从列表或元组中显式选择项目 [英] Explicitly select items from a list or tuple

问题描述

我有以下 Python 列表(也可以是元组):

myList = ['foo', 'bar', 'baz', 'quux']

我可以说

<预><代码>>>>我的列表[0:3]['foo', 'bar', 'baz']>>>myList[::2]['foo', 'baz']>>>myList[1::2]['bar', 'quux']

如何明确挑选索引没有特定模式的项目?例如，我想选择[0,2,3].或者从一个包含 1000 个项目的非常大的列表中，我想选择 [87, 342, 217, 998, 500].是否有一些 Python 语法可以做到这一点?看起来像的东西:

<预><代码>>>>myBigList[87, 342, 217, 998, 500]

解决方案

list( myBigList[i] for i in [87, 342, 217, 998, 500] )

<小时>

我将答案与 python 2.5.2 进行了比较:

• 19.7 usec: [ myBigList[i] for i in [87, 342, 217, 998, 500] ]

• 20.6 使用:map(myBigList.__getitem__, (87, 342, 217, 998, 500))

• 22.7 使用:itemgetter(87, 342, 217, 998, 500)(myBigList)

• 24.6 usec: list( myBigList[i] for i in [87, 342, 217, 998, 500] )

请注意，在 Python 3 中，第 1 个已更改为与第 4 个相同.

<小时>

另一种选择是从 numpy.array 开始，它允许通过列表或 numpy.array 进行索引:

<预><代码>>>>导入 numpy>>>myBigList = numpy.array(range(1000))>>>myBigList[(87, 342, 217, 998, 500)]回溯(最近一次调用最后一次):文件<stdin>"，第 1 行，在 <module> 中索引错误:无效索引>>>myBigList[[87, 342, 217, 998, 500]]数组([ 87, 342, 217, 998, 500])>>>myBigList[numpy.array([87, 342, 217, 998, 500])]数组([ 87, 342, 217, 998, 500])

tuple 与切片的工作方式不同.

I have the following Python list (can also be a tuple):

myList = ['foo', 'bar', 'baz', 'quux']

I can say

>>> myList[0:3]
['foo', 'bar', 'baz']
>>> myList[::2]
['foo', 'baz']
>>> myList[1::2]
['bar', 'quux']

How do I explicitly pick out items whose indices have no specific patterns? For example, I want to select [0,2,3]. Or from a very big list of 1000 items, I want to select [87, 342, 217, 998, 500]. Is there some Python syntax that does that? Something that looks like:

>>> myBigList[87, 342, 217, 998, 500]

解决方案

list( myBigList[i] for i in [87, 342, 217, 998, 500] )

---

I compared the answers with python 2.5.2:

• 19.7 usec: [ myBigList[i] for i in [87, 342, 217, 998, 500] ]

• 20.6 usec: map(myBigList.__getitem__, (87, 342, 217, 998, 500))

• 22.7 usec: itemgetter(87, 342, 217, 998, 500)(myBigList)

• 24.6 usec: list( myBigList[i] for i in [87, 342, 217, 998, 500] )

Note that in Python 3, the 1st was changed to be the same as the 4th.

---

Another option would be to start out with a numpy.array which allows indexing via a list or a numpy.array:

>>> import numpy
>>> myBigList = numpy.array(range(1000))
>>> myBigList[(87, 342, 217, 998, 500)]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
IndexError: invalid index
>>> myBigList[[87, 342, 217, 998, 500]]
array([ 87, 342, 217, 998, 500])
>>> myBigList[numpy.array([87, 342, 217, 998, 500])]
array([ 87, 342, 217, 998, 500])

The tuple doesn't work the same way as those are slices.

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