Kyle所提, 于其书中关乎的观点, 于其《 》一书里对的描述存有相同主题, 他们大体在探讨这两种语言的同一问题, 按我之言表述为之所以这门语言很容易学, 致使众多实践者只掌握了其基础知识, 却忽视去深入探究这门语言更具高级性与强大之处, 而这恰恰是它真正独特且强大的所在。因此, 咱们来简略探讨一番, 那些你大概没听闻过的功能, 然而若你想变为一名着实经验丰富的, 你必定是想要知晓的。参数默认值要着重留意的是, 其参数于碰到函数定义之际便会展开评估。这表明每当调用下文将会提及的函数之时, 要是未明确给出 memo 参数的值, 那它就会运用函数定义之时所创建的同一个字典对象。def fib_memo(n, memo{0:0, 1:1}):n 是你想要返回的序列中的第 n 个数字if not n in memo:memo[n] fib_memo(n-1) fib_memo(n-2)return memo[n]# 返回一个介于0和100之间包括0和100的数字。fib_memo(6) # 应该返回8这段话所涉及的代码于其中能够顺利运行, 这同样表明你能够于一个脚本里多次去执行那个函数, 像在一个for循环之中, 每一回执行之际都会使所要计算之数字得以增加, 然而却不会触及“超过最大递归深度”这一限制, 原因在于memo会持续进行扩展, 有关这方面的更多讯息可于我的另外一篇文章里寻觅到。海象操作符首先, 海象操作符, 也就是 : , 是在 3.8 中被引入的, 其次, 它能够允许你在表达式里为变量进行赋值, 进而, 这样一来, 你就可以在一个表达式当中为变量赋值并且检查其值。import randomsome_value random.randint(0,100) # 返回一个介于0和100之间包括0和100的数字。if((below_ten : some_value) 10):print(f{below_ten} 小于 10)显然它也可以方便地赋值并检查返回的值是否包含真值if(result : some_method): # 如果结果不为假值Falsyprint(result)*args 和 **凭借星号*, 于将其传递给函数以前, 你能够解包参数或者解包关键字参数即使用**。例如考虑以下代码my_numbers [1,2]def sum_numbers(first_number, second_number):return first_number second_number# 这将返回一个类型错误。# 类型错误sum 缺少 1 个必需的位置参数“second_number” sum_numbersmy_numbers#这将返回预期的结果3。sum_numbers(*my_numbers)当我们去调用函数之际, 要是不解包的话, 那么它就会引发一个情况, 鉴于函数所期望获得的是两个独立分开的参数。然而呢, 借助使用星号*, 我们能够从某个里面解包出值并且把它们当作独立的参数去进行传递, 如此一来便可以得到正确无误的输出结果。这一种解包技术, 并非仅仅适用于元组以及列表, 它同样适用于字典, 即便它会把键当作参数去传递。那么关键字参数又如何呢? 针对于此, 我们能够借助双星号 (**)。下面这段代码作为示例:def greet_person(last_name, first_name):print(fHello {first_name} {last_name})data {first_name: John, last_name: Doe}greet_person(**data)将它们当作函数的参数来用, 除了对一个序列进行解包之外, 你还能够借助它去构建一个全新的序列, 举例来说:numbers [1, 2, 3, 4, 5]new_list_numbers [*numbers]起初的列表维持原状, 并且你存有一个变量, 它容纳了同款列表的复制品。然而, 針對含有对象的链接需谨慎留意:numbers [[1, 2], [3, 4], [5, 6]]packed_numbers [*numbers]numbers[0].append(10) # 修改原始列表中的嵌套列表print(numbers) # 输出: [[1, 2, 10], [3, 4], [5, 6]]print(packed_numbers) # 输出: [[1, 2, 10], [3, 4], [5, 6]]any 和 all“any”与“all”属于两个内建函数, 它们针对可迭代对象运行操作, 这些可迭代对象包含列表、元组或者集合, 且基于可迭代对象里的元素返回一个布尔值。例如some_booleans [True, False, False, False]any(some_booleans) # 返回 Trueall(some_booleans) # 返回 False你能够把 all 以及 any 函数跟列表推导式相互结合起来运用, 它会返回一个能够迭代的结果, 并且把这个结果当作参数传送给 all 函数:numbers [5, 10, 3, 8, -2]all_positive all(num 0 for num in numbers)… 或 any 函数fruits [apple, banana, cherry, durian]# 检查是否所有水果都以“a”开头result all(fruit.startswith(a) for fruit in fruits)print(result)下面的表格显示了根据可迭代对象中的值返回的输出的不同。变量交换你能够将元组进行组合打包于等号 () 的右边出现以及解包在等号 () 的左边出现, 并且借助此功能来实现变量的交换:a 10b 5# 通过打包和解包交换 b 和 a 的值a, b b, aprint(a) #5print(b) #10str vs repr我们惯常运用str()把某个变量或者值转化成字符串, 以此便于开展调试打印。我想要你知悉repr()。主要的差异在于, repr尝试返回对象的可打印表示, 然而str仅仅尝试返回一个字符串表示。下面是一个更好的例子import datetimetoday datetime.datetime.nowprint(str(today))print(str(today)) # 输出: 2023-07-20 15:30:00.123456print(repr(today)) # 输出datetime.datetime(2023, 7, 20, 15, 30, 0, 123456)如你所见到的, str只是简单地把 当作一个用于字符串表示的返回。要是你想要去确定变量today是不是包含着一个字符串或者是一个 对象, 单单从这个信息里你是得不出来答案的。另一方面来讲, repr给出了关于变量所持有实际对象的相关信息。在进行调试这个过程的时候, 这个信息会更具价值。扩展的可迭代对象解包这能够以较为简易的方式去理解, 要是你期望借助一条命令来获取序列的首个值以及最后一个值。first, *middle, last [1, 2, 3, 4, 5]print(first) # 1print(middle) # [2, 3, 4]print(last) # 5但这也是可行的*the_first_three, second_last, last [1, 2, 3, 4, 5]print(the_first_three) # [1, 2, 3]print(second_last) # 4print(last) # 5或者其他组合。多个上下文管理器我们习惯于一次使用一个上下文管理器比如打开一个文件with open(file.txt, r) as file:# 使用该文件的代码# 该文件将在块结束时自动关闭# 即使发生异常# 示例从文件中读取行for line in file:print(line.strip)with open(file_2.txt, r) as other_file:# 第二个上下文管理器for line in other_file:print(line.strip)但, 我们能够轻松地于一个语句之内开启多个文件。要是你希望把一行内容写入另外一个文件, 这是极为简便的:with open(file1.txt) as file1, open(file2.txt) as file2:# 同时使用 file1 和 file2 的代码# 文件将在块结束时自动关闭# 即使发生异常# 示例从文件 1 读取行并将其写入文件 2for line in file1:file2.write(line)调试器众多变量, 能于我们的文件里打印出来作调试用, 或是, 可仅仅使用调试器pdb, 它有助于我们去设置断点好让操作变得更简便:import pdb# 在你的代码中设置这个断点pdb.set_trace让这个功能具备更高价值的要点在于, 程序会于你给定的断点之处实现停止, 你能够打印任意变量来核查其在特定断点那儿的值或者存在状况。去尝试一下当程序引发一个断点的时候, 你能够借助以下几个命令:.模块里头的, 类给出了一种便利的办法去计算那些可迭代对象当中的元素数量:Countercollectionsfrom collections import Countermy_list [1, 2, 3, 1, 2, 1, 3, 4, 5]counts Counter(my_list)print(counts) # 输出: 计数器({1: 3, 2: 2, 3: 2, 4: 1, 5: 1})使用 实现组合我们能够将不一样的for循环予以组合, 以此来创建排列, 或者组合, 又或者笛卡尔积, 要不然的话, 我们能够单纯地运用内建的。(排列)import itertools# 生成排列perms itertools.permutations([1, 2, 3], 2)print(list(perms)) # 输出: [(1, 2), (1, 3), (2, 1), (2, 3), (3, 1), (3, 2)](组合)import itertools# 生成组合combs itertools.combinations(ABC, 2)print(list(combs)) # 输出: [(A, B), (A, C), (B, C)](笛卡尔积)import itertools# 生成笛卡尔积cartesian itertools.product(AB, [1, 2])print(list(cartesian)) # 输出: [(A, 1), (A, 2), (B, 1), (B, 2)]下划线的两种用法如下存在于其中的, 是运用下划线的两类方式: 是当作大数字的分隔标识之物, 又或是当作舍弃变量的用途之方式。丢弃变量下划线 _ 可以用作丢弃变量用来丢弃不想要的值# 忽略函数的第一个返回值_, result some_function# 不使用循环变量进行循环for _ in range(5):do_something# 你只需要第一个和最后一个first, *_, last [1, 2, 3, 4, 5]大数字的分隔符遇到要处理大数字值那时, 你能去使用下划线 (_) 当作视觉分隔符, 拿来提升可读性。此特性于 3.6 里被引入, 称作了下划线字面量。population 7_900_000_000revenue 3_249_576_382.50print(population) # 输出: 7900000000print(revenue) # 输出: 3249576382.5你可知晓, 当中另外存在哪些极易被忽视的好用功能或者技巧呢? 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