--- language: python3 contributors: - ["Louie Dinh", "http://pythonpracticeprojects.com"] - ["Steven Basart", "http://github.com/xksteven"] - ["Andre Polykanine", "https://github.com/Oire"] translators: - ["Geoff Liu", "http://geoffliu.me"] filename: learnpython3.py --- 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](http://twitter.com/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 ```python # 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 #################################################### # 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 :( ``` ## Ready For More? ### Free Online * [Learn Python The Hard Way](http://learnpythonthehardway.org/book/) * [Dive Into Python](http://www.diveintopython.net/) * [Ideas for Python Projects](http://pythonpracticeprojects.com) * [The Official Docs](http://docs.python.org/3/) * [Hitchhiker's Guide to Python](http://docs.python-guide.org/en/latest/) * [Python Module of the Week](http://pymotw.com/3/) * [A Crash Course in Python for Scientists](http://nbviewer.ipython.org/5920182) ### Dead Tree * [Programming Python](http://www.amazon.com/gp/product/0596158106/ref=as_li_qf_sp_asin_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=0596158106&linkCode=as2&tag=homebits04-20) * [Dive Into Python](http://www.amazon.com/gp/product/1441413022/ref=as_li_tf_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=1441413022&linkCode=as2&tag=homebits04-20) * [Python Essential Reference](http://www.amazon.com/gp/product/0672329786/ref=as_li_tf_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=0672329786&linkCode=as2&tag=homebits04-20)