给大家整理了19个pythonic的编程习惯

浅浅的花香味﹌ 2022-01-16 21:17 209阅读 0赞

[为什么80%的码农都做不了架构师？>>> ][80_] ![hot3.png][]

![python学习好习惯][python]

Python最大的优点之一就是语法简洁，好的代码就像伪代码一样，干净、整洁、一目了然。

要写出 Pythonic（优雅的、地道的、整洁的）代码，需要多看多学大牛们写的代码，github 上有很多非常优秀的源代码值得阅读，比如：requests、flask、tornado，下面列举一些常见的Pythonic写法。

# 0. 程序必须先让人读懂，然后才能让计算机执行。 #

“Programs must be written for people to read, and only incidentally for machines to execute.”

# 1. 交换赋值 #

##不推荐
    temp = a
    a = b
    b = a  
    
    ##推荐
    a, b = b, a  #  先生成一个元组(tuple)对象，然后unpack

# 2. Unpacking #

##不推荐
    l = ['David', 'Pythonista', '+1-514-555-1234']
    first_name = l[0]
    last_name = l[1]
    phone_number = l[2]  
    
    ##推荐
    l = ['David', 'Pythonista', '+1-514-555-1234']
    first_name, last_name, phone_number = l
    
    # Python 3 Only
    first, *middle, last = another_list

# 3. 使用操作符in #

##不推荐
    if fruit == "apple" or fruit == "orange" or fruit == "berry":
        # 多次判断  
    
    ##推荐
    if fruit in ["apple", "orange", "berry"]:
        # 使用 in 更加简洁

# 4. 字符串操作 #

##不推荐
    colors = ['red', 'blue', 'green', 'yellow']
    
    result = ''
    for s in colors:
        result += s  #  每次赋值都丢弃以前的字符串对象, 生成一个新对象  
    
    ##推荐
    colors = ['red', 'blue', 'green', 'yellow']
    result = ''.join(colors)  #  没有额外的内存分配

# 5. 字典键值列表 #

##不推荐
    for key in my_dict.keys():
        #  my_dict[key] ...  
    
    ##推荐
    for key in my_dict:
        #  my_dict[key] ...
    
    # 只有当循环中需要更改key值的情况下，我们需要使用 my_dict.keys()
    # 生成静态的键值列表。

# 6. 字典键值判断 #

##不推荐
    if my_dict.has_key(key):
        # ...do something with d[key]  
    
    ##推荐
    if key in my_dict:
        # ...do something with d[key]

# 7. 字典 get 和 setdefault 方法 #

##不推荐
    navs = {}
    for (portfolio, equity, position) in data:
        if portfolio not in navs:
                navs[portfolio] = 0
        navs[portfolio] += position * prices[equity]
    ##推荐
    navs = {}
    for (portfolio, equity, position) in data:
        # 使用 get 方法
        navs[portfolio] = navs.get(portfolio, 0) + position * prices[equity]
        # 或者使用 setdefault 方法
        navs.setdefault(portfolio, 0)
        navs[portfolio] += position * prices[equity]

# 8. 判断真伪 #

##不推荐
    if x == True:
        # ....
    if len(items) != 0:
        # ...
    if items != []:
        # ...  
    
    ##推荐
    if x:
        # ....
    if items:
        # ...

# 9. 遍历列表以及索引 #

##不推荐
    items = 'zero one two three'.split()
    # method 1
    i = 0
    for item in items:
        print i, item
        i += 1
    # method 2
    for i in range(len(items)):
        print i, items[i]
    
    ##推荐
    items = 'zero one two three'.split()
    for i, item in enumerate(items):
        print i, item

# 10. 列表推导 #

##不推荐
    new_list = []
    for item in a_list:
        if condition(item):
            new_list.append(fn(item))  
    
    ##推荐
    new_list = [fn(item) for item in a_list if condition(item)]

# 11. 列表推导-嵌套 #

##不推荐
    for sub_list in nested_list:
        if list_condition(sub_list):
            for item in sub_list:
                if item_condition(item):
                    # do something...  
    ##推荐
    gen = (item for sl in nested_list if list_condition(sl) \
                for item in sl if item_condition(item))
    for item in gen:
        # do something...

# 12. 循环嵌套 #

##不推荐
    for x in x_list:
        for y in y_list:
            for z in z_list:
                # do something for x &amp; y  
    
    ##推荐
    from itertools import product
    for x, y, z in product(x_list, y_list, z_list):
        # do something for x, y, z

# 13. 尽量使用生成器代替列表 #

##不推荐
    def my_range(n):
        i = 0
        result = []
        while i &lt; n:
            result.append(fn(i))
            i += 1
        return result  #  返回列表
    
    ##推荐
    def my_range(n):
        i = 0
        result = []
        while i &lt; n:
            yield fn(i)  #  使用生成器代替列表
            i += 1
    # 尽量用生成器代替列表，除非必须用到列表特有的函数。

# 14. 中间结果尽量使用imap/ifilter代替map/filter #

##不推荐
    reduce(rf, filter(ff, map(mf, a_list)))
    
    ##推荐
    from itertools import ifilter, imap
    reduce(rf, ifilter(ff, imap(mf, a_list)))
    # lazy evaluation 会带来更高的内存使用效率，特别是当处理大数据操作的时候。

# 15. 使用any/all函数 #

##不推荐
    found = False
    for item in a_list:
        if condition(item):
            found = True
            break
    if found:
        # do something if found...  
    
    ##推荐
    if any(condition(item) for item in a_list):
        # do something if found...

# 16. 属性(property) #

##不推荐
    class Clock(object):
        def __init__(self):
            self.__hour = 1
        def setHour(self, hour):
            if 25 &gt; hour &gt; 0: self.__hour = hour
            else: raise BadHourException
        def getHour(self):
            return self.__hour
    
    ##推荐
    class Clock(object):
        def __init__(self):
            self.__hour = 1
        def __setHour(self, hour):
            if 25 &gt; hour &gt; 0: self.__hour = hour
            else: raise BadHourException
        def __getHour(self):
            return self.__hour
        hour = property(__getHour, __setHour)

# 17. 使用 with 处理文件打开 #

##不推荐
    f = open("some_file.txt")
    try:
        data = f.read()
        # 其他文件操作..
    finally:
        f.close()
    
    ##推荐
    with open("some_file.txt") as f:
        data = f.read()
        # 其他文件操作...

# 18. 使用 with 忽视异常(仅限Python 3) #

##不推荐
    try:
        os.remove("somefile.txt")
    except OSError:
        pass
    
    ##推荐
    from contextlib import ignored  # Python 3 only
    
    with ignored(OSError):
        os.remove("somefile.txt")

# 19. 使用 with 处理加锁 #

##不推荐
    import threading
    lock = threading.Lock()
    
    lock.acquire()
    try:
        # 互斥操作...
    finally:
        lock.release()
    
    ##推荐
    import threading
    lock = threading.Lock()
    
    with lock:
        # 互斥操作...

关注公众号：「**Python专栏**」，后台回复「腾讯架构资源1」，获取由腾讯架构师整理的大数据学习资源包全套！！！ ![Python专栏二维码][Python]

转载于:https://my.oschina.net/u/1778027/blog/3018938

[80_]: https://my.oschina.net/u/2663968/blog/3051541
[hot3.png]: /images/20220117/374d6b91d0ae47d3a1a5396484dff33e.png
[python]: /images/20220117/9ebb187773fb41be9272abec66615d3a.png
[Python]: /images/20220117/7c67afb05fc340a2ba926e5b9c48eb51.png