分分钟学会一门语言之Python3篇【转载】

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 :(

# 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)

posted on 2018-03-10 09:26 FengQIYUN 阅读(563) 评论(0) 编辑收藏举报