列表推导式
[expr for val in collection if condition]
- 等同于以下的for循环
result = []
for val in collections:
if condition:
result.append(expr)
- 举例
strings = ['a', 'as', 'bat', 'car', 'dove', 'python']
list1 = [x.upper() for x in strings if len(x) > 2]
print(list1)
字典的推导式
dict_comp = {key-expr: value-expr for value in collection if condition}
- 集合的推导式
set_comp = {expr for value in collection if condition}
- 举例
strings = ['a', 'as', 'bat', 'car', 'dove', 'python']
loc_mapping = {val: index for index, val in enumerate(strings)}
print(loc_mapping)
unique_lengths = {len(x) for x in strings}
print(unique_lengths)
unique_lengths2 = set(map(len, strings))
print(unique_lengths2)
嵌套列表推导式
- 请牢记for表达式的顺序应当和你写嵌套for循环来替代列表推导式的顺序一致：
flattened2 = []
some_tuples = [['John', 'Emily', 'Michael', 'Mary', 'Steven'], ['Maria', 'Juan', 'Javier', 'Natalia', 'Pilar']]
for tup in some_tuples:
for x in tup:
flattened2.append(x)
print(flattened2)
net_list = [[x for x in tup] for tup in some_tuples]
print(net_list)
- 举例
all_data = [['John', 'Emily', 'Michael', 'Mary', 'Steven'], ['Maria', 'Juan', 'Javier', 'Natalia', 'Pilar']]
names_of_interest = []
for names in all_data:
enough_es = [name for name in names if name.count('e') >= 2]
names_of_interest.extend(enough_es)
print(names_of_interest)
result = [name for names in all_data for name in names if name.count('e') >= 2]
print(result)
some_tuples = [(1, 2, 3), (4, 5, 6), (7, 8, 9)]
flattened = [x for tup in some_tuples for x in tup]
print(flattened)
生成器表达式
用生成器表达式来创建生成器更为简单。生成器表达式与列表、字典、集合的推导式很类似，创建一个生成器表达式，只需要将列表推导式的中括号替换为小括号即可：
生成器是构造新的可遍历对象的一种非常简洁的方式。普通函数执行并一次返回单个结果，而生成器则“惰性”地返回一个多结果序列，在每一个元素产生之后暂停，直到下一个请求。如需创建一个生成器，只需要在函数中将返回关键字return替换为yield关键字：
gen = (x ** 2 for x in range(100))
print(gen)
def _make_gen():
for x in range(100):
yield x ** 2
gen2 = _make_gen()
s = sum(x ** 2 for x in range(100))
s2 = sum([x ** 2 for x in range(100)])
d = dict((i, i ** 2) for i in range(5))
d2 = dict([(i, i**2) for i in range(5)])
print(s)
print(s2)
print(d)
print(d2)