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True1， 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：
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，它集成超级英雄类和蝙蝠类这两个类。
# 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 :(
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