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。想要查看英文原文的同学，请点击查看原文。
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 # => 28 - 1 # => 710 * 2 # => 2035 / 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 # => -25.0 // 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 operation7 % 3 # => 1Python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 parentheses1 + 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 # => TrueFalse # => 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 notnot True # => Falsenot False # => True# Boolean Operators# Note "and" and "or" are case-sensitiveTrue and False # => FalseFalse 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 keywordsTrue + True # => 2True * 8 # => 8False - 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 False0 == False # => True1 == True # => True2 == 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) # => Falsebool(4) # => Truebool(-6) # => True0 and 2 # => 0-5 or 0 # => -5Python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 # => True2 == 1 # => False# Inequality is !=1 != 1 # => False2 != 1 # => True# More comparisons1 < 10 # => True1 > 10 # => False2 <= 2 # => True2 >= 2 # => True我们可以用and和or拼装各个比较操作：
# Seeing whether a value is in a range1 < 2 and 2 < 3 # => True2 < 3 and 3 < 2 # => False# Chaining makes this look nicer1 < 2 < 3 # => True2 < 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使用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 tob is a # => True, a and b refer to the same objectb == a # => True, a's and b's objects are equalb = [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 objectb == a # => True, a's and b's objects are equalPython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 stringlen("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。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 objectNone # => 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 # => FalseNone 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 Truebool(None)# => Falsebool(0) # => Falsebool("") # => Falsebool([]) # => Falsebool({}) # => Falsebool(()) # => 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 functionprint("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 consoleinput_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_underscoressome_var = 5some_var # => 5# Accessing a previously unassigned variable is an exception.# See Control Flow to learn more about exception handling.some_unknown_var # Raises a NameErrorPython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 sequencesli = []# You can start with a prefilled listother_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 appendli.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 popli.pop() # => 3 and li is now [1, 2, 4]# Let's put it backli.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表示导数第二个，以此类推。如果访问的元素超过数组长度，则会触发IndexError的错误。
# Access a list like you would any arrayli[0] # => 1# Look at the last elementli[-1] # => 3# Looking out of bounds is an IndexErrorli[4] # Raises an IndexErrorlist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]区间的倒叙，应该写成[6:3:-1]。
只写一个:，表示全部获取，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 slicesli2 = 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 valueli.remove(2) # li is now [1, 3]li.remove(2) # Raises a ValueError as 2 is not in the listinsert方法可以执行指定位置插入元素，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 indexli.insert(1, 2) # li is now [1, 2, 3] again# Get the index of the first item found matching the argumentli.index(2) # => 1li.index(4) # Raises a ValueError as 4 is not in the listlist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] # => 1tup[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 toolen(tup) # => 3tup + (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 variablesa, b, c = (1, 2, 3) # a is now 1, b is now 2 and c is now 3# You can also do extended unpackinga, *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 parenthesesd, 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 valuese, 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 valuesempty_dict = {}# Here is a prefilled dictionaryfilled_dict = {"one": 1, "two": 2, "three": 3}dict的key必须为不可变对象，所以list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"] # => 1filled_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 <3.7list(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 <3.7list(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 # => True1 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 KeyErrorfilled_dict["four"] # KeyError# Use "get()" method to avoid the KeyErrorfilled_dict.get("one") # => 1filled_dict.get("four") # => None# The get method supports a default argument when the value is missingfilled_dict.get("one", 4) # => 1filled_dict.get("four", 4) # => 4setdefault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 presentfilled_dict.setdefault("five", 5) # filled_dict["five"] is set to 5filled_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 dictionaryfilled_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 setsempty_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 setfilled_set = some_setfilled_set.add(5) # filled_set is now {1, 2, 3, 4, 5}# Sets do not have duplicate elementsfilled_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 in2 in filled_set # => True10 in filled_set # => False# Make a one layer deep copyfilled_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 variablesome_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 listsprints: 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 numbersfrom zero to the given numberprints: 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 numbersfrom the lower number to the upper numberprints: 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 numbersfrom the lower number to the upper number, while incrementingby 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 listprints: 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 = 0while 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 blocktry: # 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 exceptionsfinally: # 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 filewith 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 fileprint(contents)# print: {"aa": 12, "bb": 21}with open('myfile2.txt', "r+") as file: contents = json.load(file) # reads a json object from a fileprint(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：
Python——五分钟带你弄懂迭代器与生成器，夯实代码能力
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 exceptionnext(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 functionsdef add(x, y): print("x is {} and y is {}".format(x, y)) return x + y # Return values with a return statement# Calling functions with parametersadd(5, 6) # => prints out "x is 5 and y is 6" and returns 11# Another way to call functions is with keyword argumentsadd(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 argumentsdef varargs(*args): return argsvarargs(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 welldef keyword_args(**kwargs): return kwargs# Let's call it to see what happenskeyword_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 likedef 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 = 1y = 2x, 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 Scopex = 5def set_x(num): # Local var x not the same as global variable x x = num # => 43 print(x) # => 43def set_global_x(num): global x print(x) # => 5 x = num # global var x is now set to 6 print(x) # => 6set_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 functionsdef create_adder(x): def adder(y): return x + y return adderadd_10 = create_adder(10)add_10(3) # => 13Python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：
Python专题——五分钟带你了解map、reduce和filter
# There are built-in higher order functionslist(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 modulesimport mathprint(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 modulefrom math import ceil, floorprint(ceil(3.7)) # => 4.0print(floor(3.7)) # => 3.0# You can import all functions from a module.# Warning: this is not recommendedfrom math import *可以使用as给模块内的方法或者类起别名：
# You can shorten module namesimport math as mmath.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 mathdir(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 classclass 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 definitionclass 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.pyclass 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.pyfrom superhero import Superherofrom bat import Bat# Define Batman as a child that inherits from both Superhero and Batclass 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：
Python——五分钟带你弄懂迭代器与生成器，夯实代码能力
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 wrapsdef 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@begdef say(say_please=False): msg = "Can you buy me a beer?" return msg, say_pleaseprint(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：
一文搞定Python装饰器，看完面试不再慌
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。