【万字长文】别再报班了，一篇文章带你入门Python

【万字长文】别再报班了，一篇文章带你入门Python

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。

""" 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

"""

Python当中的数字定义和其他语言一样：

3

获得一个浮点数

10.0

1 + 1 # => 2
8 - 1 # => 7
10 * 2 # => 20
35 / 5 # => 7.0

5 // 3 # => 1
-5 // 3 # => -2
5.0 // 3.0 # => 1.0 # works on floats too
-5.0 // 3.0 # => -2.0

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：

2**3 # => 8

Enforce precedence with parentheses

1 + 3 * 2 # => 7
(1 + 3) * 2 # => 8

Python中用首字母大写的True和False表示真和假。

True # => True
False # => False

negate with not

not True # => False
not False # => True

Note "and" and "or" are case-sensitive

True and False # => False
False or True # => True

True and False are actually 1 and 0 but with different keywords

True + True # => 2
True * 8 # => 8
False - 5 # => -5

Comparison operators look at the numerical value of True and False

0 == False # => True
1 == True # => True
2 == True # => False
-5 != False # => 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

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

list和字符串

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。

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。

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!"

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

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."

None # => None

Use "is" instead. This checks for equality of object identity.

"etc" is None # => False
None is None # => True

All other values are True

bool(None)# => False
bool(0) # => False
bool("") # => False
bool([]) # => False
bool({}) # => False
bool(()) # => False

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!

Use the optional argument end to change the end string.

print("Hello, World", end="!") # => Hello, World!

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()

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。

some_var = 5
some_var # => 5

Accessing a previously unassigned variable is an exception.

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：

Equivalent of C's '?:' ternary operator

"yahoo!" if 3 > 2 else 2 # => "yahoo!"

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 = []

other_li = [4, 5, 6]

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。

li[0] # => 1

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。

(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]

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：

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]

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

tuples of other lengths, even zero, do not.

type((1)) # =>
type((1,)) # =>
type(()) # =>
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

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

Now look how easy it is to swap two values

e, d = d, e # d is now 5 and e is now 4

a, *b, c = (1, 2, 3, 4) # a is now 1, b is now [2, 3] and c is now 4

a = (3, [4])

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。

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：

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.

Look up values with []

filled_dict["one"] # => 1
filled_dict.get('one') #=> 1

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+

ordering.

list(filled_dict.values()) # => [3, 2, 1] in Python <3.7
list(filled_dict.values()) # => [1, 2, 3] in Python 3.7+

Check for existence of keys in a dictionary with "in"

"one" in filled_dict # => True
1 in filled_dict # => False

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

filled_dict.update({"four":4}) # => {"one": 1, "two": 2, "three": 3, "four": 4}
filled_dict["four"] = 4 # another way to add to dict

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}

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。

计算交集

other_set = {3, 4, 5, 6}
filled_set & other_set # => {3, 4, 5}

计算并集

filled_set | other_set # => {1, 2, 3, 4, 5, 6}

计算差集

{1, 2, 3, 4} - {2, 3, 5} # => {1, 4}

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

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

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。

some_var = 5

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.")

"""
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))

"""
"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)

"""
"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)

"""
"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)

"""
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

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

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)


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


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}

Python——五分钟带你弄懂迭代器与生成器，夯实代码能力

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

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)

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

Use "def" to create new functions

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.

positional arguments

def varargs(*args):

return args


varargs(1, 2, 3) # => (1, 2, 3)

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"}

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}

"""

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

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 adder(y):
return x + y


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

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]

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}

You can import modules

import math
print(math.sqrt(16)) # => 4.0

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

Warning: this is not recommended

from math import *

You can shorten module names

import math as m
math.sqrt(16) == m.sqrt(16) # => True

import math
dir(math)

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

Python——slots，property和对象命名规范

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

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"]):

# 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!'

# 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))

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)

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

# 重写父类的sing方法
def sing(self):
return 'nan nan nan nan nan batman!'

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

def double_numbers(iterable):

for i in iterable:
yield i + i


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

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]

will change the returned message.

from functools import wraps

def beg(target_function):

@wraps(target_function)
def wrapper(*args, **kwargs):
return "{} {}".format(msg, "Please! I am poor :(")
return msg

return wrapper


@beg

msg = "Can you buy me a beer?"


print(say()) # Can you buy me a beer?

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