关于python:15个-Python-坏习惯你中招了吗

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大家好,我是陈程。

明天我将分享 15 个 Python 坏习惯,呈现这些坏习惯的起因次要是开发者在 Python 方面经验不足。通过摒弃这些习惯并以 Pythonic 的形式编写代码,不仅能够进步你的代码品质,还能够给看代码的人留下好印象哦~

1、拼接字符串用 + 号

坏的做法:

def manual_str_formatting(name, subscribers):

if subscribers > 100000:
    print("Wow" + name + "! you have" + str(subscribers) + "subscribers!")
else:
    print("Lol" + name + "that's not many subs")

调整后的做法是应用 f-string,而且效率会更高:

def manual_str_formatting(name, subscribers):

# better
if subscribers > 100000:
    print(f"Wow {name}! you have {subscribers} subscribers!")
else:
    print(f"Lol {name} that's not many subs")

2、应用 finaly 而不是上下文管理器

坏的做法:

def finally_instead_of_context_manager(host, port):

s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
try:
    s.connect((host, port))
    s.sendall(b'Hello, world')
finally:
    s.close()

调整后的做法是应用上下文管理器,即便产生异样,也会敞开 socket::

def finally_instead_of_context_manager(host, port):

# close even if exception
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
    s.connect((host, port))
    s.sendall(b'Hello, world')

3、尝试手动敞开文件

坏的做法:

def manually_calling_close_on_a_file(filename):

f = open(filename, "w")
f.write("hello!\n")
f.close()

调整后的做法是应用上下文管理器,即便产生异样,也会主动敞开文件,但凡有上下文管理器的,都应该首先采纳:

def manually_calling_close_on_a_file(filename):

with open(filename) as f:
    f.write("hello!\n")
# close automatic, even if exception

4、except 前面什么也不写

坏的做法:

def bare_except():

while True:
    try:
        s = input("Input a number:")
        x = int(s)
        break
    except:  # oops! can't CTRL-C to exit
        print("Not a number, try again")

这样会捕获所有异样,导致按下 CTRL-C 程序都不会终止,调整后的做法是

def bare_except():

while True:
    try:
        s = input("Input a number:")
        x = int(s)
        break
    except Exception:  # 比这更好的是用 ValueError
        print("Not a number, try again")

5、函数参数应用可变对象

如果函数参数应用可变对象,那么下次调用时可能会产生非预期后果,坏的做法

def mutable_default_arguments():

def append(n, l=[]):
    l.append(n)
    return l
l1 = append(0)  # [0]
l2 = append(1)  # [0, 1]

调整后的做法,如下:

def mutable_default_arguments():

def append(n, l=None):
    if l is None:
        l = []
    l.append(n)
    return l

l1 = append(0)  # [0]
l2 = append(1)  # [1]

6、从不必推导式

坏的做法

squares = {}
for i in range(10):

squares[i] = i * i

调整后的做法

odd_squares = {i: i * i for i in range(10)}

7、推导式用的上瘾

推导式尽管好用,然而不能够就义可读性,坏的做法

c = [

sum(a[n * i + k] * b[n * k + j] for k in range(n))
for i in range(n)
for j in range(n)

]

调整后的做法,如下:

c = []
for i in range(n):

for j in range(n):
    ij_entry = sum(a[n * i + k] * b[n * k + j] for k in range(n))
    c.append(ij_entry)

8、用 == 判断是否单例

坏的做法

def equality_for_singletons(x):

if x == None:
    pass
if x == True:
    pass

if x == False:
    pass

调整后的做法,如下:

def equality_for_singletons(x):

# better
if x is None:
    pass
if x is True:
    pass
if x is False:
    pass

9、应用类 C 格调的 for 循环

坏的做法

def range_len_pattern():

a = [1, 2, 3]
for i in range(len(a)):
    v = a[i]
    ...
b = [4, 5, 6]
for i in range(len(b)):
    av = a[i]
    bv = b[i]
    ...

调整后的做法,如下:

def range_len_pattern():

a = [1, 2, 3]
# instead
for v in a:
    ...
# or if you wanted the index
for i, v in enumerate(a):
    ...
# instead use zip
for av, bv in zip(a, b):
    ...

10、不实用 dict.items

坏的做法

def not_using_dict_items():

d = {"a": 1, "b": 2, "c": 3}
for key in d:
    val = d[key]
    ...

调整后的做法,如下:

def not_using_dict_items():

d = {"a": 1, "b": 2, "c": 3}
for key, val in d.items():
    ...

11、应用 time.time() 统计耗时

坏的做法

def timing_with_time():

start = time.time()
time.sleep(1)
end = time.time()
print(end - start)

调整后的做法是应用 time.perf_counter(),更准确:

def timing_with_time():
# more accurate

start = time.perf_counter()
time.sleep(1)
end = time.perf_counter()
print(end - start)

12、记录日志应用 print 而不是 logging

坏的做法

def print_vs_logging():

print("debug info")
print("just some info")
print("bad error")

调整后的做法,如下:

def print_vs_logging():

# versus
# in main
level = logging.DEBUG
fmt = '[%(levelname)s] %(asctime)s - %(message)s'
logging.basicConfig(level=level, format=fmt)
# wherever
logging.debug("debug info")
logging.info("just some info")
logging.error("uh oh :(")

13、调用外部命令时应用 shell=True

坏的做法

subprocess.run([“ls -l”], capture_output=True, shell=True)

如果 shell=True,则将 ls -l 传递给 /bin/sh(shell) 而不是 Unix 上的 ls 程序,会导致 subprocess 产生一个两头 shell 过程,换句话说,应用两头 shell 意味着在命令运行之前,命令字符串中的变量、glob 模式和其余非凡的 shell 性能都会被预处理。比方,$HOME 会在在执行 echo 命令之前被解决解决。

调整后的做法是回绝从 shell 执行,如下:

subprocess.run([“ls”, “-l”], capture_output=True)

14、从不尝试应用 numpy

坏的做法

def not_using_numpy_pandas():

x = list(range(100))
y = list(range(100))
s = [a + b for a, b in zip(x, y)]

调整后的的做法,如下:

import numpy as np
def not_using_numpy_pandas():

# 性能更快
x = np.arange(100)
y = np.arange(100)
s = x + y

15、喜爱 import *

调整后的做法,如下:

from itertools import *
count()

这样的话,没有人晓得这个脚本到底有少数变量,比拟好的做法:

from mypackage.nearby_module import awesome_function
def main():
awesome_function()
if name == ‘__main__’:

main()

以上就是我总结的一些小技巧,到这里就完结了

若是有小伙伴还有其余补充或者不同意见,欢送在评论中进行探讨或者私信我哦~

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