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174 lines
2.9 KiB
Markdown
174 lines
2.9 KiB
Markdown
# operator, functools, itertools, toolz, fn, funcy 模块
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## operator 模块
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In [1]:
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```py
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import operator as op
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```
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`operator` 模块提供了各种操作符(`+,*,[]`)的函数版本方便使用:
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加法:
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In [2]:
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```py
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print reduce(op.add, range(10))
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```
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```py
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45
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```
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乘法:
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In [3]:
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```py
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print reduce(op.mul, range(1,10))
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```
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```py
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362880
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```
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`[]`:
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In [4]:
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```py
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my_list = [('a', 1), ('bb', 4), ('ccc', 2), ('dddd', 3)]
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# 标准排序
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print sorted(my_list)
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# 使用元素的第二个元素排序
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print sorted(my_list, key=op.itemgetter(1))
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# 使用第一个元素的长度进行排序:
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print sorted(my_list, key=lambda x: len(x[0]))
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```
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```py
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[('a', 1), ('bb', 4), ('ccc', 2), ('dddd', 3)]
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[('a', 1), ('ccc', 2), ('dddd', 3), ('bb', 4)]
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[('a', 1), ('bb', 4), ('ccc', 2), ('dddd', 3)]
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```
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## functools 模块
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`functools` 包含很多跟函数相关的工具,比如之前看到的 `wraps` 函数,不过最常用的是 `partial` 函数,这个函数允许我们使用一个函数中生成一个新函数,这个函数使用原来的函数,不过某些参数被指定了:
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In [5]:
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```py
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from functools import partial
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# 将 reduce 的第一个参数指定为加法,得到的是类似求和的函数
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sum_ = partial(reduce, op.add)
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# 将 reduce 的第一个参数指定为乘法,得到的是类似求连乘的函数
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prod_ = partial(reduce, op.mul)
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print sum_([1,2,3,4])
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print prod_([1,2,3,4])
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```
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```py
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10
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24
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```
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`partial` 函数还可以按照键值对传入固定参数。
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## itertools 模块
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`itertools` 包含很多与迭代器对象相关的工具,其中比较常用的是排列组合生成器 `permutations` 和 `combinations`,还有在数据分析中常用的 `groupby` 生成器:
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In [6]:
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```py
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from itertools import cycle, groupby, islice, permutations, combinations
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```
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`cycle` 返回一个无限的迭代器,按照顺序重复输出输入迭代器中的内容,`islice` 则返回一个迭代器中的一段内容:
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In [7]:
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```py
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print list(islice(cycle('abcd'), 0, 10))
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```
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```py
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['a', 'b', 'c', 'd', 'a', 'b', 'c', 'd', 'a', 'b']
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```
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`groupby` 返回一个字典,按照指定的 `key` 对一组数据进行分组,字典的键是 `key`,值是一个迭代器:
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In [8]:
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```py
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animals = sorted(['pig', 'cow', 'giraffe', 'elephant',
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'dog', 'cat', 'hippo', 'lion', 'tiger'], key=len)
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# 按照长度进行分组
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for k, g in groupby(animals, key=len):
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print k, list(g)
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print
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```
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```py
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3 ['pig', 'cow', 'dog', 'cat']
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4 ['lion']
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5 ['hippo', 'tiger']
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7 ['giraffe']
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8 ['elephant']
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```
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排列:
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In [9]:
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```py
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print [''.join(p) for p in permutations('abc')]
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```
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```py
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['abc', 'acb', 'bac', 'bca', 'cab', 'cba']
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```
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组合:
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In [10]:
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```py
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print [list(c) for c in combinations([1,2,3,4], r=2)]
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```
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```py
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[[1, 2], [1, 3], [1, 4], [2, 3], [2, 4], [3, 4]]
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```
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## toolz, fn 和 funcy 模块
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这三个模块的作用是方便我们在编程的时候使用函数式编程的风格。 |