learnxinyminutes-docs/python3-cn.html.markdown at ba5c351f216ca63e4f46230db683224fd6d26355

Python was created by Guido Van Rossum in the early 90's. It is now one of the most popular languages in existence. I fell in love with Python for its syntactic clarity. It's basically executable pseudocode.
Feedback would be highly appreciated! You can reach me at @louiedinh or louiedinh [at] [google's email service]
Note: This article applies to Python 3 specifically. Check out the other tutorial if you want to learn the old Python 2.7

# 用井字符开头的是单行注释

""" 多行字符串用三个引号
    包裹，也常被用来做多
    行注释
"""

####################################################
## 1. 原始数据类型和运算符
####################################################

# 整数
3  # => 3

# 算术没有什么出乎意料的
1 + 1  # => 2
8 - 1  # => 7
10 * 2  # => 20

# 但是除法例外，会自动转换成浮点数
35 / 5  # => 7.0
5 / 3  # => 1.6666666666666667

# 整数除法的结果都是向下取整
5 // 3     # => 1
5.0 // 3.0 # => 1.0 # 浮点数也可以
-5 // 3  # => -2
-5.0 // 3.0 # => -2.0

# 浮点数的运算结果也是浮点数
3 * 2.0 # => 6.0

# 模除
7 % 3 # => 1

# x的y次方
2**4 # => 16

# 用括号决定优先级
(1 + 3) * 2  # => 8

# 布尔值
True
False

# 用not取非
not True  # => False
not False  # => True

# 逻辑运算符，注意and和or都是小写
True and False #=> False
False or True #=> True

# 整数也可以当作布尔值
0 and 2 #=> 0
-5 or 0 #=> -5
0 == False #=> True
2 == True #=> False
1 == True #=> True

# 用==判断相等
1 == 1  # => True
2 == 1  # => False

# 用!=判断不等
1 != 1  # => False
2 != 1  # => True

# 比较大小
1 < 10  # => True
1 > 10  # => False
2 <= 2  # => True
2 >= 2  # => True

# 大小比较可以连起来！
1 < 2 < 3  # => True
2 < 3 < 2  # => False

# 字符串用单引双引都可以
"这是个字符串"
'这也是个字符串'

# 用加号连接字符串
"Hello " + "world!"  # => "Hello world!"

# 字符串可以被当作字符列表
"This is a string"[0]  # => 'T'

# 用.format来格式化字符串
"{} can be {}".format("strings", "interpolated")

# 可以重复参数以节省时间
"{0} be nimble, {0} be quick, {0} jump over the {1}".format("Jack", "candle stick")
#=> "Jack be nimble, Jack be quick, Jack jump over the candle stick"

# 如果不想数参数，可以用关键字
"{name} wants to eat {food}".format(name="Bob", food="lasagna") #=> "Bob wants to eat lasagna"

# 如果你的Python3程序也要在Python2.5以下环境运行，也可以用老式的格式化语法
"%s can be %s the %s way" % ("strings", "interpolated", "old")

# None是一个对象
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

# None，0，空字符串，空列表，空关联数组都算是False
# 所有其他值都是True
bool(0)  # => False
bool("")  # => False
bool([]) #=> False
bool({}) #=> False


####################################################
## 2. 变量和集合
####################################################

# print是内置的打印函数
print("I'm Python. Nice to meet you!")

# 在给变量赋值前不用提前声明
# 传统的变量命名是小写，用下划线分隔单词
some_var = 5
some_var  # => 5

# 存取未赋值的变量会抛出异常
# 下面流程控制一段更深入讲解异常处理
some_unknown_var  # 抛出NameError

# 用列表(list)储存序列
li = []
# 创建列表时也可以同时赋给元素
other_li = [4, 5, 6]

# 用append在列表最后追加元素
li.append(1)    # li现在是[1]
li.append(2)    # li现在是[1, 2]
li.append(4)    # li现在是[1, 2, 4]
li.append(3)    # li现在是[1, 2, 4, 3]
# 用pop从列表尾部删除
li.pop()        # => 3 且li现在是[1, 2, 4]
# 把3再放回去
li.append(3)    # li变回[1, 2, 4, 3]

# 列表取值跟数组一样
li[0]  # => 1
# 取出最后一个元素
li[-1]  # => 3

# 越界读取会造成IndexError
li[4]  # 抛出IndexError

# 列表的切割语法
# (It's a closed/open range for you mathy types.)
li[1:3]  # => [2, 4]
# 取尾
li[2:]  # => [4, 3]
# 取头
li[:3]  # => [1, 2, 4]
# 每两个取一个
li[::2]   # =>[1, 4]
# 倒排列表
li[::-1]   # => [3, 4, 2, 1]
# Use any combination of these to make advanced slices
# li[start:end:step]

# 用del删除任何一个元素
del li[2]   # li is now [1, 2, 3]

# 列表可以相加
# 注意：li和other_li的值都不变
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


# Tuples are like lists but are immutable.
tup = (1, 2, 3)
tup[0]   # => 1
tup[0] = 3  # Raises a TypeError

# 列表允许的操作元组也可以
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
# Tuples are created by default if you leave out the parentheses
d, e, f = 4, 5, 6
# Now look how easy it is to swap two values
e, d = d, e     # d is now 5 and e is now 4


# Dictionaries store mappings
empty_dict = {}
# Here is a prefilled dictionary
filled_dict = {"one": 1, "two": 2, "three": 3}

# Look up values with []
filled_dict["one"]   # => 1

# Get all keys as a list with "keys()".
# We need to wrap the call in list() because we are getting back an iterable. We'll talk about those later.
# Note - Dictionary key ordering is not guaranteed.
# Your results might not match this exactly.
list(filled_dict.keys())   # => ["three", "two", "one"]


# Get all values as a list 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]


# 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()" 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

# 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

# Remove keys from a dictionary with del
del filled_dict["one"]  # Removes the key "one" from filled dict


# 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}

#Can set new variables to a set
filled_set = some_set

# Add one more item to the set
filled_set.add(5)   # filled_set is now {1, 2, 3, 4, 5}

# Do set intersection with &
other_set = {3, 4, 5, 6}
filled_set & other_set   # => {3, 4, 5}

# Do set union with |
filled_set | other_set   # => {1, 2, 3, 4, 5, 6}

# Do set difference with -
{1, 2, 3, 4} - {2, 3, 5}   # => {1, 4}

# Check for existence in a set with in
2 in filled_set   # => True
10 in filled_set   # => False


####################################################
## 3. 流程控制和迭代器
####################################################

# 先随便定义一个变量
some_var = 5

# 这是个if语句。注意缩进在Python里是有意义的
# 印出"some_var比10小"
if some_var > 10:
    print("some_var比10大")
elif some_var < 10:    # elif句是可选的
    print("some_var比10小")
else:                  # else也是可选的
    print("some_var就是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 a list of numbers
from zero to the given number
prints:
    0
    1
    2
    3
"""
for i in range(4):
    print(i)

"""
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

# 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

# Python offers a fundamental abstraction called the Iterable.
# An iterable is an object that can be treated as a sequence.
# The object returned the range function, is an iterable.

filled_dict = {"one": 1, "two": 2, "three": 3}
our_iterable = filled_dict.keys()
print(our_iterable) #=> range(1,10). 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)

# Our iterator is an object that can remember the state as we traverse through it.
# We get the next object by calling the __next__ function.
our_iterator.__next__()  #=> "one"

# It maintains state as we call __next__.
our_iterator.__next__()  #=> "two"
our_iterator.__next__()  #=> "three"

# After the iterator has returned all of its data, it gives you a StopIterator Exception
our_iterator.__next__() # Raises StopIteration

# You can grab all the elements of an iterator by calling list() on it.
list(filled_dict.keys())  #=> Returns ["one", "two", "three"]



####################################################
## 4. 函数
####################################################

# 用def定义新函数
def add(x, y):
    print("x is {} and y is {}".format(x, y))
    return x + y    # 用return语句返回

# 调用函数
add(5, 6)   # => 印出"x is 5 and y is 6"并且返回11

# 也可以用关键字参数来调用函数
add(y=6, x=5)   # 关键字参数可以用任何顺序


# You can define functions that take a variable number of
# positional arguments
def varargs(*args):
    return args

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


# 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"}


# 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}
"""

# 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 foo(1, 2, 3, 4)
all_the_args(**kwargs)   # equivalent to foo(a=3, b=4)
all_the_args(*args, **kwargs)   # equivalent to foo(1, 2, 3, 4, a=3, b=4)


# Function Scope
x = 5

def setX(num):
    # Local var x not the same as global variable x
    x = num # => 43
    print (x) # => 43

def setGlobalX(num):
    global x
    print (x) # => 5
    x = num # global var x is now set to 6
    print (x) # => 6

setX(43)
setGlobalX(6)


# 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

# There are also anonymous functions
(lambda x: x > 2)(3)   # => True

# TODO - Fix for iterables
# There are built-in higher order functions
map(add_10, [1, 2, 3])   # => [11, 12, 13]
filter(lambda x: x > 5, [3, 4, 5, 6, 7])   # => [6, 7]

# 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]

####################################################
## 5. 类
####################################################


# We subclass from object to get a class.
class Human(object):

    # A class attribute. It is shared by all instances of this class
    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. You should not invent such names on your own.
    def __init__(self, name):
        # Assign the argument to the instance's name attribute
        self.name = name

    # An instance method. All methods take "self" as the first argument
    def say(self, msg):
        return "{name}: {message}".format(name=self.name, message=msg)

    # A class method is shared among all instances
    # They are called with the calling class as the first argument
    @classmethod
    def get_species(cls):
        return cls.species

    # A static method is called without a class or instance reference
    @staticmethod
    def grunt():
        return "*grunt*"


# Instantiate a class
i = Human(name="Ian")
print(i.say("hi"))     # prints out "Ian: hi"

j = Human("Joel")
print(j.say("hello"))  # prints out "Joel: hello"

# Call our class method
i.get_species()   # => "H. sapiens"

# Change the shared attribute
Human.species = "H. neanderthalensis"
i.get_species()   # => "H. neanderthalensis"
j.get_species()   # => "H. neanderthalensis"

# Call the static method
Human.grunt()   # => "*grunt*"


####################################################
## 6. 模块
####################################################

# You can import modules
import math
print(math.sqrt(16))  # => 4

# 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 *

# You can shorten module names
import math as m
math.sqrt(16) == m.sqrt(16)   # => True

# Python modules are just ordinary python files. You
# can write your own, and import them. The name of the
# module is the same as the name of the file.

# You can find out which functions and attributes
# defines a module.
import math
dir(math)


####################################################
## 7. 高级用法
####################################################

# Generators help you make lazy code
def double_numbers(iterable):
    for i in iterable:
        yield i + i

# A generator creates values on the fly.
# Instead of generating and returning all values at once it creates one in each
# iteration.  This means values bigger than 15 wont be processed in
# double_numbers.
# Note range is a generator too. Creating a list 1-900000000 would take lot of
# time to be made
# We use a trailing underscore in variable names when we want to use a name that
# would normally collide with a python keyword
range_ = range(1, 900000000)
# will double all numbers until a result >=30 found
for i in double_numbers(range_):
    print(i)
    if i >= 30:
        break


# Decorators
# in this example beg wraps say
# Beg will call say. If say_please is True then it will change the returned
# message
from functools import wraps


def beg(target_function):
    @wraps(target_function)
    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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