元组传参
1
2
3
4
| def add(*args):
print(args, type(args))
add(2, 3)
|
1
2
3
4
5
6
7
8
9
10
| def add(*numbers):
total = 0
for num in numbers:
total += num
return total
print(add(2, 3))
print(add(2, 3, 5))
print(add(2, 3, 5, 7))
print(add(2, 3, 5, 7, 9))
|
字典传参
1
2
3
4
5
| def total_fruits(**kwargs):
print(kwargs, type(kwargs))
total_fruits(banana=5, mango=7, apple=8)
|
1
| {'banana': 5, 'mango': 7, 'apple': 8} <class 'dict'>
|
1
2
3
4
5
6
7
8
9
10
| def total_fruits(**fruits):
total = 0
for amount in fruits.values():
total += amount
return total
print(total_fruits(banana=5, mango=7, apple=8))
print(total_fruits(banana=5, mango=7, apple=8, oranges=10))
print(total_fruits(banana=5, mango=7))
|
和return返回不同，yeild返回的是一个可迭代对象，可以通过next(yeild返回object)来取下一个元素
1
2
3
4
5
6
7
8
9
10
11
| def test_yield():
for i in range(10):
yield i
print(type(test_yield()))
_iter = test_yield()
print(next(_iter))
print(next(_iter))
print(next(_iter))
|
1
2
3
4
| <class 'generator'>
0
1
2
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
| def test_yield():
for i in [3, 5, 6, 1, 3, 516, 6321, 1]:
yield i
_iter = test_yield()
num = 0
try:
while True:
num += 1
print(next(_iter))
except Exception as ex:
pass
print("列表的元素个数%d" % num)
|
1
2
3
4
5
6
7
8
9
| 3
5
6
1
3
516
6321
1
列表的元素个数9
|
==问，为什么要这么干，直接len()不就好了吗？答，除了获取长度，还可以进行其他操作，而不用先获取列表长度进行。==
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
| >>> # 返回一个迭代器
>>> var = map(str, [3, 1, 2])
>>> print(list(var))
>>> print(list(var)) # 迭代器走完就没有值了
>>> begin = time.time() # 记录转换耗时
>>> # 返回一个列表(推荐使用，速度更快，因未出现显式循环)
>>> var_s = list(map(str, [3, 1, 2]))
>>> print(var_s, "转换耗时: ", time.time()-begin)
>>> begin = time.time() # 记录转换耗时
>>> # 返回一个列表
>>> var_s = [str(i) for i in [3, 1, 2]]
>>> print(var_s, "转换耗时: ", time.time()-begin)
['3', '1', '2']
[]
['3', '1', '2'] 转换耗时: 1.430511474609375e-06
['3', '1', '2'] 转换耗时: 1.6689300537109375e-06
|
python的执行中，等号右边先执行，如下代码中，python先创建出数组然后再让a指向该数组，如果简单用等号将a 赋值给b，则b也指向该数组，修改b的同时，原来的a也会随之改变，如果不想要a 发生改变，需使用b = a.copy()
1
2
3
4
5
6
7
8
9
10
11
12
13
| >>> # 第一种赋值方式
>>> a = [3, 1, 2]
>>> b = a
>>> b[0] = 0
>>> print(a, b)
>>> # 第二种赋值方式
>>> a = [3, 1, 2]
>>> b = a.copy() # 复制法
>>> b[0] = 0
>>> print(a, b)
out:
[0, 1, 2] [0, 1, 2]
[3, 1, 2] [0, 1, 2]
|
单纯输出列表效果不好看，可以做成表格形式。
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
| >>> # 列表显示为表格
>>> from prettytable import PrettyTable
>>> fruit = [['apple', 'red', '100g'], ['banana', 'yellow', '80g']]
>>> print("直接打印列表\n", fruit)
>>> x = PrettyTable() # 创建表格实例
>>> x.field_names = ['fruit', 'color', 'weight'] # 定义表头
>>> x.add_rows(fruit)
>>> print("打印成表格\n", x)
out:
直接打印列表
[['apple', 'red', '100g'], ['banana', 'yellow', '80g']]
打印成表格
+--------+--------+--------+
| fruit | color | weight |
+--------+--------+--------+
| apple | red | 100g |
| banana | yellow | 80g |
+--------+--------+--------+
|
1
2
3
4
5
6
7
8
9
10
| >>> a = [[1, 2],
[3, 4],
[5, 6]]
>>> print(*a)
>>> print(zip(*a))
>>> print(list(zip(*a)))
out:
[1, 2] [3, 4] [5, 6]
<zip object at 0x7fb6cd7e7f00>
[(1, 3, 5), (2, 4, 6)]
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
| >>> a = [3, 2, None, "", 0]
>>> # 剔除假值（python中,None、0、"" 都为假”）
>>> print(list(filter(bool, a)))
>>> # 剔除 3
>>> print(list(filter(lambda i: i != 3, a)))
# 筛选出奇数
b = [1, 2, 3, 4, 5]
print(list(filter(lambda i: i % 2, b)))
out:
[3, 2]
[2, None, '', 0]
[1, 3, 5]
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
| >>> import sys
>>> a = [3, 2, None, "", 0]
>>> b = 9
>>> c = "python"
>>> print("变量a占用的内存空间为{}字节，\n变量b占用的内存空间为{}字节，\n变量c占用内存空间为{}字 节。".format(
>>> sys.getsizeof(a),
>>> sys.getsizeof(b),
>>> sys.getsizeof(c),
>>> ))
out:
变量a占用的内存空间为112字节，
变量b占用的内存空间为28字节，
变量c占用内存空间为55字节。
|
1
2
3
| >>> print(f"{5/2:.2f}")
out:
2.50
|
1
2
3
4
5
6
7
8
9
10
11
12
13
| import time
s = time.time()
np.sum(distance)
print(f"np求和耗时 {time.time() - s}")
sum = 0
total = distance.shape[0]
s = time.time()
for i in range(total):
sum += distance[i]
print(f"循环求和耗时 {time.time() - s}")
np求和耗时 0.00003552436828613281e-05
循环求和耗时 0.0009284019470214844
|
1
2
3
4
5
6
7
8
9
10
| distance = np.random.random((7532, 11314))
import time
s = time.time()
sort_idx = np.argsort(distance, axis=-1) # 独立将每一行元素从小到达排列（即相似性从大到小）的下标 (7532, 11314)
print(f"np排序耗时{time.time() - s}")
s = time.time()
sort_idx = np.array(torch.argsort(torch.tensor(distance).cuda(), dim=-1).detach().cpu().numpy())
print(f"tensor排序耗时{time.time() - s}")
np排序耗时3.7687795162200928
tensor排序耗时3.2619576454162598
|
