回答編集履歴
3
追記
test
CHANGED
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@@ -151,3 +151,23 @@
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151
151
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257 µs ± 19.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
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152
152
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153
153
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```
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154
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+
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155
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+
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156
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+
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157
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+
追記
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158
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+
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159
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+
**長い**dequeから、**長い**部分要素を取り出すならisliceの方が有利です。（popを繰り返す操作がネックになりますから）
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160
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+
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161
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+
```plain
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162
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+
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163
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+
In : %timeit y = list(islice(x, 700000, 100000))
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164
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165
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2.21 ms ± 24.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
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166
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+
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167
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+
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168
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169
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In : %timeit slice_deque(x, 700000, 100000)
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170
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+
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171
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9.72 ms ± 48.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
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172
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+
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173
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```
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2
スライスに関して
test
CHANGED
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@@ -87,3 +87,67 @@
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87
87
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5.93 µs ± 636 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
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88
88
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89
89
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```
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90
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+
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91
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92
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+
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93
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----
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94
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95
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+
追記
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96
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97
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98
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99
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+
[https://docs.python.org/ja/3/library/collections.html#deque-recipes](https://docs.python.org/ja/3/library/collections.html#deque-recipes)
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100
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+
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101
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+
> To implement deque slicing, use a similar approach applying rotate() to bring a target element to the left side of the deque. Remove old entries with popleft(), add new entries with extend(), and then reverse the rotation. With minor variations on that approach, it is easy to implement Forth style stack manipulations such as dup, drop, swap, over, pick, rot, and roll.
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102
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+
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103
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104
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105
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```python
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106
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107
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def slice_deque(d, i, n):
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108
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109
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d.rotate(-i)
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110
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111
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t = [d.popleft() for _i in range(n)]
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112
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113
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d.extend(t)
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114
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115
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d.rotate(i+n)
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116
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117
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return t
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118
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+
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119
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```
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120
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+
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121
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122
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123
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124
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125
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```plain
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126
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+
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127
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+
In : x = deque(range(1000000), 1000000)
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128
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+
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129
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130
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+
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131
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+
In : %timeit y = list(islice(x, 100, 10))
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132
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133
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808 ns ± 12.6 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
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134
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135
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136
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137
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In : %timeit slice_deque(x, 100,10)
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138
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139
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1.99 µs ± 52.5 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
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140
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+
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141
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+
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142
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+
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143
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+
In %timeit y = list(islice(x, 700000, 10))
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144
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145
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2.31 ms ± 44.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
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146
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+
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147
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148
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149
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+
In : %timeit slice_deque(x, 700000,10)
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150
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151
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257 µs ± 19.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
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152
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+
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153
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```
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1
追求
test
CHANGED
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@@ -1,6 +1,8 @@
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1
1
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一般的にこれが最良という方法はありません。
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2
2
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3
3
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dequeはどういうアルゴリズムで動いているかを中までわかった上でないと**速くならない**し、実際にどのぐらいのデータをどのように扱うかを前提にしないと使うべきなのか判断できない、かなり低水準なライブラリなのです。
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4
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+
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5
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+
（だからこそスライス実装を提供していないように思えます）
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4
6
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5
7
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6
8
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