing and |
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376 eliminating duplicate entries. Set objects also support mathematical operations |
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377 like union, intersection, difference, and symmetric difference. |
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378 |
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379 Curly braces or the :func:`set` function can be used to create sets. Note: To |
| 54632 | 380 create an empty set you have to use ``set()``, not ``{}``; the latter creates an |
| 381 empty dictionary, a data structure that we discuss in the next section. | |
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382 |
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383 Here is a brief demonstration:: |
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384 |
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385 >>> basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'} |
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386 >>> print(basket) # show that duplicates have been removed |
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387 {'orange', 'banana', 'pear', 'apple'} |
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388 >>> 'orange' in basket # fast membership testing |
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389 True |
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390 >>> 'crabgrass' in basket |
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391 False |
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392 |
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393 >>> # Demonstrate set operations on unique letters from two words |
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394 ... |
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395 >>> a = set('abracadabra') |
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396 >>> b = set('alacazam') |
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397 >>> a # unique letters in a |
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398 {'a', 'r', 'b', 'c', 'd'} |
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399 >>> a - b # letters in a but not in b |
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400 {'r', 'd', 'b'} |
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401 >>> a | b # letters in either a or b |
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402 {'a', 'c', 'r', 'd', 'b', 'm', 'z', 'l'} |
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403 >>> a & b # letters in both a and b |
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404 {'a', 'c'} |
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405 >>> a ^ b # letters in a or b but not both |
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406 {'r', 'd', 'b', 'm', 'z', 'l'} |
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407 |
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408 Like :ref:`for lists <tut-listcomps>`, there is a set comprehension syntax:: |
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409 |
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410 >>> a = {x for x in 'abracadabra' if x not in 'abc'} |
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411 >>> a |
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412 {'r', 'd'} |
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413 |
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414 |
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415 |
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416 .. _tut-dictionaries: |
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417 |
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418 Dictionaries |
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419 ============ |
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420 |
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421 Another useful data type built into Python is the *dictionary* (see |
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422 :ref:`typesmapping`). Dictionaries are sometimes found in other languages as |
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423 "associative memories" or "associative arrays". Unlike sequences, which are |
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424 indexed by a range of numbers, dictionaries are indexed by *keys*, which can be |
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425 any immutable type; strings and numbers can always be keys. Tuples can be used |
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426 as keys if they contain only strings, numbers, or tuples; if a tuple contains |
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427 any mutable object either directly or indirectly, it cannot be used as a key. |
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428 You can't use lists as keys, since lists can be modified in place using index |
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429 assignments, slice assignments, or methods like :meth:`append` and |
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430 :meth:`extend`. |
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431 |
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432 It is best to think of a dictionary as an unordered set of *key: value* pairs, |
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433 with the requirement that the keys are unique (within one dictionary). A pair of |
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434 braces creates an empty dictionary: ``{}``. Placing a comma-separated list of |
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435 key:value pairs within the braces adds initial key:value pairs to the |
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436 dictionary; this is also the way dictionaries are written on output. |
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437 |
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438 The main operations on a dictionary are storing a value with some key and |
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439 extracting the value given the key. It is also possible to delete a key:value |
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440 pair with ``del``. If you store using a key that is already in use, the old |
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441 value associated with that key is forgotten. It is an error to extract a value |
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442 using a non-existent key. |
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443 |
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444 Performing ``list(d.keys())`` on a dictionary returns a list of all the keys |
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445 used in the dictionary, in arbitrary order (if you want it sorted, just use |
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446 ``sorted(d.keys())`` instead). [2]_ To check whether a single key is in the |
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447 dictionary, use the :keyword:`in` keyword. |
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448 |
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449 Here is a small example using a dictionary:: |
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450 |
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451 >>> tel = {'jack': 4098, 'sape': 4139} |
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452 >>> tel['guido'] = 4127 |
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453 >>> tel |
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454 {'sape': 4139, 'guido': 4127, 'jack': 4098} |
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455 >>> tel['jack'] |
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456 4098 |
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457 >>> del tel['sape'] |
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458 >>> tel['irv'] = 4127 |
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459 >>> tel |
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460 {'guido': 4127, 'irv': 4127, 'jack': 4098} |
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461 >>> list(tel.keys()) |
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462 ['irv', 'guido', 'jack'] |
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463 >>> sorted(tel.keys()) |
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464 ['guido', 'irv', 'jack'] |
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465 >>> 'guido' in tel |
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466 True |
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467 >>> 'jack' not in tel |
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468 False |
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469 |
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470 The :func:`dict` constructor builds dictionaries directly from sequences of |
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471 key-value pairs:: |
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472 |
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473 >>> dict([('sape', 4139), ('guido', 4127), ('jack', 4098)]) |
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474 {'sape': 4139, 'jack': 4098, 'guido': 4127} |
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475 |
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476 In addition, dict comprehensions can be used to create dictionaries from |
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477 arbitrary key and value expressions:: |
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478 |
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479 >>> {x: x**2 for x in (2, 4, 6)} |
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480 {2: 4, 4: 16, 6: 36} |
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481 |
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482 When the keys are simple strings, it is sometimes easier to specify pairs using |
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483 keyword arguments:: |
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484 |
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485 >>> dict(sape=4139, guido=4127, jack=4098) |
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486 {'sape': 4139, 'jack': 4098, 'guido': 4127} |
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487 |
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488 |
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489 .. _tut-loopidioms: |
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490 |
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491 Looping Techniques |
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492 ================== |
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493 |
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494 When looping through dictionaries, the key and corresponding value can be |
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495 retrieved at the same time using the :meth:`items` method. :: |
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496 |
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497 >>> knights = {'gallahad': 'the pure', 'robin': 'the brave'} |
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498 >>> for k, v in knights.items(): |
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499 ... print(k, v) |
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500 ... |
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501 gallahad the pure |
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502 robin the brave |
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503 |
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504 When looping through a sequence, the position index and corresponding value can |
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505 be retrieved at the same time using the :func:`enumerate` function. :: |
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506 |
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507 >>> for i, v in enumerate(['tic', 'tac', 'toe']): |
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508 ... print(i, v) |
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509 ... |
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510 0 tic |
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511 1 tac |
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512 2 toe |
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513 |
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514 To loop over two or more sequences at the same time, the entries can be paired |
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515 with the :func:`zip` function. :: |
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516 |
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517 >>> questions = ['name', 'quest', 'favorite color'] |
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518 >>> answers = ['lancelot', 'the holy grail', 'blue'] |
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519 >>> for q, a in zip(questions, answers): |
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520 ... print('What is your {0}? It is {1}.'.format(q, a)) |
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521 ... |
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522 What is your name? It is lancelot. |
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523 What is your quest? It is the holy grail. |
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524 What is your favorite color? It is blue. |
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525 |
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526 To loop over a sequence in reverse, first specify the sequence in a forward |
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527 direction and then call the :func:`reversed` function. :: |
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528 |
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529 >>> for i in reversed(range(1, 10, 2)): |
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530 ... print(i) |
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531 ... |
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532 9 |
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533 7 |
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534 5 |
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535 3 |
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536 1 |
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537 |
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538 To loop over a sequence in sorted order, use the :func:`sorted` function which |
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539 returns a new sorted list while leaving the source unaltered. :: |
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540 |
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541 >>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana'] |
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542 >>> for f in sorted(set(basket)): |
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543 ... print(f) |
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544 ... |
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545 apple |
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546 banana |
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547 orange |
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548 pear |
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549 |
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550 |
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551 .. _tut-conditions: |
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552 |
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553 More on Conditions |
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554 ================== |
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555 |
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556 The conditions used in ``while`` and ``if`` statements can contain any |
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557 operators, not just comparisons. |
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558 |
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559 The comparison operators ``in`` and ``not in`` check whether a value occurs |
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560 (does not occur) in a sequence. The operators ``is`` and ``is not`` compare |
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561 whether two objects are really the same object; this only matters for mutable |
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562 objects like lists. All comparison operators have the same priority, which is |
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563 lower than that of all numerical operators. |
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564 |
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565 Comparisons can be chained. For example, ``a < b == c`` tests whether ``a`` is |
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566 less than ``b`` and moreover ``b`` equals ``c``. |
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567 |
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568 Comparisons may be combined using the Boolean operators ``and`` and ``or``, and |
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569 the outcome of a comparison (or of any other Boolean expression) may be negated |
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570 with ``not``. These have lower priorities than comparison operators; between |
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571 them, ``not`` has the highest priority and ``or`` the lowest, so that ``A and |
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572 not B or C`` is equivalent to ``(A and (not B)) or C``. As always, parentheses |
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573 can be used to express the desired composition. |
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574 |
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575 The Boolean operators ``and`` and ``or`` are so-called *short-circuit* |
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576 operators: their arguments are evaluated from left to right, and evaluation |
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577 stops as soon as the outcome is determined. For example, if ``A`` and ``C`` are |
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578 true but ``B`` is false, ``A and B and C`` does not evaluate the expression |
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579 ``C``. When used as a general value and not as a Boolean, the return value of a |
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580 short-circuit operator is the last evaluated argument. |
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581 |
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582 It is possible to assign the result of a comparison or other Boolean expression |
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583 to a variable. For example, :: |
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584 |
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585 >>> string1, string2, string3 = '', 'Trondheim', 'Hammer Dance' |
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586 >>> non_null = string1 or string2 or string3 |
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587 >>> non_null |
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588 'Trondheim' |
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589 |
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590 Note that in Python, unlike C, assignment cannot occur inside expressions. C |
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591 programmers may grumble about this, but it avoids a common class of problems |
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592 encountered in C programs: typing ``=`` in an expression when ``==`` was |
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593 intended. |
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594 |
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595 |
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596 .. _tut-comparing: |
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597 |
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598 Comparing Sequences and Other Types |
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599 =================================== |
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600 |
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601 Sequence objects may be compared to other objects with the same sequence type. |
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602 The comparison uses *lexicographical* ordering: first the first two items are |
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603 compared, and if they differ this determines the outcome of the comparison; if |
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604 they are equal, the next two items are compared, and so on, until either |
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605 sequence is exhausted. If two items to be compared are themselves sequences of |
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606 the same type, the lexicographical comparison is carried out recursively. If |
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607 all items of two sequences compare equal, the sequences are considered equal. |
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608 If one sequence is an initial sub-sequence of the other, the shorter sequence is |
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609 the smaller (lesser) one. Lexicographical ordering for strings uses the Unicode |
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610 codepoint number to order individual characters. Some examples of comparisons |
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611 between sequences of the same type:: |
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612 |
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613 (1, 2, 3) < (1, 2, 4) |
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614 [1, 2, 3] < [1, 2, 4] |
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615 'ABC' < 'C' < 'Pascal' < 'Python' |
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616 (1, 2, 3, 4) < (1, 2, 4) |
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617 (1, 2) < (1, 2, -1) |
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618 (1, 2, 3) == (1.0, 2.0, 3.0) |
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619 (1, 2, ('aa', 'ab')) < (1, 2, ('abc', 'a'), 4) |
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620 |
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621 Note that comparing objects of different types with ``<`` or ``>`` is legal |
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622 provided that the objects have appropriate comparison methods. For example, |
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623 mixed numeric types are compared according to their numeric value, so 0 equals |
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624 0.0, etc. Otherwise, rather than providing an arbitrary ordering, the |
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625 interpreter will raise a :exc:`TypeError` exception. |
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626 |
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627 |
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628 .. rubric:: Footnotes |
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629 |
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630 .. [1] Other languages may return the mutated object, which allows method |
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631 chaining, such as ``d->insert("a")->remove("b")->sort();``. |
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632 |
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633 .. [2] Calling ``d.keys()`` will return a :dfn:`dictionary view` object. It |
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634 supports operations like membership test and iteration, but its contents |
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635 are not independent of the original dictionary -- it is only a *view*. |
