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| language | contributors | translators | filename | ||||||||||||
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| python3 |
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learnpython3.py |
Python是由吉多·范罗苏姆(Guido Van Rossum)在90年代早期设计。它是如今最常用的编程 语言之一。它的语法简洁且优美，几乎就是可执行的伪代码。
欢迎大家斧正。英文版原作Louie Dinh @louiedinh 或着Email louiedinh [at] [谷歌的信箱服务]。中文翻译Geoff Liu。
注意：这篇教程是特别为Python3写的。如果你想学旧版Python2，我们特别有另一篇教程。
# 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，空字典都算是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
# 访问未赋值的变量会抛出异常
# 参考流程控制一段来学习异常处理
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]
# 列表存取跟数组一样
li[0] # => 1
# 取出最后一个元素
li[-1] # => 3
# 越界存取会造成IndexError
li[4] # 抛出IndexError
# 列表有切割语法
li[1:3] # => [2, 4]
# 取尾
li[2:] # => [4, 3]
# 取头
li[:3] # => [1, 2, 4]
# 隔一个取一个
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]
# 用extend拼接列表
li.extend(other_li) # li现在是[1, 2, 3, 4, 5, 6]
# 用in测试列表是否包含值
1 in li # => True
# 用len取列表长度
len(li) # => 6
# 元组是不可改变的序列
tup = (1, 2, 3)
tup[0] # => 1
tup[0] = 3 # 抛出TypeError
# 列表允许的操作元组大都可以
len(tup) # => 3
tup + (4, 5, 6) # => (1, 2, 3, 4, 5, 6)
tup[:2] # => (1, 2)
2 in tup # => True
# 可以把元组合列表解包，赋值给变量
a, b, c = (1, 2, 3) # 现在a是1，b是2，c是3
# 元组周围的括号是可以省略的
d, e, f = 4, 5, 6
# 交换两个变量的值就这么简单
e, d = d, e # 现在d是5，e是4
# 用字典表达映射关系
empty_dict = {}
# 初始化的字典
filled_dict = {"one": 1, "two": 2, "three": 3}
# 用[]取值
filled_dict["one"] # => 1
# 用keys获得所有的键。因为keys返回一个可迭代对象，所以在这里把结果包在list里。我们下面会详细介绍可迭代。
# 注意：字典键的顺序是不定的，你得到的结果可能和以下不同。
list(filled_dict.keys()) # => ["three", "two", "one"]
# 用values获得所有的值。跟keys一样，要用list包起来，顺序也可能不同。
list(filled_dict.values()) # => [3, 2, 1]
# 用in测试一个字典是否包含一个键
"one" in filled_dict # => True
1 in filled_dict # => False
# 访问不存在的键会导致KeyError
filled_dict["four"] # KeyError
# 用get来避免KeyError
filled_dict.get("one") # => 1
filled_dict.get("four") # => None
# 当键不存在的时候get方法可以返回默认值
filled_dict.get("one", 4) # => 1
filled_dict.get("four", 4) # => 4
# setdefault方法只有当键不存在的时候插入新值
filled_dict.setdefault("five", 5) # filled_dict["five"]设为5
filled_dict.setdefault("five", 6) # filled_dict["five"]还是5
# 字典赋值
filled_dict.update({"four":4}) #=> {"one": 1, "two": 2, "three": 3, "four": 4}
filled_dict["four"] = 4 # 另一种赋值方法
# 用del删除
del filled_dict["one"] # 从filled_dict中把one删除
# 用set表达集合
empty_set = set()
# 初始化一个集合，语法跟字典相似。
some_set = {1, 1, 2, 2, 3, 4} # some_set现在是{1, 2, 3, 4}
# 可以把集合赋值于变量
filled_set = some_set
# 为集合添加元素
filled_set.add(5) # filled_set现在是{1, 2, 3, 4, 5}
# & 取交集
other_set = {3, 4, 5, 6}
filled_set & other_set # => {3, 4, 5}
# | 取并集
filled_set | other_set # => {1, 2, 3, 4, 5, 6}
# - 取补集
{1, 2, 3, 4} - {2, 3, 5} # => {1, 4}
# in 测试集合是否包含元素
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")
"""
用for循环语句遍历列表
打印:
dog is a mammal
cat is a mammal
mouse is a mammal
"""
for animal in ["dog", "cat", "mouse"]:
print("{} is a mammal".format(animal))
"""
"range(number)"返回数字列表从0到给的数字
打印:
0
1
2
3
"""
for i in range(4):
print(i)
"""
while循环直到条件不满足
打印:
0
1
2
3
"""
x = 0
while x < 4:
print(x)
x += 1 # x = x + 1 的简写
# try/except块处理异常状况
try:
# 用raise来抛出异常
raise IndexError("This is an index error")
except IndexError as e:
pass # pass是无操作，但是应该在这里处理错误
except (TypeError, NameError):
pass # 可以同时处理不同类的错误
else: # else语句是可选的，必须在所有的except之后
print("All good!") # 只有当try运行完没有错误的时候这句才会运行
# Python提供一个叫做可迭代(iterable)的基本抽象。一个可迭代对象是可以被当作序列
# 的对象。比如说上面range返回的对象就是可迭代的。
filled_dict = {"one": 1, "two": 2, "three": 3}
our_iterable = filled_dict.keys()
print(our_iterable) # => range(1,10) 是一个实现可迭代接口的对象
# 可迭代对象可以遍历
for i in our_iterable:
print(i) # 打印 one, two, three
# 但是不可以随机访问
our_iterable[1] # 抛出TypeError
# 可迭代对象知道怎么生成迭代器
our_iterator = iter(our_iterable)
# 迭代器是一个可以记住遍历的位置的对象
# 用__next__可以取得下一个元素
our_iterator.__next__() #=> "one"
# 再一次调取__next__时会记得位置
our_iterator.__next__() #=> "two"
our_iterator.__next__() #=> "three"
# 当迭代器所有元素都取出后，会抛出StopIteration
our_iterator.__next__() # 抛出StopIteration
# 可以用list一次取出迭代器所有的元素
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
####################################################
# 用import导入模块
import math
print(math.sqrt(16)) # => 4
# 也可以从模块中导入个别值
from math import ceil, floor
print(ceil(3.7)) # => 4.0
print(floor(3.7)) # => 3.0
# 可以导入一个模块中所有值
# 警告：不建议这么做
from math import *
# 如此缩写模块名字
import math as m
math.sqrt(16) == m.sqrt(16) # => True
# Python模块其实就是普通的Python文件。你可以自己写，然后导入，
# 模块的名字就是文件的名字。
# 你可以这样列出一个模块里所有的值
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 :(