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最近有许多小伙伴后台联系我,说目前想要学习Python,但是没有一份很好的资料入门。一方面的确现在市面上 Python 的资料过多,导致新手会不知如何选择,另一个问题很多资料内容也很杂,从1+1到深度学习都包括, 纯粹关注Python本身语法 的优质教材并不太多。
刚好我最近看到一份不错的英文Python入门资料,我将它做了一些 整理和翻译 写下了本文。这份资料非常纯粹,只有Python的基础语法,专门针对想要学习Python的小白。
注释
Python中用#表示单行注释,#之后的同行的内容都会被注释掉。
# Python中单行注释用#表示,#之后同行字符全部认为被注释。
使用三个连续的双引号表示多行注释,两个多行注释标识之间内容会被视作是注释。
""" 与之对应的是多行注释 用三个双引号表示,这两段双引号当中的内容都会被视作是注释 """
基础变量类型与操作符
Python当中的数字定义和其他语言一样:
#获得一个整数 3 # 获得一个浮点数 10.0
我们分别使用+, -, *, /表示加减乘除四则运算符。
1 + 1 # => 2 8 - 1 # => 7 10 * 2 # => 20 35 / 5 # => 7.0
这里要注意的是,在Python2当中,10/3这个操作会得到3,而不是3.33333。因为除数和被除数都是整数,所以Python会自动执行整数的计算,帮我们把得到的商取整。如果是10.0 / 3,就会得到3.33333。目前Python2已经不再维护了,可以不用关心其中的细节。
但问题是Python是一个 弱类型 的语言,如果我们在一个函数当中得到两个变量,是无法直接判断它们的类型的。这就导致了同样的计算符可能会得到不同的结果,这非常蛋疼。以至于 程序员 在运算除法的时候,往往都需要手工加上类型转化符,将被除数转成浮点数。
在Python3当中拨乱反正,修正了这个问题,即使是两个整数相除,并且可以整除的情况下,得到的结果也一定是浮点数。
如果我们想要得到整数,我们可以这么操作:
5 // 3 # => 1 -5 // 3 # => -2 5.0 // 3.0 # => 1.0 # works on floats too -5.0 // 3.0 # => -2.0
两个除号表示 取整除 ,Python会为我们保留去除余数的结果。
除了取整除操作之外还有取余数操作,数学上称为取模,Python中用%表示。
# Modulo operation 7 % 3 # => 1
Python中支持 乘方运算 ,我们可以不用调用额外的函数,而使用**符号来完成:
# Exponentiation (x**y, x to the yth power) 2**3 # => 8
当运算比较复杂的时候,我们可以用括号来强制改变运算顺序。
# Enforce precedence with parentheses 1 + 3 * 2 # => 7 (1 + 3) * 2 # => 8
逻辑运算
Python中用首字母大写的True和False表示真和假。
True # => True False # => False
用and表示与操作,or表示或操作,not表示非操作。而不是C++或者是 Java 当中的&&, || 和!。
# negate with not not True # => False not False # => True # Boolean Operators # Note "and" and "or" are case-sensitive True and False # => False False or True # => True
在Python底层, True和False其实是1和0 ,所以如果我们执行以下操作,是不会报错的,但是在逻辑上毫无意义。
# True and False are actually 1 and 0 but with different keywords True + True # => 2 True * 8 # => 8 False - 5 # => -5
我们用==判断相等的操作,可以看出来True==1, False == 0.
# Comparison operators look at the numerical value of True and False 0 == False # => True 1 == True # => True 2 == True # => False -5 != False # => True
我们要小心Python当中的bool()这个函数,它并不是转成bool类型的意思。如果我们执行这个函数,那么 只有0会被视作是False,其他所有数值都是True :
bool(0) # => False bool(4) # => True bool(-6) # => True 0 and 2 # => 0 -5 or 0 # => -5
Python中用==判断相等,>表示大于,>=表示大于等于, <表示小于,<=表示小于等于,!=表示不等。
# Equality is == 1 == 1 # => True 2 == 1 # => False # Inequality is != 1 != 1 # => False 2 != 1 # => True # More comparisons 1 < 10 # => True 1 > 10 # => False 2 <= 2 # => True 2 >= 2 # => True
我们可以用and和or拼装各个逻辑运算:
# Seeing whether a value is in a range 1 < 2 and 2 < 3 # => True 2 < 3 and 3 < 2 # => False # Chaining makes this look nicer 1 < 2 < 3 # => True 2 < 3 < 2 # => False
注意not,and,or之间的优先级,其中not > and > or。如果分不清楚的话,可以用括号强行改变运行顺序。
list和字符串
关于list的判断,我们常用的判断有两种,一种是刚才介绍的==,还有一种是is。我们有时候也会简单实用is来判断,那么这两者有什么区别呢?我们来看下面的例子:
a = [1, 2, 3, 4] # Point a at a new list, [1, 2, 3, 4] b = a # Point b at what a is pointing to b is a # => True, a and b refer to the same object b == a # => True, a's and b's objects are equal b = [1, 2, 3, 4] # Point b at a new list, [1, 2, 3, 4] b is a # => False, a and b do not refer to the same object b == a # => True, a's and b's objects are equal
Python是全引用的语言,其中的对象都使用引用来表示。is判断的就是 两个引用是否指向同一个对象 ,而==则是判断两个引用指向的具体内容是否相等。举个例子,如果我们把引用比喻成地址的话,is就是判断两个变量的是否指向同一个地址,比如说都是沿河东路XX号。而==则是判断这两个地址的收件人是否都叫张三。
显然,住在同一个地址的人一定都叫张三,但是住在不同地址的两个人也可以都叫张三,也可以叫不同的名字。所以如果a is b,那么a == b一定成立,反之则不然。
Python当中对字符串的限制比较松, 双引号和单引号都可以表示字符串 ,看个人喜好使用单引号或者是双引号。我个人比较喜欢单引号,因为写起来方便。
字符串也支持+操作,表示两个字符串相连。除此之外,我们把两个字符串写在一起,即使没有+,Python也会为我们拼接:
# Strings are created with " or ' "This is a string." 'This is also a string.' # Strings can be added too! But try not to do this. "Hello " + "world!" # => "Hello world!" # String literals (but not variables) can be concatenated without using '+' "Hello " "world!" # => "Hello world!"
我们可以使用[]来查找字符串当中某个位置的字符,用 len 来计算字符串的长度。
# A string can be treated like a list of characters "This is a string"[0] # => 'T' # You can find the length of a string len("This is a string") # => 16
我们可以在字符串前面 加上f表示格式操作 ,并且在格式操作当中也支持运算,比如可以嵌套上len函数等。不过要注意,只有Python3.6以上的版本支持f操作。
# You can also format using f-strings or formatted string literals (in Python 3.6+) name = "Reiko" f"She said her name is {name}." # => "She said her name is Reiko" # You can basically put any Python statement inside the braces and it will be output in the string. f"{name} is {len(name)} characters long." # => "Reiko is 5 characters long."
最后是None的判断,在Python当中None也是一个对象, 所有为None的变量都会指向这个对象 。根据我们前面所说的,既然所有的None都指向同一个地址,我们需要判断一个变量是否是None的时候,可以使用is来进行判断,当然用==也是可以的,不过我们通常使用is。
# None is an object None # => None # Don't use the equality "==" symbol to compare objects to None # Use "is" instead. This checks for equality of object identity. "etc" is None # => False None is None # => True
理解了None之后,我们再回到之前介绍过的bool()函数,它的用途其实就是判断值是否是空。所有类型的 默认空值会被返回False ,否则都是True。比如0,"",[], {}, ()等。
# None, 0, and empty strings/lists/dicts/tuples all evaluate to False. # All other values are True bool(None)# => False bool(0) # => False bool("") # => False bool([]) # => False bool({}) # => False bool(()) # => False
除了上面这些值以外的所有值传入都会得到True。
变量与集合
输入输出
Python当中的标准输入输出是 input和print 。
print会输出一个字符串,如果传入的不是字符串会自动调用__str__方法转成字符串进行输出。 默认输出会自动换行 ,如果想要以不同的字符结尾代替换行,可以传入end参数:
# Python has a print function print("I'm Python. Nice to meet you!") # => I'm Python. Nice to meet you! # By default the print function also prints out a newline at the end. # Use the optional argument end to change the end string. print("Hello, World", end="!") # => Hello, World!
使用input时,Python会在命令行接收一行字符串作为输入。可以在input当中传入字符串,会被当成提示输出:
# Simple way to get input data from console input_string_var = input("Enter some data: ") # Returns the data as a string # Note: In earlier versions of Python, input() method was named as raw_input()
变量
Python中声明对象 不需要带上类型 ,直接赋值即可,Python会自动关联类型,如果我们使用之前没有声明过的变量则会出发NameError异常。
# There are no declarations, only assignments. # Convention is to use lower_case_with_underscores some_var = 5 some_var # => 5 # Accessing a previously unassigned variable is an exception. # See Control Flow to learn more about exception handling. some_unknown_var # Raises a NameError
Python支持 三元表达式 ,但是语法和C++不同,使用if else结构,写成:
# if can be used as an expression # Equivalent of C's '?:' ternary operator "yahoo!" if 3 > 2 else 2 # => "yahoo!"
上段代码等价于:
if 3 > 2: return 'yahoo' else: return 2
list
Python中用[]表示空的list,我们也可以直接在其中填充元素进行初始化:
# Lists store sequences li = [] # You can start with a prefilled list other_li = [4, 5, 6]
使用append和pop可以在list的末尾插入或者删除元素:
# Add stuff to the end of a list with append li.append(1) # li is now [1] li.append(2) # li is now [1, 2] li.append(4) # li is now [1, 2, 4] li.append(3) # li is now [1, 2, 4, 3] # Remove from the end with pop li.pop() # => 3 and li is now [1, 2, 4] # Let's put it back li.append(3) # li is now [1, 2, 4, 3] again.
list可以通过[]加上下标访问指定位置的元素,如果是负数,则表示 倒序访问 。-1表示最后一个元素,-2表示倒数第二个,以此类推。如果访问的元素超过数组长度,则会出发 IndexError 的错误。
# Access a list like you would any array li[0] # => 1 # Look at the last element li[-1] # => 3 # Looking out of bounds is an IndexError li[4] # Raises an IndexError
list支持切片操作,所谓的切片则是从原list当中 拷贝 出指定的一段。我们用start: end的格式来获取切片,注意,这是一个 左闭右开区间 。如果留空表示全部获取,我们也可以额外再加入一个参数表示步长,比如[1:5:2]表示从1号位置开始,步长为2获取元素。得到的结果为[1, 3]。如果步长设置成-1则代表反向遍历。
# You can look at ranges with slice syntax. # The start index is included, the end index is not # (It's a closed/open range for you mathy types.) li[1:3] # Return list from index 1 to 3 => [2, 4] li[2:] # Return list starting from index 2 => [4, 3] li[:3] # Return list from beginning until index 3 => [1, 2, 4] li[::2] # Return list selecting every second entry => [1, 4] li[::-1] # Return list in reverse order => [3, 4, 2, 1] # Use any combination of these to make advanced slices # li[start:end:step]
如果我们要指定一段区间倒序,则前面的start和end也需要反过来,例如我想要获取[3: 6]区间的倒序,应该写成[6:3:-1]。
只写一个:,表示全部拷贝,如果用is判断拷贝前后的list会得到False。可以使用del删除指定位置的元素,或者可以使用remove方法。
# Make a one layer deep copy using slices li2 = li[:] # => li2 = [1, 2, 4, 3] but (li2 is li) will result in false. # Remove arbitrary elements from a list with "del" del li[2] # li is now [1, 2, 3] # Remove first occurrence of a value li.remove(2) # li is now [1, 3] li.remove(2) # Raises a ValueError as 2 is not in the list
insert方法可以 指定位置插入元素 ,index方法可以查询某个元素第一次出现的下标。
# Insert an element at a specific index li.insert(1, 2) # li is now [1, 2, 3] again # Get the index of the first item found matching the argument li.index(2) # => 1 li.index(4) # Raises a ValueError as 4 is not in the list
list可以进行加法运算,两个list相加表示list当中的元素合并。 等价于使用extend 方法:
# You can add lists # Note: values for li and for other_li are not modified. li + other_li # => [1, 2, 3, 4, 5, 6] # Concatenate lists with "extend()" li.extend(other_li) # Now li is [1, 2, 3, 4, 5, 6]
我们想要判断元素是否在list中出现,可以使用 in关键字 ,通过使用len计算list的长度:
# Check for existence in a list with "in" 1 in li # => True # Examine the length with "len()" len(li) # => 6
tuple
tuple和list非常接近,tuple通过()初始化。和list不同, tuple是不可变对象 。也就是说tuple一旦生成不可以改变。如果我们修改tuple,会引发TypeError异常。
# Tuples are like lists but are immutable. tup = (1, 2, 3) tup[0] # => 1 tup[0] = 3 # Raises a TypeError
由于小括号是有改变优先级的含义,所以我们定义单个元素的tuple, 末尾必须加上逗号 ,否则会被当成是单个元素:
# Note that a tuple of length one has to have a comma after the last element but # tuples of other lengths, even zero, do not. type((1)) # => <class 'int'> type((1,)) # => <class 'tuple'> type(()) # => <class 'tuple'>
tuple支持list当中绝大部分操作:
# You can do most of the list operations on tuples too len(tup) # => 3 tup + (4, 5, 6) # => (1, 2, 3, 4, 5, 6) tup[:2] # => (1, 2) 2 in tup # => True
我们可以用多个变量来解压一个tuple:
# You can unpack tuples (or lists) into variables a, b, c = (1, 2, 3) # a is now 1, b is now 2 and c is now 3 # You can also do extended unpacking a, *b, c = (1, 2, 3, 4) # a is now 1, b is now [2, 3] and c is now 4 # Tuples are created by default if you leave out the parentheses d, e, f = 4, 5, 6 # tuple 4, 5, 6 is unpacked into variables d, e and f # respectively such that d = 4, e = 5 and f = 6 # Now look how easy it is to swap two values e, d = d, e # d is now 5 and e is now 4
解释一下这行代码:
a, *b, c = (1, 2, 3, 4) # a is now 1, b is now [2, 3] and c is now 4
我们在b的前面加上了星号, 表示这是一个list 。所以Python会在将其他变量对应上值的情况下,将剩下的元素都赋值给b。
补充一点,tuple本身虽然是不可变的,但是 tuple当中的可变元素是可以改变的 。比如我们有这样一个tuple:
a = (3, [4])
我们虽然不能往a当中添加或者删除元素,但是a当中含有一个list,我们可以改变这个list类型的元素,这并不会触发tuple的异常:
a[1].append(0) # 这是合法的
dict
dict也是Python当中经常使用的容器,它等价于C++当中的map,即 存储key和value的键值对 。我们用{}表示一个dict,用:分隔key和value。
# Dictionaries store mappings from keys to values empty_dict = {} # Here is a prefilled dictionary filled_dict = {"one": 1, "two": 2, "three": 3}
dict的key必须为不可变对象,所以 list、set和dict不可以作为另一个dict的key ,否则会抛出异常:
# Note keys for dictionaries have to be immutable types. This is to ensure that # the key can be converted to a constant hash value for quick look-ups. # Immutable types include ints, floats, strings, tuples. invalid_dict = {[1,2,3]: "123"} # => Raises a TypeError: unhashable type: 'list' valid_dict = {(1,2,3):[1,2,3]} # Values can be of any type, however.
我们同样用[]查找dict当中的元素,我们传入key,获得value,等价于get方法。
# Look up values with [] filled_dict["one"] # => 1 filled_dict.get('one') #=> 1
我们可以call dict当中的keys和values方法,获取dict当中的所有key和value的集合,会得到一个list。在Python3.7以下版本当中,返回的结果的顺序可能和插入顺序不同,在Python3.7及以上版本中,Python会保证返回的顺序和插入顺序一致:
# Get all keys as an iterable with "keys()". We need to wrap the call in list() # to turn it into a list. We'll talk about those later. Note - for Python # versions <3.7, dictionary key ordering is not guaranteed. Your results might # not match the example below exactly. However, as of Python 3.7, dictionary # items maintain the order at which they are inserted into the dictionary. list(filled_dict.keys()) # => ["three", "two", "one"] in Python <3.7 list(filled_dict.keys()) # => ["one", "two", "three"] in Python 3.7+ # Get all values as an iterable with "values()". Once again we need to wrap it # in list() to get it out of the iterable. Note - Same as above regarding key # ordering. list(filled_dict.values()) # => [3, 2, 1] in Python <3.7 list(filled_dict.values()) # => [1, 2, 3] in Python 3.7+
我们也可以用in判断一个key是否在dict当中,注意只能判断key。
# Check for existence of keys in a dictionary with "in" "one" in filled_dict # => True 1 in filled_dict # => False
如果使用[]查找不存在的key,会引发KeyError的异常。如果使用 get方法则不会引起异常,只会得到一个None :
# Looking up a non-existing key is a KeyError filled_dict["four"] # KeyError # Use "get()" method to avoid the KeyError filled_dict.get("one") # => 1 filled_dict.get("four") # => None # The get method supports a default argument when the value is missing filled_dict.get("one", 4) # => 1 filled_dict.get("four", 4) # => 4
setdefault方法可以 为不存在的key 插入一个value,如果key已经存在,则不会覆盖它:
# "setdefault()" inserts into a dictionary only if the given key isn't present filled_dict.setdefault("five", 5) # filled_dict["five"] is set to 5 filled_dict.setdefault("five", 6) # filled_dict["five"] is still 5
我们可以使用update方法用另外一个dict来更新当前dict,比如a.update(b)。对于a和b交集的key会被b覆盖,a当中不存在的key会被插入进来:
# Adding to a dictionary filled_dict.update({"four":4}) # => {"one": 1, "two": 2, "three": 3, "four": 4} filled_dict["four"] = 4 # another way to add to dict
我们一样可以使用del删除dict当中的元素,同样只能传入key。
Python3.5以上的版本支持使用**来解压一个dict:
{'a': 1, **{'b': 2}} # => {'a': 1, 'b': 2} {'a': 1, **{'a': 2}} # => {'a': 2}
set
set是用来存储 不重复元素 的容器,当中的元素都是不同的,相同的元素会被删除。我们可以通过set(),或者通过{}来进行初始化。注意当我们使用{}的时候,必须要传入数据,否则Python会将它和dict弄混。
# Sets store ... well sets empty_set = set() # Initialize a set with a bunch of values. Yeah, it looks a bit like a dict. Sorry. some_set = {1, 1, 2, 2, 3, 4} # some_set is now {1, 2, 3, 4}
set当中的元素也必须是不可变对象,因此list不能传入set。
# Similar to keys of a dictionary, elements of a set have to be immutable. invalid_set = {[1], 1} # => Raises a TypeError: unhashable type: 'list' valid_set = {(1,), 1}
可以调用add方法为set插入元素:
# Add one more item to the set filled_set = some_set filled_set.add(5) # filled_set is now {1, 2, 3, 4, 5} # Sets do not have duplicate elements filled_set.add(5) # it remains as before {1, 2, 3, 4, 5}
set还可以被认为是集合,所以它还支持一些集合交叉并补的操作。
# Do set intersection with & # 计算交集 other_set = {3, 4, 5, 6} filled_set & other_set # => {3, 4, 5} # Do set union with | # 计算并集 filled_set | other_set # => {1, 2, 3, 4, 5, 6} # Do set difference with - # 计算差集 {1, 2, 3, 4} - {2, 3, 5} # => {1, 4} # Do set symmetric difference with ^ # 这个有点特殊,计算对称集,也就是去掉重复元素剩下的内容 {1, 2, 3, 4} ^ {2, 3, 5} # => {1, 4, 5}
set还支持 超集和子集的判断 ,我们可以用大于等于和小于等于号判断一个set是不是另一个的超集或子集:
# Check if set on the left is a superset of set on the right {1, 2} >= {1, 2, 3} # => False # Check if set on the left is a subset of set on the right {1, 2} <= {1, 2, 3} # => True
和dict一样,我们可以使用in判断元素在不在set当中。用copy可以拷贝一个set。
# Check for existence in a set with in 2 in filled_set # => True 10 in filled_set # => False # Make a one layer deep copy filled_set = some_set.copy() # filled_set is {1, 2, 3, 4, 5} filled_set is some_set # => False
控制流和迭代
判断语句
Python当中的判断语句非常简单,并且Python不支持switch,所以即使是多个条件,我们也只能 罗列if-else 。
# Let's just make a variable some_var = 5 # Here is an if statement. Indentation is significant in Python! # Convention is to use four spaces, not tabs. # This prints "some_var is smaller than 10" if some_var > 10: print("some_var is totally bigger than 10.") elif some_var < 10: # This elif clause is optional. print("some_var is smaller than 10.") else: # This is optional too. print("some_var is indeed 10.")
循环
我们可以用in来循环迭代一个list当中的内容,这也是Python当中基本的循环方式。
""" For loops iterate over lists prints: dog is a mammal cat is a mammal mouse is a mammal """ for animal in ["dog", "cat", "mouse"]: # You can use format() to interpolate formatted strings print("{} is a mammal".format(animal))
如果我们要循环一个范围,可以使用range。range加上一个参数表示从0开始的序列,比如range(10),表示[0, 10)区间内的所有整数:
""" "range(number)" returns an iterable of numbers from zero to the given number prints: 0 1 2 3 """ for i in range(4): print(i)
如果我们传入两个参数,则 代表迭代区间的首尾 。
""" "range(lower, upper)" returns an iterable of numbers from the lower number to the upper number prints: 4 5 6 7 """ for i in range(4, 8): print(i)
如果我们传入第三个元素,表示每次 循环变量自增的步长 。
""" "range(lower, upper, step)" returns an iterable of numbers from the lower number to the upper number, while incrementing by step. If step is not indicated, the default value is 1. prints: 4 6 """ for i in range(4, 8, 2): print(i)
如果使用enumerate函数,可以 同时迭代一个list的下标和元素 :
""" To loop over a list, and retrieve both the index and the value of each item in the list prints: 0 dog 1 cat 2 mouse """ animals = ["dog", "cat", "mouse"] for i, value in enumerate(animals): print(i, value)
while循环和C++类似,当条件为True时执行,为false时退出。并且判断条件不需要加上括号:
""" While loops go until a condition is no longer met. prints: 0 1 2 3 """ x = 0 while x < 4: print(x) x += 1 # Shorthand for x = x + 1
捕获异常
Python当中使用 try和except捕获异常 ,我们可以在except后面限制异常的类型。如果有多个类型可以写多个except,还可以使用else语句表示其他所有的类型。finally语句内的语法 无论是否会触发异常都必定执行 :
# Handle exceptions with a try/except block try: # Use "raise" to raise an error raise IndexError("This is an index error") except IndexError as e: pass # Pass is just a no-op. Usually you would do recovery here. except (TypeError, NameError): pass # Multiple exceptions can be handled together, if required. else: # Optional clause to the try/except block. Must follow all except blocks print("All good!") # Runs only if the code in try raises no exceptions finally: # Execute under all circumstances print("We can clean up resources here")
with操作
在Python当中我们经常会使用资源,最常见的就是open打开一个文件。我们 打开了文件句柄就一定要关闭 ,但是如果我们手动来编码,经常会忘记执行close操作。并且如果文件异常,还会触发异常。这个时候我们可以使用with语句来代替这部分处理,使用with会 自动在with块执行结束或者是触发异常时关闭打开的资源 。
以下是with的几种用法和功能:
# Instead of try/finally to cleanup resources you can use a with statement # 代替使用try/finally语句来关闭资源 with open("myfile.txt") as f: for line in f: print(line) # Writing to a file # 使用with写入文件 contents = {"aa": 12, "bb": 21} with open("myfile1.txt", "w+") as file: file.write(str(contents)) # writes a string to a file with open("myfile2.txt", "w+") as file: file.write(json.dumps(contents)) # writes an object to a file # Reading from a file # 使用with读取文件 with open('myfile1.txt', "r+") as file: contents = file.read() # reads a string from a file print(contents) # print: {"aa": 12, "bb": 21} with open('myfile2.txt', "r+") as file: contents = json.load(file) # reads a json object from a file print(contents) # print: {"aa": 12, "bb": 21}
可迭代对象
凡是可以使用in语句来迭代的对象都叫做 可迭代对象 ,它和迭代器不是一个含义。这里只有可迭代对象的介绍,想要了解迭代器的具体内容,请移步传送门:
当我们调用dict当中的keys方法的时候,返回的结果就是一个可迭代对象。
# Python offers a fundamental abstraction called the Iterable. # An iterable is an object that can be treated as a sequence. # The object returned by the range function, is an iterable. filled_dict = {"one": 1, "two": 2, "three": 3} our_iterable = filled_dict.keys() print(our_iterable) # => dict_keys(['one', 'two', 'three']). This is an object that implements our Iterable interface. # We can loop over it. for i in our_iterable: print(i) # Prints one, two, three
我们 不能使用下标来访问 可迭代对象,但我们可以用iter将它转化成迭代器,使用next关键字来获取下一个元素。也可以将它转化成list类型,变成一个list。
# However we cannot address elements by index. our_iterable[1] # Raises a TypeError # An iterable is an object that knows how to create an iterator. our_iterator = iter(our_iterable) # Our iterator is an object that can remember the state as we traverse through it. # We get the next object with "next()". next(our_iterator) # => "one" # It maintains state as we iterate. next(our_iterator) # => "two" next(our_iterator) # => "three" # After the iterator has returned all of its data, it raises a StopIteration exception next(our_iterator) # Raises StopIteration # We can also loop over it, in fact, "for" does this implicitly! our_iterator = iter(our_iterable) for i in our_iterator: print(i) # Prints one, two, three # You can grab all the elements of an iterable or iterator by calling list() on it. list(our_iterable) # => Returns ["one", "two", "three"] list(our_iterator) # => Returns [] because state is saved
函数
使用def关键字来定义函数,我们在传参的时候如果指定函数内的参数名, 可以不按照函数定义的顺序 传参:
# Use "def" to create new functions def add(x, y): print("x is {} and y is {}".format(x, y)) return x + y # Return values with a return statement # Calling functions with parameters add(5, 6) # => prints out "x is 5 and y is 6" and returns 11 # Another way to call functions is with keyword arguments add(y=6, x=5) # Keyword arguments can arrive in any order.
可以在参数名之前加上*表示任意长度的参数,参数会被转化成list:
# You can define functions that take a variable number of # positional arguments def varargs(*args): return args varargs(1, 2, 3) # => (1, 2, 3)
也可以指定任意长度的关键字参数,在参数前加上**表示接受一个dict:
# You can define functions that take a variable number of # keyword arguments, as well def keyword_args(**kwargs): return kwargs # Let's call it to see what happens keyword_args(big="foot", loch="ness") # => {"big": "foot", "loch": "ness"}
当然我们也可以两个都用上,这样可以接受任何参数:
# You can do both at once, if you like def all_the_args(*args, **kwargs): print(args) print(kwargs) """ all_the_args(1, 2, a=3, b=4) prints: (1, 2) {"a": 3, "b": 4} """
传入参数的时候我们也可以使用*和**来解压list或者是dict:
# When calling functions, you can do the opposite of args/kwargs! # Use * to expand tuples and use ** to expand kwargs. args = (1, 2, 3, 4) kwargs = {"a": 3, "b": 4} all_the_args(*args) # equivalent to all_the_args(1, 2, 3, 4) all_the_args(**kwargs) # equivalent to all_the_args(a=3, b=4) all_the_args(*args, **kwargs) # equivalent to all_the_args(1, 2, 3, 4, a=3, b=4)
Python中的参数 可以返回多个值 :
# Returning multiple values (with tuple assignments) def swap(x, y): return y, x # Return multiple values as a tuple without the parenthesis. # (Note: parenthesis have been excluded but can be included) x = 1 y = 2 x, y = swap(x, y) # => x = 2, y = 1 # (x, y) = swap(x,y) # Again parenthesis have been excluded but can be included.
函数内部定义的变量即使和全局变量重名,也 不会覆盖全局变量的值 。想要在函数内部使用全局变量,需要加上 global 关键字,表示这是一个全局变量:
# Function Scope x = 5 def set_x(num): # Local var x not the same as global variable x x = num # => 43 print(x) # => 43 def set_global_x(num): global x print(x) # => 5 x = num # global var x is now set to 6 print(x) # => 6 set_x(43) set_global_x(6)
Python支持 函数式编程 ,我们可以在一个函数内部返回一个函数:
# Python has first class functions def create_adder(x): def adder(y): return x + y return adder add_10 = create_adder(10) add_10(3) # => 13
Python中可以使用lambda表示 匿名函数 ,使用:作为分隔,:前面表示匿名函数的参数,:后面的是函数的返回值:
# There are also anonymous functions (lambda x: x > 2)(3) # => True (lambda x, y: x ** 2 + y ** 2)(2, 1) # => 5
我们还可以将函数作为参数使用map和filter,实现元素的批量处理和过滤。关于Python中map、reduce和filter的使用,具体可以查看之前的文章:
# There are built-in higher order functions list(map(add_10, [1, 2, 3])) # => [11, 12, 13] list(map(max, [1, 2, 3], [4, 2, 1])) # => [4, 2, 3] list(filter(lambda x: x > 5, [3, 4, 5, 6, 7])) # => [6, 7]
我们还可以结合循环和判断语来给list或者是dict进行初始化:
# We can use list comprehensions for nice maps and filters # List comprehension stores the output as a list which can itself be a nested list [add_10(i) for i in [1, 2, 3]] # => [11, 12, 13] [x for x in [3, 4, 5, 6, 7] if x > 5] # => [6, 7] # You can construct set and dict comprehensions as well. {x for x in 'abcddeef' if x not in 'abc'} # => {'d', 'e', 'f'} {x: x**2 for x in range(5)} # => {0: 0, 1: 1, 2: 4, 3: 9, 4: 16}
模块
使用 import语句引入一个Python模块 ,我们可以用.来访问模块中的函数或者是类。
# You can import modules import math print(math.sqrt(16)) # => 4.0
我们也可以使用from import的语句,单独引入模块内的函数或者是类,而不再需要写出完整路径。使用from import *可以引入模块内所有内容(不推荐这么干)
# You can get specific functions from a module from math import ceil, floor print(ceil(3.7)) # => 4.0 print(floor(3.7)) # => 3.0 # You can import all functions from a module. # Warning: this is not recommended from math import *
可以使用as给模块内的方法或者类起别名:
# You can shorten module names import math as m math.sqrt(16) == m.sqrt(16) # => True
我们可以使用dir查看我们用的模块的路径:
# You can find out which functions and attributes # are defined in a module. import math dir(math)
这么做的原因是如果我们当前的路径下也有一个叫做math的Python文件,那么 会覆盖系统自带的math的模块 。这是尤其需要注意的,不小心会导致很多奇怪的bug。
类
我们来看一个完整的类,相关的介绍都在注释当中
# We use the "class" statement to create a class class Human: # A class attribute. It is shared by all instances of this class # 类属性,可以直接通过Human.species调用,而不需要通过实例 species = "H. sapiens" # Basic initializer, this is called when this class is instantiated. # Note that the double leading and trailing underscores denote objects # or attributes that are used by Python but that live in user-controlled # namespaces. Methods(or objects or attributes) like: __init__, __str__, # __repr__ etc. are called special methods (or sometimes called dunder methods) # You should not invent such names on your own. # 最基础的构造函数 # 加了下划线的函数和变量表示不应该被用户使用,其中双下划线的函数或者是变量将不会被子类覆盖 # 前后都有双下划线的函数和属性是类当中的特殊属性 def __init__(self, name): # Assign the argument to the instance's name attribute self.name = name # Initialize property self._age = 0 # An instance method. All methods take "self" as the first argument # 类中的函数,所有实例可以调用,第一个参数必须是self # self表示实例的引用 def say(self, msg): print("{name}: {message}".format(name=self.name, message=msg)) # Another instance method def sing(self): return 'yo... yo... microphone check... one two... one two...' # A class method is shared among all instances # They are called with the calling class as the first argument @classmethod # 加上了注解,表示是类函数 # 通过Human.get_species来调用,所有实例共享 def get_species(cls): return cls.species # A static method is called without a class or instance reference @staticmethod # 静态函数,通过类名或者是实例都可以调用 def grunt(): return "*grunt*" # A property is just like a getter. # It turns the method age() into an read-only attribute of the same name. # There's no need to write trivial getters and setters in Python, though. @property # property注解,类似于get,set方法 # 效率很低,除非必要,不要使用 def age(self): return self._age # This allows the property to be set @age.setter def age(self, age): self._age = age # This allows the property to be deleted @age.deleter def age(self): del self._age
以上内容的详细介绍之前也有过相关文章,可以查看:
Python—— slots ,property和对象命名规范
下面我们来看看Python当中类的使用:
# When a Python interpreter reads a source file it executes all its code. # This __name__ check makes sure this code block is only executed when this # module is the main program. # 这个是main函数也是整个程序入口的惯用写法 if __name__ == '__main__': # Instantiate a class # 实例化一个类,获取类的对象 i = Human(name="Ian") # 执行say方法 i.say("hi") # "Ian: hi" j = Human("Joel") j.say("hello") # "Joel: hello" # i和j都是Human的实例,都称作是Human类的对象 # i and j are instances of type Human, or in other words: they are Human objects # Call our class method # 类属性被所有实例共享,一旦修改全部生效 i.say(i.get_species()) # "Ian: H. sapiens" # Change the shared attribute Human.species = "H. neanderthalensis" i.say(i.get_species()) # => "Ian: H. neanderthalensis" j.say(j.get_species()) # => "Joel: H. neanderthalensis" # 通过类名调用静态方法 # Call the static method print(Human.grunt()) # => "*grunt*" # Cannot call static method with instance of object # because i.grunt() will automatically put "self" (the object i) as an argument # 不能通过对象调用静态方法,因为对象会传入self实例,会导致不匹配 print(i.grunt()) # => TypeError: grunt() takes 0 positional arguments but 1 was given # Update the property for this instance # 实例级别的属性是独立的,各个对象各自拥有,修改不会影响其他对象内的值 i.age = 42 # Get the property i.say(i.age) # => "Ian: 42" j.say(j.age) # => "Joel: 0" # Delete the property del i.age # i.age # => this would raise an AttributeError
这里解释一下,实例和对象可以理解成一个概念,实例的英文是instance,对象的英文是object。都是指类经过实例化之后得到的对象。
继承
继承可以让子类 继承父类的变量以及方法 ,并且我们还可以在子类当中指定一些属于自己的特性,并且还可以重写父类的一些方法。一般我们会将不同的类放在不同的文件当中,使用import引入,一样可以实现继承。
from human import Human # Specify the parent class(es) as parameters to the class definition class Superhero(Human): # If the child class should inherit all of the parent's definitions without # any modifications, you can just use the "pass" keyword (and nothing else) # but in this case it is commented out to allow for a unique child class: # pass # 如果要完全继承父类的所有的实现,我们可以使用关键字pass,表示跳过。这样不会修改父类当中的实现 # Child classes can override their parents' attributes species = 'Superhuman' # Children automatically inherit their parent class's constructor including # its arguments, but can also define additional arguments or definitions # and override its methods such as the class constructor. # This constructor inherits the "name" argument from the "Human" class and # adds the "superpower" and "movie" arguments: # 子类会完全继承父类的构造方法,我们也可以进行改造,比如额外增加一些参数 def __init__(self, name, movie=False, superpowers=["super strength", "bulletproofing"]): # add additional class attributes: # 额外新增的参数 self.fictional = True self.movie = movie # be aware of mutable default values, since defaults are shared self.superpowers = superpowers # The "super" function lets you access the parent class's methods # that are overridden by the child, in this case, the __init__ method. # This calls the parent class constructor: # 子类可以通过super关键字调用父类的方法 super().__init__(name) # override the sing method # 重写父类的sing方法 def sing(self): return 'Dun, dun, DUN!' # add an additional instance method # 新增方法,只属于子类 def boast(self): for power in self.superpowers: print("I wield the power of {pow}!".format(pow=power))
if __name__ == '__main__': sup = Superhero(name="Tick") # Instance type checks # 检查继承关系 if isinstance(sup, Human): print('I am human') # 检查类型 if type(sup) is Superhero: print('I am a superhero') # Get the Method Resolution search Order used by both getattr() and super() # This attribute is dynamic and can be updated # 查看方法查询的顺序 # 先是自身,然后沿着继承顺序往上,最后到object print(Superhero.__mro__) # => (<class '__main__.Superhero'>, # => <class 'human.Human'>, <class 'object'>) # 相同的属性子类覆盖了父类 # Calls parent method but uses its own class attribute print(sup.get_species()) # => Superhuman # Calls overridden method # 相同的方法也覆盖了父类 print(sup.sing()) # => Dun, dun, DUN! # Calls method from Human # 继承了父类的方法 sup.say('Spoon') # => Tick: Spoon # Call method that exists only in Superhero # 子类特有的方法 sup.boast() # => I wield the power of super strength! # => I wield the power of bulletproofing! # Inherited class attribute sup.age = 31 print(sup.age) # => 31 # Attribute that only exists within Superhero print('Am I Oscar eligible? ' + str(sup.movie))
多继承
我们创建一个蝙蝠类:
# Another class definition # bat.py class Bat: species = 'Baty' def __init__(self, can_fly=True): self.fly = can_fly # This class also has a say method def say(self, msg): msg = '... ... ...' return msg # And its own method as well # 蝙蝠独有的声呐方法 def sonar(self): return '))) ... (((' if __name__ == '__main__': b = Bat() print(b.say('hello')) print(b.fly)
我们再创建一个蝙蝠侠的类,同时继承Superhero和Bat:
# And yet another class definition that inherits from Superhero and Bat # superhero.py from superhero import Superhero from bat import Bat # Define Batman as a child that inherits from both Superhero and Bat class Batman(Superhero, Bat): def __init__(self, *args, **kwargs): # Typically to inherit attributes you have to call super: # super(Batman, self).__init__(*args, **kwargs) # However we are dealing with multiple inheritance here, and super() # only works with the next base class in the MRO list. # So instead we explicitly call __init__ for all ancestors. # The use of *args and **kwargs allows for a clean way to pass arguments, # with each parent "peeling a layer of the onion". # 通过类名调用两个父类各自的构造方法 Superhero.__init__(self, 'anonymous', movie=True, superpowers=['Wealthy'], *args, **kwargs) Bat.__init__(self, *args, can_fly=False, **kwargs) # override the value for the name attribute self.name = 'Sad Affleck' # 重写父类的sing方法 def sing(self): return 'nan nan nan nan nan batman!'
执行这个类:
if __name__ == '__main__': sup = Batman() # Get the Method Resolution search Order used by both getattr() and super(). # This attribute is dynamic and can be updated # 可以看到方法查询的顺序是先沿着superhero这条线到human,然后才是bat print(Batman.__mro__) # => (<class '__main__.Batman'>, # => <class 'superhero.Superhero'>, # => <class 'human.Human'>, # => <class 'bat.Bat'>, <class 'object'>) # Calls parent method but uses its own class attribute # 只有superhero有get_species方法 print(sup.get_species()) # => Superhuman # Calls overridden method print(sup.sing()) # => nan nan nan nan nan batman! # Calls method from Human, because inheritance order matters sup.say('I agree') # => Sad Affleck: I agree # Call method that exists only in 2nd ancestor # 调用蝙蝠类的声呐方法 print(sup.sonar()) # => ))) ... ((( # Inherited class attribute sup.age = 100 print(sup.age) # => 100 # Inherited attribute from 2nd ancestor whose default value was overridden. print('Can I fly? ' + str(sup.fly)) # => Can I fly? False
进阶
生成器
我们可以通过yield关键字创建一个生成器,每次我们调用的时候执行到yield关键字处则停止。下次再次调用则还是从yield处开始往下执行:
# Generators help you make lazy code. def double_numbers(iterable): for i in iterable: yield i + i # Generators are memory-efficient because they only load the data needed to # process the next value in the iterable. This allows them to perform # operations on otherwise prohibitively large value ranges. # NOTE: `range` replaces `xrange` in Python 3. for i in double_numbers(range(1, 900000000)): # `range` is a generator. print(i) if i >= 30: break
除了yield之外,我们还可以使用()小括号来生成一个生成器:
# Just as you can create a list comprehension, you can create generator # comprehensions as well. values = (-x for x in [1,2,3,4,5]) for x in values: print(x) # prints -1 -2 -3 -4 -5 to console/terminal # You can also cast a generator comprehension directly to a list. values = (-x for x in [1,2,3,4,5]) gen_to_list = list(values) print(gen_to_list) # => [-1, -2, -3, -4, -5]
关于生成器和迭代器更多的内容,可以查看下面这篇文章:
装饰器
我们引入functools当中的wraps之后,可以创建一个装饰器。装饰器可以在不修改函数内部代码的前提下,在外面包装一层其他的逻辑:
# Decorators # In this example `beg` wraps `say`. If say_please is True then it # will change the returned message. from functools import wraps def beg(target_function): @wraps(target_function) # 如果please为True,额外输出一句Please! I am poor :( def wrapper(*args, **kwargs): msg, say_please = target_function(*args, **kwargs) if say_please: return "{} {}".format(msg, "Please! I am poor :(") return msg return wrapper @beg def say(say_please=False): msg = "Can you buy me a beer?" return msg, say_please print(say()) # Can you buy me a beer? print(say(say_please=True)) # Can you buy me a beer? Please! I am poor :(
装饰器之前也有专门的文章详细介绍,可以移步下面的传送门:
结尾
不知道有多少小伙伴可以看到结束,原作者的确非常厉害,把Python的基本操作基本上都囊括在里面了。如果都能读懂并且理解的话,那么Python这门语言就算是入门了。
原作者写的是一个 Python文件 ,所有的内容都在Python的注释当中。我在它的基础上做了修补和额外的描述。如果想要获得原文,可以点击查看原文,或者是在公众号内回复 learnpython 获取。
如果你之前就有其他语言的语言基础,我想本文读完应该不用30分钟。当然在30分钟内学会一门语言是不可能的,也不是我所提倡的。但至少通过本文我们可以做到熟悉Python的语法,知道大概有哪些操作,剩下的就要我们亲自去写代码的时候去体会和运用了。
根据我的经验,在学习一门新语言的前期,不停地查阅资料是免不了的。希望本文可以作为你在使用Python时候的查阅文档。
今天的文章就到这里,原创不易,需要你的 一个关注 ,你的举手之劳对我来说很重要。
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