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這篇“Python開發中需要摒棄的壞習慣有哪些”文章的知識點大部分人都不太理解,所以小編給大家總結了以下內容,內容詳細,步驟清晰,具有一定的借鑒價值,希望大家閱讀完這篇文章能有所收獲,下面我們一起來看看這篇“Python開發中需要摒棄的壞習慣有哪些”文章吧。
壞的做法:
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")
好的做法是使用 f-string,而且效率會更高:
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")
壞的做法:
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()
好的做法是使用上下文管理器,即使發生異常,也會關閉 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')
壞的做法:
def manually_calling_close_on_a_file(filename): f = open(filename, "w") f.write("hello!\n") f.close()
好的做法是使用上下文管理器,即使發生異常,也會自動關閉文件,凡是有上下文管理器的,都應該首先采用:
def manually_calling_close_on_a_file(filename): with open(filename) as f: f.write("hello!\n") # close automatic, even if exception
壞的做法:
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")
這樣會捕捉所有異常,導致按下 CTRL-C 程序都不會終止,好的做法是
def bare_except(): while True: try: s = input("Input a number: ") x = int(s) break except Exception: # 比這更好的是用 ValueError print("Not a number, try again")
如果函數參數使用可變對象,那么下次調用時可能會產生非預期結果,壞的做法
def mutable_default_arguments(): def append(n, l=[]): l.append(n) return l l1 = append(0) # [0] l2 = append(1) # [0, 1]
好的做法:
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]
壞的做法
squares = {} for i in range(10): squares[i] = i * i
好的做法
odd_squares = {i: i * i for i in range(10)}
推導式雖然好用,但是不可以犧牲可讀性,壞的做法
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) ]
好的做法:
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)
壞的做法
def checking_type_equality(): Point = namedtuple('Point', ['x', 'y']) p = Point(1, 2) if type(p) == tuple: print("it's a tuple") else: print("it's not a tuple")
好的做法
def checking_type_equality(): Point = namedtuple('Point', ['x', 'y']) p = Point(1, 2) # probably meant to check if is instance of tuple if isinstance(p, tuple): print("it's a tuple") else: print("it's not a tuple")
壞的做法
def equality_for_singletons(x): if x == None: pass if x == True: pass if x == False: pass
好的做法
def equality_for_singletons(x): # better if x is None: pass if x is True: pass if x is False: pass
壞的做法
def checking_bool_or_len(x): if bool(x): pass if len(x) != 0: pass
好的做法
def checking_bool_or_len(x): # usually equivalent to if x: pass
壞的做法
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] ...
好的做法
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): ...
壞的做法
def not_using_dict_items(): d = {"a": 1, "b": 2, "c": 3} for key in d: val = d[key] ...
好的做法
def not_using_dict_items(): d = {"a": 1, "b": 2, "c": 3} for key, val in d.items(): ...
壞的做法
mytuple = 1, 2 x = mytuple[0] y = mytuple[1]
好的做法
mytuple = 1, 2 x, y = mytuple
壞的做法
def timing_with_time(): start = time.time() time.sleep(1) end = time.time() print(end - start)
好的做法是使用 time.perf_counter(),更精確:
def timing_with_time(): # more accurate start = time.perf_counter() time.sleep(1) end = time.perf_counter() print(end - start)
壞的做法
def print_vs_logging(): print("debug info") print("just some info") print("bad error")
好的做法
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 :(")
壞的做法
subprocess.run(["ls -l"], capture_output=True, shell=True)
如果 shell=True,則將 ls -l
傳遞給/bin/sh(shell) 而不是 Unix 上的 ls 程序,會導致 subprocess 產生一個中間 shell 進程, 換句話說,使用中間 shell 意味著在命令運行之前,命令字符串中的變量、glob 模式和其他特殊的 shell 功能都會被預處理。比如,$HOME 會在在執行 echo 命令之前被處理處理。
好的做法是拒絕從 shell 執行:
subprocess.run(["ls", "-l"], capture_output=True)
壞的做法
def not_using_numpy_pandas(): x = list(range(100)) y = list(range(100)) s = [a + b for a, b in zip(x, y)]
好的做法:
import numpy as np def not_using_numpy_pandas(): # 性能更快 x = np.arange(100) y = np.arange(100) s = x + y
壞的做法
from itertools import * count()
這樣的話,沒有人直到這個腳本到底有多數變量, 好的做法:
from mypackage.nearby_module import awesome_function def main(): awesome_function() if __name__ == '__main__': main()
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