Mercurial > cpython-lru_cache-weakref
annotate Doc/tutorial/datastructures.rst @ 43744:c19b6849411c legacy-trunk
false "fix" undone as correct problem was found and fixed.
| author | Gregory P. Smith <greg@mad-scientist.com> |
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| date | Thu, 01 Nov 2007 21:17:47 +0000 |
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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 |
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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 |
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27 Extend the list by appending all the items in the given list; equivalent to |
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28 ``a[len(a):] = L``. |
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29 |
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30 |
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31 .. method:: list.insert(i, x) |
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32 |
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33 Insert an item at a given position. The first argument is the index of the |
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34 element before which to insert, so ``a.insert(0, x)`` inserts at the front of |
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35 the list, and ``a.insert(len(a), x)`` is equivalent to ``a.append(x)``. |
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36 |
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37 |
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38 .. method:: list.remove(x) |
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39 |
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40 Remove the first item from the list whose value is *x*. It is an error if there |
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41 is no such item. |
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42 |
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43 |
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44 .. method:: list.pop([i]) |
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45 |
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46 Remove the item at the given position in the list, and return it. If no index |
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47 is specified, ``a.pop()`` removes and returns the last item in the list. (The |
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48 square brackets around the *i* in the method signature denote that the parameter |
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49 is optional, not that you should type square brackets at that position. You |
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50 will see this notation frequently in the Python Library Reference.) |
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51 |
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52 |
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53 .. method:: list.index(x) |
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54 |
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55 Return the index in the list of the first item whose value is *x*. It is an |
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56 error if there is no such item. |
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57 |
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58 |
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59 .. method:: list.count(x) |
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60 |
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61 Return the number of times *x* appears in the list. |
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62 |
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63 |
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64 .. method:: list.sort() |
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65 |
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66 Sort the items of the list, in place. |
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67 |
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68 |
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69 .. method:: list.reverse() |
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70 |
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71 Reverse the elements of the list, in place. |
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72 |
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73 An example that uses most of the list methods:: |
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74 |
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75 >>> a = [66.25, 333, 333, 1, 1234.5] |
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76 >>> print a.count(333), a.count(66.25), a.count('x') |
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77 2 1 0 |
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78 >>> a.insert(2, -1) |
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79 >>> a.append(333) |
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80 >>> a |
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81 [66.25, 333, -1, 333, 1, 1234.5, 333] |
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82 >>> a.index(333) |
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83 1 |
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84 >>> a.remove(333) |
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85 >>> a |
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86 [66.25, -1, 333, 1, 1234.5, 333] |
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87 >>> a.reverse() |
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88 >>> a |
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89 [333, 1234.5, 1, 333, -1, 66.25] |
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90 >>> a.sort() |
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91 >>> a |
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92 [-1, 1, 66.25, 333, 333, 1234.5] |
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93 |
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94 |
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95 .. _tut-lists-as-stacks: |
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96 |
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97 Using Lists as Stacks |
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98 --------------------- |
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99 |
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100 .. sectionauthor:: Ka-Ping Yee <ping@lfw.org> |
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101 |
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102 |
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103 The list methods make it very easy to use a list as a stack, where the last |
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104 element added is the first element retrieved ("last-in, first-out"). To add an |
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105 item to the top of the stack, use :meth:`append`. To retrieve an item from the |
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106 top of the stack, use :meth:`pop` without an explicit index. For example:: |
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107 |
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108 >>> stack = [3, 4, 5] |
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109 >>> stack.append(6) |
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110 >>> stack.append(7) |
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111 >>> stack |
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112 [3, 4, 5, 6, 7] |
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113 >>> stack.pop() |
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114 7 |
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115 >>> stack |
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116 [3, 4, 5, 6] |
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117 >>> stack.pop() |
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118 6 |
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119 >>> stack.pop() |
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120 5 |
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121 >>> stack |
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122 [3, 4] |
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123 |
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124 |
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125 .. _tut-lists-as-queues: |
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126 |
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127 Using Lists as Queues |
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128 --------------------- |
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129 |
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130 .. sectionauthor:: Ka-Ping Yee <ping@lfw.org> |
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131 |
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132 |
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133 You can also use a list conveniently as a queue, where the first element added |
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134 is the first element retrieved ("first-in, first-out"). To add an item to the |
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135 back of the queue, use :meth:`append`. To retrieve an item from the front of |
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136 the queue, use :meth:`pop` with ``0`` as the index. For example:: |
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137 |
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138 >>> queue = ["Eric", "John", "Michael"] |
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139 >>> queue.append("Terry") # Terry arrives |
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140 >>> queue.append("Graham") # Graham arrives |
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141 >>> queue.pop(0) |
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142 'Eric' |
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143 >>> queue.pop(0) |
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144 'John' |
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145 >>> queue |
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146 ['Michael', 'Terry', 'Graham'] |
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147 |
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148 |
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149 .. _tut-functional: |
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150 |
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151 Functional Programming Tools |
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152 ---------------------------- |
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153 |
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154 There are three built-in functions that are very useful when used with lists: |
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155 :func:`filter`, :func:`map`, and :func:`reduce`. |
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156 |
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157 ``filter(function, sequence)`` returns a sequence consisting of those items from |
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158 the sequence for which ``function(item)`` is true. If *sequence* is a |
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159 :class:`string` or :class:`tuple`, the result will be of the same type; |
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160 otherwise, it is always a :class:`list`. For example, to compute some primes:: |
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161 |
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162 >>> def f(x): return x % 2 != 0 and x % 3 != 0 |
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163 ... |
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164 >>> filter(f, range(2, 25)) |
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165 [5, 7, 11, 13, 17, 19, 23] |
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166 |
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167 ``map(function, sequence)`` calls ``function(item)`` for each of the sequence's |
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168 items and returns a list of the return values. For example, to compute some |
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169 cubes:: |
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170 |
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171 >>> def cube(x): return x*x*x |
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172 ... |
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173 >>> map(cube, range(1, 11)) |
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174 [1, 8, 27, 64, 125, 216, 343, 512, 729, 1000] |
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175 |
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176 More than one sequence may be passed; the function must then have as many |
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177 arguments as there are sequences and is called with the corresponding item from |
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178 each sequence (or ``None`` if some sequence is shorter than another). For |
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179 example:: |
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180 |
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181 >>> seq = range(8) |
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182 >>> def add(x, y): return x+y |
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183 ... |
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184 >>> map(add, seq, seq) |
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185 [0, 2, 4, 6, 8, 10, 12, 14] |
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186 |
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187 ``reduce(function, sequence)`` returns a single value constructed by calling the |
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188 binary function *function* on the first two items of the sequence, then on the |
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189 result and the next item, and so on. For example, to compute the sum of the |
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190 numbers 1 through 10:: |
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191 |
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192 >>> def add(x,y): return x+y |
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193 ... |
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194 >>> reduce(add, range(1, 11)) |
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195 55 |
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196 |
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197 If there's only one item in the sequence, its value is returned; if the sequence |
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198 is empty, an exception is raised. |
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199 |
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200 A third argument can be passed to indicate the starting value. In this case the |
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201 starting value is returned for an empty sequence, and the function is first |
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202 applied to the starting value and the first sequence item, then to the result |
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203 and the next item, and so on. For example, :: |
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204 |
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205 >>> def sum(seq): |
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206 ... def add(x,y): return x+y |
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207 ... return reduce(add, seq, 0) |
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208 ... |
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209 >>> sum(range(1, 11)) |
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210 55 |
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211 >>> sum([]) |
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212 0 |
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213 |
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214 Don't use this example's definition of :func:`sum`: since summing numbers is |
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215 such a common need, a built-in function ``sum(sequence)`` is already provided, |
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216 and works exactly like this. |
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217 |
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218 .. versionadded:: 2.3 |
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219 |
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220 |
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221 List Comprehensions |
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222 ------------------- |
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223 |
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224 List comprehensions provide a concise way to create lists without resorting to |
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225 use of :func:`map`, :func:`filter` and/or :keyword:`lambda`. The resulting list |
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226 definition tends often to be clearer than lists built using those constructs. |
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227 Each list comprehension consists of an expression followed by a :keyword:`for` |
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228 clause, then zero or more :keyword:`for` or :keyword:`if` clauses. The result |
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229 will be a list resulting from evaluating the expression in the context of the |
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230 :keyword:`for` and :keyword:`if` clauses which follow it. If the expression |
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231 would evaluate to a tuple, it must be parenthesized. :: |
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232 |
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233 >>> freshfruit = [' banana', ' loganberry ', 'passion fruit '] |
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234 >>> [weapon.strip() for weapon in freshfruit] |
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235 ['banana', 'loganberry', 'passion fruit'] |
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236 >>> vec = [2, 4, 6] |
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237 >>> [3*x for x in vec] |
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238 [6, 12, 18] |
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239 >>> [3*x for x in vec if x > 3] |
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240 [12, 18] |
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241 >>> [3*x for x in vec if x < 2] |
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242 [] |
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243 >>> [[x,x**2] for x in vec] |
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244 [[2, 4], [4, 16], [6, 36]] |
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245 >>> [x, x**2 for x in vec] # error - parens required for tuples |
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246 File "<stdin>", line 1, in ? |
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247 [x, x**2 for x in vec] |
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248 ^ |
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249 SyntaxError: invalid syntax |
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250 >>> [(x, x**2) for x in vec] |
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251 [(2, 4), (4, 16), (6, 36)] |
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252 >>> vec1 = [2, 4, 6] |
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253 >>> vec2 = [4, 3, -9] |
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254 >>> [x*y for x in vec1 for y in vec2] |
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255 [8, 6, -18, 16, 12, -36, 24, 18, -54] |
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256 >>> [x+y for x in vec1 for y in vec2] |
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257 [6, 5, -7, 8, 7, -5, 10, 9, -3] |
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258 >>> [vec1[i]*vec2[i] for i in range(len(vec1))] |
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259 [8, 12, -54] |
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260 |
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261 List comprehensions are much more flexible than :func:`map` and can be applied |
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262 to complex expressions and nested functions:: |
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263 |
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264 >>> [str(round(355/113.0, i)) for i in range(1,6)] |
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265 ['3.1', '3.14', '3.142', '3.1416', '3.14159'] |
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266 |
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267 |
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268 .. _tut-del: |
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269 |
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270 The :keyword:`del` statement |
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271 ============================ |
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272 |
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273 There is a way to remove an item from a list given its index instead of its |
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274 value: the :keyword:`del` statement. This differs from the :meth:`pop` method |
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275 which returns a value. The :keyword:`del` statement can also be used to remove |
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276 slices from a list or clear the entire list (which we did earlier by assignment |
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277 of an empty list to the slice). For example:: |
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278 |
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279 >>> a = [-1, 1, 66.25, 333, 333, 1234.5] |
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280 >>> del a[0] |
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281 >>> a |
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282 [1, 66.25, 333, 333, 1234.5] |
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283 >>> del a[2:4] |
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284 >>> a |
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285 [1, 66.25, 1234.5] |
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286 >>> del a[:] |
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287 >>> a |
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288 [] |
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289 |
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290 :keyword:`del` can also be used to delete entire variables:: |
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291 |
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292 >>> del a |
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293 |
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294 Referencing the name ``a`` hereafter is an error (at least until another value |
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295 is assigned to it). We'll find other uses for :keyword:`del` later. |
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296 |
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297 |
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298 .. _tut-tuples: |
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299 |
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300 Tuples and Sequences |
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301 ==================== |
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302 |
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303 We saw that lists and strings have many common properties, such as indexing and |
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304 slicing operations. They are two examples of *sequence* data types (see |
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305 :ref:`typesseq`). Since Python is an evolving language, other sequence data |
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306 types may be added. There is also another standard sequence data type: the |
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307 *tuple*. |
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308 |
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309 A tuple consists of a number of values separated by commas, for instance:: |
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310 |
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311 >>> t = 12345, 54321, 'hello!' |
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312 >>> t[0] |
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313 12345 |
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314 >>> t |
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315 (12345, 54321, 'hello!') |
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316 >>> # Tuples may be nested: |
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317 ... u = t, (1, 2, 3, 4, 5) |
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318 >>> u |
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319 ((12345, 54321, 'hello!'), (1, 2, 3, 4, 5)) |
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320 |
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321 As you see, on output tuples are always enclosed in parentheses, so that nested |
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322 tuples are interpreted correctly; they may be input with or without surrounding |
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323 parentheses, although often parentheses are necessary anyway (if the tuple is |
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324 part of a larger expression). |
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325 |
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326 Tuples have many uses. For example: (x, y) coordinate pairs, employee records |
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327 from a database, etc. Tuples, like strings, are immutable: it is not possible |
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328 to assign to the individual items of a tuple (you can simulate much of the same |
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329 effect with slicing and concatenation, though). It is also possible to create |
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330 tuples which contain mutable objects, such as lists. |
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331 |
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332 A special problem is the construction of tuples containing 0 or 1 items: the |
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333 syntax has some extra quirks to accommodate these. Empty tuples are constructed |
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334 by an empty pair of parentheses; a tuple with one item is constructed by |
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335 following a value with a comma (it is not sufficient to enclose a single value |
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336 in parentheses). Ugly, but effective. For example:: |
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337 |
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338 >>> empty = () |
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339 >>> singleton = 'hello', # <-- note trailing comma |
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340 >>> len(empty) |
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341 0 |
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342 >>> len(singleton) |
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343 1 |
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344 >>> singleton |
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345 ('hello',) |
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346 |
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347 The statement ``t = 12345, 54321, 'hello!'`` is an example of *tuple packing*: |
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348 the values ``12345``, ``54321`` and ``'hello!'`` are packed together in a tuple. |
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349 The reverse operation is also possible:: |
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350 |
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351 >>> x, y, z = t |
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352 |
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353 This is called, appropriately enough, *sequence unpacking*. Sequence unpacking |
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354 requires the list of variables on the left to have the same number of elements |
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355 as the length of the sequence. Note that multiple assignment is really just a |
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356 combination of tuple packing and sequence unpacking! |
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357 |
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358 There is a small bit of asymmetry here: packing multiple values always creates |
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359 a tuple, and unpacking works for any sequence. |
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360 |
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361 .. % XXX Add a bit on the difference between tuples and lists. |
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362 |
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363 |
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364 .. _tut-sets: |
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365 |
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366 Sets |
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367 ==== |
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368 |
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369 Python also includes a data type for *sets*. A set is an unordered collection |
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370 with no duplicate elements. Basic uses include membership testing and |
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371 eliminating duplicate entries. Set objects also support mathematical operations |
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372 like union, intersection, difference, and symmetric difference. |
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373 |
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374 Here is a brief demonstration:: |
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375 |
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376 >>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana'] |
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377 >>> fruit = set(basket) # create a set without duplicates |
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378 >>> fruit |
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379 set(['orange', 'pear', 'apple', 'banana']) |
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380 >>> 'orange' in fruit # fast membership testing |
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381 True |
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382 >>> 'crabgrass' in fruit |
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383 False |
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384 |
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385 >>> # Demonstrate set operations on unique letters from two words |
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386 ... |
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387 >>> a = set('abracadabra') |
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388 >>> b = set('alacazam') |
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389 >>> a # unique letters in a |
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390 set(['a', 'r', 'b', 'c', 'd']) |
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391 >>> a - b # letters in a but not in b |
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392 set(['r', 'd', 'b']) |
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393 >>> a | b # letters in either a or b |
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394 set(['a', 'c', 'r', 'd', 'b', 'm', 'z', 'l']) |
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395 >>> a & b # letters in both a and b |
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396 set(['a', 'c']) |
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397 >>> a ^ b # letters in a or b but not both |
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398 set(['r', 'd', 'b', 'm', 'z', 'l']) |
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399 |
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400 |
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401 .. _tut-dictionaries: |
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402 |
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403 Dictionaries |
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404 ============ |
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405 |
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406 Another useful data type built into Python is the *dictionary* (see |
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407 :ref:`typesmapping`). Dictionaries are sometimes found in other languages as |
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408 "associative memories" or "associative arrays". Unlike sequences, which are |
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409 indexed by a range of numbers, dictionaries are indexed by *keys*, which can be |
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410 any immutable type; strings and numbers can always be keys. Tuples can be used |
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411 as keys if they contain only strings, numbers, or tuples; if a tuple contains |
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412 any mutable object either directly or indirectly, it cannot be used as a key. |
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413 You can't use lists as keys, since lists can be modified in place using index |
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414 assignments, slice assignments, or methods like :meth:`append` and |
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415 :meth:`extend`. |
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416 |
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417 It is best to think of a dictionary as an unordered set of *key: value* pairs, |
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418 with the requirement that the keys are unique (within one dictionary). A pair of |
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419 braces creates an empty dictionary: ``{}``. Placing a comma-separated list of |
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420 key:value pairs within the braces adds initial key:value pairs to the |
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421 dictionary; this is also the way dictionaries are written on output. |
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422 |
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423 The main operations on a dictionary are storing a value with some key and |
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424 extracting the value given the key. It is also possible to delete a key:value |
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425 pair with ``del``. If you store using a key that is already in use, the old |
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426 value associated with that key is forgotten. It is an error to extract a value |
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427 using a non-existent key. |
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428 |
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429 The :meth:`keys` method of a dictionary object returns a list of all the keys |
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430 used in the dictionary, in arbitrary order (if you want it sorted, just apply |
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431 the :meth:`sort` method to the list of keys). To check whether a single key is |
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432 in the dictionary, either use the dictionary's :meth:`has_key` method or the |
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433 :keyword:`in` keyword. |
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434 |
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435 Here is a small example using a dictionary:: |
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436 |
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437 >>> tel = {'jack': 4098, 'sape': 4139} |
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438 >>> tel['guido'] = 4127 |
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439 >>> tel |
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440 {'sape': 4139, 'guido': 4127, 'jack': 4098} |
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441 >>> tel['jack'] |
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442 4098 |
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443 >>> del tel['sape'] |
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444 >>> tel['irv'] = 4127 |
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445 >>> tel |
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446 {'guido': 4127, 'irv': 4127, 'jack': 4098} |
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447 >>> tel.keys() |
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448 ['guido', 'irv', 'jack'] |
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449 >>> tel.has_key('guido') |
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450 True |
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451 >>> 'guido' in tel |
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452 True |
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453 |
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454 The :func:`dict` constructor builds dictionaries directly from lists of |
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455 key-value pairs stored as tuples. When the pairs form a pattern, list |
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456 comprehensions can compactly specify the key-value list. :: |
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457 |
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458 >>> dict([('sape', 4139), ('guido', 4127), ('jack', 4098)]) |
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459 {'sape': 4139, 'jack': 4098, 'guido': 4127} |
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460 >>> dict([(x, x**2) for x in (2, 4, 6)]) # use a list comprehension |
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461 {2: 4, 4: 16, 6: 36} |
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462 |
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463 Later in the tutorial, we will learn about Generator Expressions which are even |
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464 better suited for the task of supplying key-values pairs to the :func:`dict` |
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465 constructor. |
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466 |
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467 When the keys are simple strings, it is sometimes easier to specify pairs using |
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468 keyword arguments:: |
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469 |
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470 >>> dict(sape=4139, guido=4127, jack=4098) |
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471 {'sape': 4139, 'jack': 4098, 'guido': 4127} |
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472 |
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473 |
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474 .. _tut-loopidioms: |
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475 |
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476 Looping Techniques |
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477 ================== |
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478 |
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479 When looping through dictionaries, the key and corresponding value can be |
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480 retrieved at the same time using the :meth:`iteritems` method. :: |
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481 |
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482 >>> knights = {'gallahad': 'the pure', 'robin': 'the brave'} |
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483 >>> for k, v in knights.iteritems(): |
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484 ... print k, v |
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485 ... |
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486 gallahad the pure |
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487 robin the brave |
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488 |
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489 When looping through a sequence, the position index and corresponding value can |
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490 be retrieved at the same time using the :func:`enumerate` function. :: |
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491 |
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492 >>> for i, v in enumerate(['tic', 'tac', 'toe']): |
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493 ... print i, v |
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494 ... |
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495 0 tic |
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496 1 tac |
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497 2 toe |
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498 |
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499 To loop over two or more sequences at the same time, the entries can be paired |
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500 with the :func:`zip` function. :: |
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501 |
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502 >>> questions = ['name', 'quest', 'favorite color'] |
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503 >>> answers = ['lancelot', 'the holy grail', 'blue'] |
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504 >>> for q, a in zip(questions, answers): |
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505 ... print 'What is your %s? It is %s.' % (q, a) |
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506 ... |
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507 What is your name? It is lancelot. |
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508 What is your quest? It is the holy grail. |
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509 What is your favorite color? It is blue. |
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510 |
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511 To loop over a sequence in reverse, first specify the sequence in a forward |
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512 direction and then call the :func:`reversed` function. :: |
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513 |
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514 >>> for i in reversed(xrange(1,10,2)): |
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515 ... print i |
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516 ... |
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517 9 |
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518 7 |
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519 5 |
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520 3 |
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521 1 |
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522 |
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523 To loop over a sequence in sorted order, use the :func:`sorted` function which |
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524 returns a new sorted list while leaving the source unaltered. :: |
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525 |
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526 >>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana'] |
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527 >>> for f in sorted(set(basket)): |
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528 ... print f |
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529 ... |
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530 apple |
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531 banana |
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532 orange |
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533 pear |
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534 |
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535 |
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536 .. _tut-conditions: |
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537 |
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538 More on Conditions |
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539 ================== |
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540 |
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541 The conditions used in ``while`` and ``if`` statements can contain any |
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542 operators, not just comparisons. |
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543 |
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544 The comparison operators ``in`` and ``not in`` check whether a value occurs |
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545 (does not occur) in a sequence. The operators ``is`` and ``is not`` compare |
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546 whether two objects are really the same object; this only matters for mutable |
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547 objects like lists. All comparison operators have the same priority, which is |
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548 lower than that of all numerical operators. |
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549 |
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550 Comparisons can be chained. For example, ``a < b == c`` tests whether ``a`` is |
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551 less than ``b`` and moreover ``b`` equals ``c``. |
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552 |
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553 Comparisons may be combined using the Boolean operators ``and`` and ``or``, and |
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554 the outcome of a comparison (or of any other Boolean expression) may be negated |
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555 with ``not``. These have lower priorities than comparison operators; between |
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556 them, ``not`` has the highest priority and ``or`` the lowest, so that ``A and |
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557 not B or C`` is equivalent to ``(A and (not B)) or C``. As always, parentheses |
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558 can be used to express the desired composition. |
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559 |
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560 The Boolean operators ``and`` and ``or`` are so-called *short-circuit* |
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561 operators: their arguments are evaluated from left to right, and evaluation |
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562 stops as soon as the outcome is determined. For example, if ``A`` and ``C`` are |
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563 true but ``B`` is false, ``A and B and C`` does not evaluate the expression |
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564 ``C``. When used as a general value and not as a Boolean, the return value of a |
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565 short-circuit operator is the last evaluated argument. |
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566 |
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567 It is possible to assign the result of a comparison or other Boolean expression |
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568 to a variable. For example, :: |
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569 |
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570 >>> string1, string2, string3 = '', 'Trondheim', 'Hammer Dance' |
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571 >>> non_null = string1 or string2 or string3 |
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572 >>> non_null |
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573 'Trondheim' |
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574 |
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575 Note that in Python, unlike C, assignment cannot occur inside expressions. C |
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576 programmers may grumble about this, but it avoids a common class of problems |
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577 encountered in C programs: typing ``=`` in an expression when ``==`` was |
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578 intended. |
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579 |
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580 |
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581 .. _tut-comparing: |
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582 |
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583 Comparing Sequences and Other Types |
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584 =================================== |
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585 |
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586 Sequence objects may be compared to other objects with the same sequence type. |
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587 The comparison uses *lexicographical* ordering: first the first two items are |
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588 compared, and if they differ this determines the outcome of the comparison; if |
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589 they are equal, the next two items are compared, and so on, until either |
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590 sequence is exhausted. If two items to be compared are themselves sequences of |
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591 the same type, the lexicographical comparison is carried out recursively. If |
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592 all items of two sequences compare equal, the sequences are considered equal. |
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593 If one sequence is an initial sub-sequence of the other, the shorter sequence is |
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594 the smaller (lesser) one. Lexicographical ordering for strings uses the ASCII |
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595 ordering for individual characters. Some examples of comparisons between |
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596 sequences of the same type:: |
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597 |
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598 (1, 2, 3) < (1, 2, 4) |
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599 [1, 2, 3] < [1, 2, 4] |
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600 'ABC' < 'C' < 'Pascal' < 'Python' |
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601 (1, 2, 3, 4) < (1, 2, 4) |
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602 (1, 2) < (1, 2, -1) |
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603 (1, 2, 3) == (1.0, 2.0, 3.0) |
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604 (1, 2, ('aa', 'ab')) < (1, 2, ('abc', 'a'), 4) |
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605 |
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606 Note that comparing objects of different types is legal. The outcome is |
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607 deterministic but arbitrary: the types are ordered by their name. Thus, a list |
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608 is always smaller than a string, a string is always smaller than a tuple, etc. |
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609 [#]_ Mixed numeric types are compared according to their numeric value, so 0 |
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610 equals 0.0, etc. |
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611 |
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612 |
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613 .. rubric:: Footnotes |
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614 |
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615 .. [#] The rules for comparing objects of different types should not be relied upon; |
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616 they may change in a future version of the language. |
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617 |
