转自:https://learnxinyminutes.com/docs/python3/
Python was created by Guido van Rossum in the early 90s. 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 here if you want to learn the old Python 2.7
# Single line comments start with a number symbol. """ Multiline strings can be written using three "s, and are often used as documentation. """ #################################################### ## 1. Primitive Datatypes and Operators #################################################### # You have numbers 3 # => 3 # Math is what you would expect 1 + 1 # => 2 8 - 1 # => 7 10 * 2 # => 20 35 / 5 # => 7.0 # Result of integer division truncated down both for positive and negative. 5 // 3 # => 1 5.0 // 3.0 # => 1.0 # works on floats too -5 // 3 # => -2 -5.0 // 3.0 # => -2.0 # The result of division is always a float 10.0 / 3 # => 3.3333333333333335 # Modulo operation 7 % 3 # => 1 # Exponentiation (x**y, x to the yth power) 2**3 # => 8 # Enforce precedence with parentheses (1 + 3) * 2 # => 8 # Boolean values are primitives (Note: the capitalization) True False # 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 # Note using Bool operators with ints # False is 0 and True is 1 # Don't mix up with bool(ints) and bitwise and/or (&,|) 0 and 2 # => 0 -5 or 0 # => -5 0 == False # => True 2 == True # => False 1 == True # => True -5 != False != True #=> True # 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 # Comparisons can be chained! 1 < 2 < 3 # => True 2 < 3 < 2 # => False # (is vs. ==) is checks if two variables refer to the same object, but == checks # if the objects pointed to have the same values. 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 # 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 # .format can be used to format strings, like this: "{} can be {}".format("Strings", "interpolated") # => "Strings can be interpolated" # You can repeat the formatting arguments to save some typing. "{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" # You can use keywords if you don't want to count. "{name} wants to eat {food}".format(name="Bob", food="lasagna") # => "Bob wants to eat lasagna" # If your Python 3 code also needs to run on Python 2.5 and below, you can also # still use the old style of formatting: "%s can be %s the %s way" % ("Strings", "interpolated", "old") # => "Strings can be interpolated the old way" # 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 # None, 0, and empty strings/lists/dicts/tuples all evaluate to False. # All other values are True bool(0) # => False bool("") # => False bool([]) # => False bool({}) # => False bool(()) # => False #################################################### ## 2. Variables and Collections #################################################### # 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! # 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() # 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 # if can be used as an expression # Equivalent of C's '?:' ternary operator "yahoo!" if 3 > 2 else 2 # => "yahoo!" # Lists store sequences li = [] # You can start with a prefilled list 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. # 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 # 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] # => [2, 4] # Omit the beginning and return the list li[2:] # => [4, 3] # Omit the end and return the list li[:3] # => [1, 2, 4] # Select every second entry li[::2] # =>[1, 4] # Return a reversed copy of the list li[::-1] # => [3, 4, 2, 1] # Use any combination of these to make advanced slices # li[start:end:step] # 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 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 # 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] # 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 # 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'> # 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 # 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 from keys to values empty_dict = {} # Here is a prefilled dictionary filled_dict = {"one": 1, "two": 2, "three": 3} # 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. # Look up values with [] filled_dict["one"] # => 1 # 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 - Dictionary key # ordering is not guaranteed. Your results might not match this exactly. list(filled_dict.keys()) # => ["three", "two", "one"] # 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] # 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 # From Python 3.5 you can also use the additional unpacking options {'a': 1, **{'b': 2}} # => {'a': 1, 'b': 2} {'a': 1, **{'a': 2}} # => {'a': 2} # 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} # 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} # 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} # Do set symmetric difference with ^ {1, 2, 3, 4} ^ {2, 3, 5} # => {1, 4, 5} # 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 #################################################### ## 3. Control Flow and Iterables #################################################### # 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.") """ 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) """ 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 finally: # Execute under all circumstances print("We can clean up resources here") # Instead of try/finally to cleanup resources you can use a with statement with open("myfile.txt") as f: for line in f: print(line) # 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 # 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 # You can grab all the elements of an iterator by calling list() on it. list(filled_dict.keys()) # => Returns ["one", "two", "three"] #################################################### ## 4. Functions #################################################### # 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. # 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 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) # 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. # 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 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 (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] # 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} #################################################### ## 5. Modules #################################################### # 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 # 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 # are defined in a module. import math dir(math) # If you have a Python script named math.py in the same # folder as your current script, the file math.py will # be loaded instead of the built-in Python module. # This happens because the local folder has priority # over Python's built-in libraries. #################################################### ## 6. Classes #################################################### # We use the "class" statement to create a class class Human: # 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. 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. 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 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 def get_species(cls): return cls.species # A static method is called without a class or instance reference @staticmethod 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 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 # 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. if __name__ == '__main__': # Instantiate a class i = Human(name="Ian") i.say("hi") # "Ian: hi" j = Human("Joel") j.say("hello") # "Joel: hello" # i and j are instances of type Human, or in other words: they are Human objects # Call our class method 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" # 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 print(i.grunt()) # => TypeError: grunt() takes 0 positional arguments but 1 was given # Update the property for this instance 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 #################################################### ## 6.1 Inheritance #################################################### # Inheritance allows new child classes to be defined that inherit methods and # variables from their parent class. # Using the Human class defined above as the base or parent class, we can # define a child class, Superhero, which inherits the class variables like # "species", "name", and "age", as well as methods, like "sing" and "grunt" # from the Human class, but can also have its own unique properties. # To take advantage of modularization by file you could place the classes above in their own files, # say, human.py # To import functions from other files use the following format # from "filename-without-extension" import "function-or-class" 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 # 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: def __init__(self, name, movie=False, superpowers=["super strength", "bulletproofing"]): # add additional class attributes: self.fictional = True self.movie = movie 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: super().__init__(name) # override the sing method def sing(self): return 'Dun, dun, DUN!' # add an additional instance method 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 if isinstance(sup, Human): print('I am human') 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 print(Superhero.__mro__) # => (<class '__main__.Superhero'>, # => <class 'human.Human'>, <class 'object'>) # Calls parent method but uses its own class attribute print(sup.get_species()) # => Superhuman # Calls overridden method print(sup.sing()) # => Dun, dun, DUN! # Calls method from Human sup.say('Spoon') # => Tick: Spoon # Call method that exists only in Superhero 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)) #################################################### ## 6.2 Multiple Inheritance #################################################### # 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 def sonar(self): return '))) ... (((' if __name__ == '__main__': b = Bat() print(b.say('hello')) print(b.fly) # 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". 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' 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 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 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 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 #################################################### ## 7. Advanced #################################################### # 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 # 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] # 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) 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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|
# Single line comments start with a hash.
# 单行注释由一个井号开头。
""" Multiline strings can be written
using three "'s, and are often used
as comments
三个双引号(或单引号)之间可以写多行字符串,
通常用来写注释。
"""
####################################################
## 1. Primitive Datatypes and Operators
## 1. 基本数据类型和操作符
####################################################
# You have numbers
# 数字就是数字
3#=> 3
# Math is what you would expect
# 四则运算也是你所期望的那样
1+1#=> 2
8-1#=> 7
10*2#=> 20
35/5#=> 7
# Division is a bit tricky. It is integer division and floors the results
# automatically.
# 除法有一点棘手。
# 对于整数除法来说,计算结果会自动取整。
5/2#=> 2
# To fix division we need to learn about floats.
# 为了修正除法的问题,我们需要先学习浮点数。
2.0 # This is a float
2.0 # 这是一个浮点数
11.0/4.0#=> 2.75 ahhh...much better
11.0/4.0#=> 2.75 啊……这样就好多了
# Enforce precedence with parentheses
# 使用小括号来强制计算的优先顺序
(1+3)*2#=> 8
# Boolean values are primitives
# 布尔值也是基本数据类型
True
False
# negate with not
# 使用 not 来取反
notTrue#=> False
notFalse#=> True
# Equality is ==
# 等式判断用 ==
1==1#=> True
2==1#=> False
# Inequality is !=
# 不等式判断是用 !=
1!=1#=> False
2!=1#=> True
# More comparisons
# 还有更多的比较运算
1<10#=> True
1>10#=> False
2<=2#=> True
2>=2#=> True
# Comparisons can be chained!
# 居然可以把比较运算串连起来!
1<2<3#=> True
2<3<2#=> False
# Strings are created with " or '
# 使用 " 或 ' 来创建字符串
"This is a string."
'This is also a string.'
# Strings can be added too!
# 字符串也可以相加!
"Hello "+"world!"#=> "Hello world!"
# A string can be treated like a list of characters
# 一个字符串可以视为一个字符的列表
# (译注:后面会讲到“列表”。)
"This is a string"[0]#=> 'T'
# % can be used to format strings, like this:
# % 可以用来格式化字符串,就像这样:
"%s can be %s"%("strings","interpolated")
# A newer way to format strings is the format method.
# This method is the preferred way
# 后来又有一种格式化字符串的新方法:format 方法。
# 我们推荐使用这个方法。
"{0} can be {1}".format("strings","formatted")
# You can use keywords if you don't want to count.
# 如果你不喜欢数数的话,可以使用关键字(变量)。
"{name} wants to eat {food}".format(name="Bob",food="lasagna")
# None is an object
# None 是一个对象
None#=> None
# Don't use the equality `==` symbol to compare objects to None
# Use `is` instead
# 不要使用相等符号 `==` 来把对象和 None 进行比较,
# 而要用 `is`。
"etc"isNone#=> False
NoneisNone #=> True
# The 'is' operator tests for object identity. This isn't
# very useful when dealing with primitive values, but is
# very useful when dealing with objects.
# 这个 `is` 操作符用于比较两个对象的标识。
# (译注:对象一旦建立,其标识就不会改变,可以认为它就是对象的内存地址。)
# 在处理基本数据类型时基本用不上,
# 但它在处理对象时很有用。
# None, 0, and empty strings/lists all evaluate to False.
# All other values are True
# None、0 以及空字符串和空列表都等于 False,
# 除此以外的所有值都等于 True。
0==False #=> True
""==False#=> True
####################################################
## 2. Variables and Collections
## 2. 变量和集合
####################################################
# Printing is pretty easy
# 打印输出很简单
print"I'm Python. Nice to meet you!"
# No need to declare variables before assigning to them.
# 在赋值给变量之前不需要声明
some_var=5 # Convention is to use lower_case_with_underscores
# 变量名的约定是使用下划线分隔的小写单词
some_var#=> 5
# Accessing a previously unassigned variable is an exception.
# See Control Flow to learn more about exception handling.
# 访问一个未赋值的变量会产生一个异常。
# 进一步了解异常处理,可参见下一节《控制流》。
some_other_var # Raises a name error
# 会抛出一个名称错误
# if can be used as an expression
# if 可以作为表达式来使用
"yahoo!"if3>2else2#=> "yahoo!"
# Lists store sequences
# 列表用于存储序列
li=[]
# You can start with a prefilled list
# 我们先尝试一个预先填充好的列表
other_li=[4,5,6]
# Add stuff to the end of a list with append
# 使用 append 方法把元素添加到列表的尾部
li.append(1) #li is now [1]
#li 现在是 [1]
li.append(2) #li is now [1, 2]
#li 现在是 [1, 2]
li.append(4) #li is now [1, 2, 4]
#li 现在是 [1, 2, 4]
li.append(3) #li is now [1, 2, 4, 3]
#li 现在是 [1, 2, 4, 3]
# Remove from the end with pop
# 使用 pop 来移除最后一个元素
li.pop() #=> 3 and li is now [1, 2, 4]
#=> 3,然后 li 现在是 [1, 2, 4]
# Let's put it back
# 我们再把它放回去
li.append(3) # li is now [1, 2, 4, 3] again.
# li 现在又是 [1, 2, 4, 3] 了
# Access a list like you would any array
# 像访问其它语言的数组那样访问列表
li[0]#=> 1
# Look at the last element
# 查询最后一个元素
li[-1]#=> 3
# Looking out of bounds is an IndexError
# 越界查询会产生一个索引错误
li[4]# Raises an IndexError
# 抛出一个索引错误
# You can look at ranges with slice syntax.
# (It's a closed/open range for you mathy types.)
# 你可以使用切片语法来查询列表的一个范围。
# (这个范围相当于数学中的左闭右开区间。)
li[1:3]#=> [2, 4]
# Omit the beginning
# 省略开头
li[2:]#=> [4, 3]
# Omit the end
# 省略结尾
li[:3]#=> [1, 2, 4]
# Remove arbitrary elements from a list with del
# 使用 del 来删除列表中的任意元素
delli[2]# li is now [1, 2, 3]
# li 现在是 [1, 2, 3]
# You can add lists
# 可以把列表相加
li+other_li#=> [1, 2, 3, 4, 5, 6] - Note: li and other_li is left alone
#=> [1, 2, 3, 4, 5, 6] - 请留意 li 和 other_li 并不会被修改
# Concatenate lists with extend
# 使用 extend 来合并列表
li.extend(other_li)# Now li is [1, 2, 3, 4, 5, 6]
# 现在 li 是 [1, 2, 3, 4, 5, 6]
# Check for existence in a list with in
# 用 in 来检查是否存在于某个列表中
1inli#=> True
# Examine the length with len
# 用 len 来检测列表的长度
len(li)#=> 6
# Tuples are like lists but are immutable.
# 元组很像列表,但它是“不可变”的。
tup=(1,2,3)
tup[0]#=> 1
tup[0]=3 # Raises a TypeError
# 抛出一个类型错误
# You can do all those list thingies on tuples too
# 操作列表的方式通常也能用在元组身上
len(tup)#=> 3
tup+(4,5,6)#=> (1, 2, 3, 4, 5, 6)
tup[:2]#=> (1, 2)
2intup#=> True
# You can unpack tuples (or lists) into variables
# 你可以把元组(或列表)中的元素解包赋值给多个变量
a,b,c=(1,2,3) # a is now 1, b is now 2 and c is now 3
# 现在 a 是 1,b 是 2,c 是 3
# Tuples are created by default if you leave out the parentheses
# 如果你省去了小括号,那么元组会被自动创建
d,e,f=4,5,6
# Now look how easy it is to swap two values
# 再来看看交换两个值是多么简单。
e,d=d,e # d is now 5 and e is now 4
# 现在 d 是 5 而 e 是 4
# Dictionaries store mappings
# 字典用于存储映射关系
empty_dict={}
# Here is a prefilled dictionary
# 这是一个预先填充的字典
filled_dict={"one":1,"two":2,"three":3}
# Look up values with []
# 使用 [] 来查询键值
filled_dict["one"]#=> 1
# Get all keys as a list
# 将字典的所有键名获取为一个列表
filled_dict.keys()#=> ["three", "two", "one"]
# Note - Dictionary key ordering is not guaranteed.
# Your results might not match this exactly.
# 请注意:无法保证字典键名的顺序如何排列。
# 你得到的结果可能跟上面的示例不一致。
# Get all values as a list
# 将字典的所有键值获取为一个列表
filled_dict.values()#=> [3, 2, 1]
# Note - Same as above regarding key ordering.
# 请注意:顺序的问题和上面一样。
# Check for existence of keys in a dictionary with in
# 使用 in 来检查一个字典是否包含某个键名
"one"infilled_dict#=> True
1infilled_dict#=> False
# Looking up a non-existing key is a KeyError
# 查询一个不存在的键名会产生一个键名错误
filled_dict["four"]# KeyError
# 键名错误
# Use get method to avoid the KeyError
# 所以要使用 get 方法来避免键名错误
filled_dict.get("one")#=> 1
filled_dict.get("four")#=> None
# The get method supports a default argument when the value is missing
# get 方法支持传入一个默认值参数,将在取不到值时返回。
filled_dict.get("one",4)#=> 1
filled_dict.get("four",4)#=> 4
# Setdefault method is a safe way to add new key-value pair into dictionary
# Setdefault 方法可以安全地把新的名值对添加到字典里
filled_dict.setdefault("five",5)#filled_dict["five"] is set to 5
#filled_dict["five"] 被设置为 5
filled_dict.setdefault("five",6)#filled_dict["five"] is still 5
#filled_dict["five"] 仍然为 5
# Sets store ... well sets
# set 用于保存集合
empty_set=set()
# Initialize a set with a bunch of values
# 使用一堆值来初始化一个集合
some_set=set([1,2,2,3,4])# some_set is now set([1, 2, 3, 4])
# some_set 现在是 set([1, 2, 3, 4])
# Since Python 2.7, {} can be used to declare a set
# 从 Python 2.7 开始,{} 可以用来声明一个集合
filled_set={1,2,2,3,4}# => {1, 2, 3, 4}
# (译注:集合是种无序不重复的元素集,因此重复的 2 被滤除了。)
# (译注:{} 不会创建一个空集合,只会创建一个空字典。)
# Add more items to a set
# 把更多的元素添加进一个集合
filled_set.add(5)# filled_set is now {1, 2, 3, 4, 5}
# filled_set 现在是 {1, 2, 3, 4, 5}
# Do set intersection with &
# 使用 & 来获取交集
other_set={3,4,5,6}
filled_set&other_set#=> {3, 4, 5}
# Do set union with |
# 使用 | 来获取并集
filled_set|other_set#=> {1, 2, 3, 4, 5, 6}
# Do set difference with -
# 使用 - 来获取补集
{1,2,3,4}-{2,3,5}#=> {1, 4}
# Check for existence in a set with in
# 使用 in 来检查是否存在于某个集合中
2infilled_set#=> True
10infilled_set#=> False
####################################################
## 3. Control Flow
## 3. 控制流
####################################################
# Let's just make a variable
# 我们先创建一个变量
some_var=5
# Here is an if statement. Indentation is significant in python!
# prints "some_var is smaller than 10"
# 这里有一个条件语句。缩进在 Python 中可是很重要的哦!
# 程序会打印出 "some_var is smaller than 10"
# (译注:意为“some_var 比 10 小”。)
ifsome_var>10:
print"some_var is totally bigger than 10."
# (译注:意为“some_var 完全比 10 大”。)
elifsome_var<10: # This elif clause is optional.
# 这里的 elif 子句是可选的
print"some_var is smaller than 10."
# (译注:意为“some_var 比 10 小”。)
else: # This is optional too.
# 这一句也是可选的
print"some_var is indeed 10."
# (译注:意为“some_var 就是 10”。)
"""
For loops iterate over lists
for 循环可以遍历列表
prints:
如果要打印出:
dog is a mammal
cat is a mammal
mouse is a mammal
"""
foranimal in["dog","cat","mouse"]:
# You can use % to interpolate formatted strings
# 别忘了你可以使用 % 来格式化字符串
print"%s is a mammal"%animal
# (译注:意为“%s 是哺乳动物”。)
"""
`range(number)` returns a list of numbers
from zero to the given number
`range(数字)` 会返回一个数字列表,
这个列表将包含从零到给定的数字。
prints:
如果要打印出:
0
1
2
3
"""
foriinrange(4):
printi
"""
While loops go until a condition is no longer met.
while 循环会一直继续,直到条件不再满足。
prints:
如果要打印出:
0
1
2
3
"""
x=0
whilex<4:
printx
x+=1 # Shorthand for x = x + 1
# 这是 x = x + 1 的简写方式
# Handle exceptions with a try/except block
# 使用 try/except 代码块来处理异常
# Works on Python 2.6 and up:
# 适用于 Python 2.6 及以上版本:
try:
# Use raise to raise an error
# 使用 raise 来抛出一个错误
raiseIndexError("This is an index error")
# 抛出一个索引错误:“这是一个索引错误”。
exceptIndexErrorase:
pass # Pass is just a no-op. Usually you would do recovery here.
# pass 只是一个空操作。通常你应该在这里做一些恢复工作。
####################################################
## 4. Functions
## 4. 函数
####################################################
# Use def to create new functions
# 使用 def 来创建新函数
defadd(x,y):
print"x is %s and y is %s"%(x,y)
# (译注:意为“x 是 %s 而且 y 是 %s”。)
returnx+y # Return values with a return statement
# 使用 return 语句来返回值
# Calling functions with parameters
# 调用函数并传入参数
add(5,6)#=> prints out "x is 5 and y is 6" and returns 11
# (译注:意为“x 是 5 而且 y 是 6”,并返回 11)
# Another way to call functions is with keyword arguments
# 调用函数的另一种方式是传入关键字参数
add(y=6,x=5) # Keyword arguments can arrive in any order.
# 关键字参数可以以任意顺序传入
# You can define functions that take a variable number of
# positional arguments
# 你可以定义一个函数,并让它接受可变数量的定位参数。
defvarargs(*args):
returnargs
varargs(1,2,3)#=> (1,2,3)
# You can define functions that take a variable number of
# keyword arguments, as well
# 你也可以定义一个函数,并让它接受可变数量的关键字参数。
defkeyword_args(**kwargs):
returnkwargs
# Let's call it to see what happens
# 我们试着调用它,看看会发生什么:
keyword_args(big="foot",loch="ness")#=> {"big": "foot", "loch": "ness"}
# You can do both at once, if you like
# 你还可以同时使用这两类参数,只要你愿意:
defall_the_args(*args,**kwargs):
printargs
printkwargs
"""
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 varargs/kwargs!
# Use * to expand tuples and use ** to expand kwargs.
# 在调用函数时,定位参数和关键字参数还可以反过来用。
# 使用 * 来展开元组,使用 ** 来展开关键字参数。
args=(1,2,3,4)
kwargs={"a":3,"b":4}
all_the_args(*args)# equivalent to foo(1, 2, 3, 4)
# 相当于 all_the_args(1, 2, 3, 4)
all_the_args(**kwargs)# equivalent to foo(a=3, b=4)
# 相当于 all_the_args(a=3, b=4)
all_the_args(*args,**kwargs)# equivalent to foo(1, 2, 3, 4, a=3, b=4)
# 相当于 all_the_args(1, 2, 3, 4, a=3, b=4)
# Python has first class functions
# 函数在 Python 中是一等公民
defcreate_adder(x):
defadder(y):
returnx+y
returnadder
add_10=create_adder(10)
add_10(3)#=> 13
# There are also anonymous functions
# 还有匿名函数
(lambdax:x>2)(3)#=> True
# There are built-in higher order functions
# 还有一些内建的高阶函数
map(add_10,[1,2,3])#=> [11, 12, 13]
filter(lambdax:x>5,[3,4,5,6,7])#=> [6, 7]
# We can use list comprehensions for nice maps and filters
# 我们可以使用列表推导式来模拟 map 和 filter
[add_10(i)foriin[1,2,3]] #=> [11, 12, 13]
[xforxin[3,4,5,6,7]ifx>5]#=> [6, 7]
####################################################
## 5. Classes
## 5. 类
####################################################
# We subclass from object to get a class.
# 我们可以从对象中继承,来得到一个类。
classHuman(object):
# A class attribute. It is shared by all instances of this class
# 下面是一个类属性。它将被这个类的所有实例共享。
species="H. sapiens"
# Basic initializer
# 基本的初始化函数(构造函数)
def__init__(self,name):
# Assign the argument to the instance's name attribute
# 把参数赋值为实例的 name 属性
self.name=name
# An instance method. All methods take self as the first argument
# 下面是一个实例方法。所有方法都以 self 作为第一个参数。
defsay(self,msg):
return"%s: %s"%(self.name,msg)
# A class method is shared among all instances
# They are called with the calling class as the first argument
# 类方法会被所有实例共享。
# 类方法在调用时,会将类本身作为第一个函数传入。
@classmethod
defget_species(cls):
returncls.species
# A static method is called without a class or instance reference
# 静态方法在调用时,不会传入类或实例的引用。
@staticmethod
defgrunt():
return"*grunt*"
# Instantiate a class
# 实例化一个类
i=Human(name="Ian")
printi.say("hi") # prints out "Ian: hi"
# 打印出 "Ian: hi"
j=Human("Joel")
printj.say("hello") # prints out "Joel: hello"
# 打印出 "Joel: hello"
# Call our class method
# 调用我们的类方法
i.get_species()#=> "H. sapiens"
# Change the shared attribute
# 修改共享属性
Human.species="H. neanderthalensis"
i.get_species()#=> "H. neanderthalensis"
j.get_species()#=> "H. neanderthalensis"
# Call the static method
# 调用静态方法
Human.grunt()#=> "*grunt*"
####################################################
## 6. Modules
## 6. 模块
####################################################
# You can import modules
# 你可以导入模块
importmath
printmath.sqrt(16)#=> 4
# You can get specific functions from a module
# 也可以从一个模块中获取指定的函数
frommathimportceil,floor
printceil(3.7) #=> 4.0
printfloor(3.7)#=> 3.0
# You can import all functions from a module.
# Warning: this is not recommended
# 你可以从一个模块中导入所有函数
# 警告:不建议使用这种方式
frommathimport*
# You can shorten module names
# 你可以缩短模块的名称
importmathasm
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.
# Python 模块就是普通的 Python 文件。
# 你可以编写你自己的模块,然后导入它们。
# 模块的名称与文件名相同。
# You can find out which functions and attributes
# defines a module.
# 你可以查出一个模块里有哪些函数和属性
importmath
dir(math)
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