python 简明教程 【转】

转自：https://learnxinyminutes.com/docs/python/

# Single line comments start with a number symbol.

""" Multiline strings can be written
    using three "s, and are often used
    as comments
"""

####################################################
# 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

# Division is a bit tricky. It is integer division and floors the results
# automatically.
5 / 2  # => 2

# To fix division we need to learn about floats.
2.0  # This is a float
11.0 / 4.0  # => 2.75 ahhh...much better

# 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

# Note that we can also import division module(Section 6 Modules)
# to carry out normal division with just one '/'.
from __future__ import division

11 / 4  # => 2.75  ...normal division
11 // 4  # => 2 ...floored division

# Modulo operation
7 % 3  # => 1

# Exponentiation (x to the yth power)
2 ** 4  # => 16

# Enforce precedence with parentheses
(1 + 3) * 2  # => 8

# Boolean Operators
# Note "and" and "or" are case-sensitive
True and False  # => False
False or True  # => True

# Note using Bool operators with ints
0 and 2  # => 0
-5 or 0  # => -5
0 == False  # => True
2 == True  # => False
1 == True  # => True

# negate with not
not True  # => False
not False  # => 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

# Strings are created with " or '
"This is a string."
'This is also a string.'

# Strings can be added too!
"Hello " + "world!"  # => "Hello world!"
# Strings can be added without using '+'
"Hello " "world!"  # => "Hello world!"

# ... or multiplied
"Hello" * 3  # => "HelloHelloHello"

# 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

# String formatting with %
# Even though the % string operator will be deprecated on Python 3.1 and removed
# later at some time, it may still be good to know how it works.
x = 'apple'
y = 'lemon'
z = "The items in the basket are %s and %s" % (x, y)

# A newer way to format strings is the format method.
# This method is the preferred way
"{} is a {}".format("This", "placeholder")
"{0} can be {1}".format("strings", "formatted")
# You can use keywords if you don't want to count.
"{name} wants to eat {food}".format(name="Bob", food="lasagna")

# None is an object
None  # => None

# Don't use the equality "==" symbol to compare objects to None
# Use "is" instead
"etc" is None  # => False
None is None  # => 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.

# Any object can be used in a Boolean context.
# The following values are considered falsey:
#    - None
#    - zero of any numeric type (e.g., 0, 0L, 0.0, 0j)
#    - empty sequences (e.g., '', (), [])
#    - empty containers (e.g., {}, set())
#    - instances of user-defined classes meeting certain conditions
#      see: https://docs.python.org/2/reference/datamodel.html#object.__nonzero__
#
# All other values are truthy (using the bool() function on them returns True).
bool(0)  # => False
bool("")  # => False

####################################################
# 2. Variables and Collections
####################################################

# Python has a print statement
print "I'm Python. Nice to meet you!"  # => I'm Python. Nice to meet you!

# Simple way to get input data from console
input_string_var = raw_input(
    "Enter some data: ")  # Returns the data as a string
input_var = input("Enter some data: ")  # Evaluates the data as python code
# Warning: Caution is recommended for input() method usage
# Note: In python 3, input() is deprecated and raw_input() is renamed to input()

# No need to declare variables before assigning to them.
some_var = 5  # Convention is to use lower_case_with_underscores
some_var  # => 5

# Accessing a previously unassigned variable is an exception.
# See Control Flow to learn more about exception handling.
some_other_var  # Raises a name error

# 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
# Assign new values to indexes that have already been initialized with =
li[0] = 42
li[0]  # => 42
li[0] = 1  # Note: setting it back to the original value
# 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.
# (It's a closed/open range for you mathy types.)
li[1:3]  # => [2, 4]
# Omit the beginning
li[2:]  # => [4, 3]
# Omit the end
li[:3]  # => [1, 2, 4]
# Select every second entry
li[::2]  # =>[1, 4]
# Reverse a copy of the list
li[::-1]  # => [3, 4, 2, 1]
# Use any combination of these to make advanced slices
# li[start:end:step]

# Remove arbitrary elements from a list with "del"
del li[2]  # li is now [1, 2, 3]

# You can add lists
li + other_li  # => [1, 2, 3, 4, 5, 6]
# Note: values for li and for other_li are not modified.

# Concatenate lists with "extend()"
li.extend(other_li)  # Now li is [1, 2, 3, 4, 5, 6]

# Remove first occurrence of a value
li.remove(2)  # li is now [1, 3, 4, 5, 6]
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, 4, 5, 6] again

# Get the index of the first item found
li.index(2)  # => 1
li.index(7)  # Raises a ValueError as 7 is not in the list

# 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

# You can do all those list thingies 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
d, e, f = 4, 5, 6  # you can leave out the parentheses
# Tuples are created by default if you leave out the parentheses
g = 4, 5, 6  # => (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()"
filled_dict.keys()  # => ["three", "two", "one"]
# Note - Dictionary key ordering is not guaranteed.
# Your results might not match this exactly.

# Get all values as a list with "values()"
filled_dict.values()  # => [3, 2, 1]
# Note - Same as above regarding key ordering.

# Get all key-value pairs as a list of tuples with "items()"
filled_dict.items()  # => [("one", 1), ("two", 2), ("three", 3)]

# 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
# note that filled_dict.get("four") is still => None
# (get doesn't set the value in the dictionary)

# set the value of a key with a syntax similar to lists
filled_dict["four"] = 4  # now, filled_dict["four"] => 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

# Sets store ... well sets (which are like lists but can contain no duplicates)
empty_set = set()
# Initialize a "set()" with a bunch of values
some_set = set([1, 2, 2, 3, 4])  # some_set is now set([1, 2, 3, 4])

# order is not guaranteed, even though it may sometimes look sorted
another_set = set([4, 3, 2, 2, 1])  # another_set is now set([1, 2, 3, 4])

# Since Python 2.7, {} can be used to declare a set
filled_set = {1, 2, 2, 3, 4}  # => {1, 2, 3, 4}

# Add more items to a 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
10 not in filled_set # => True

# Check data type of variable
type(li)   # => list
type(filled_dict)   # => dict
type(5)   # => int

####################################################
#  3. Control Flow
####################################################

# Let's just make a variable
some_var = 5

# Here is an if statement. Indentation is significant in python!
# 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 {0} to interpolate formatted strings. (See above.)
    print "{0} 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

"""
"range(lower, upper)" returns a list of numbers
from the lower number to the upper number
prints:
    4
    5
    6
    7
"""
for i in range(4, 8):
    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

# Works on Python 2.6 and up:
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

####################################################
# 4. Functions
####################################################

# Use "def" to create new functions
def add(x, y):
    print "x is {0} and y is {1}".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 args, which will be interpreted as a tuple by using *
def varargs(*args):
    return args

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

# You can define functions that take a variable number of
# keyword args, as well, which will be interpreted as a dict by using **
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 positional args and use ** to expand keyword args.
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)

# you can pass args and kwargs along to other functions that take args/kwargs
# by expanding them with * and ** respectively
def pass_all_the_args(*args, **kwargs):
    all_the_args(*args, **kwargs)
    print varargs(*args)
    print keyword_args(**kwargs)

# 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
map(add_10, [1, 2, 3])  # => [11, 12, 13]
map(max, [1, 2, 3], [4, 2, 1])  # => [4, 2, 3]

filter(lambda x: x > 5, [3, 4, 5, 6, 7])  # => [6, 7]

# We can use list comprehensions for nice maps and filters
[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 in 'abc'}  # => {'a', 'b', 'c'}
{x: x ** 2 for x in range(5)}  # => {0: 0, 1: 1, 2: 4, 3: 9, 4: 16}

####################################################
# 5. Classes
####################################################

# 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

        # Initialize property
        self.age = 0

    # An instance method. All methods take "self" as the first argument
    def say(self, msg):
        return "{0}: {1}".format(self.name, 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*"

    # A property is just like a getter.
    # It turns the method age() into an read-only attribute
    # of the same name.
    @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

# 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*"

# Update the property
i.age = 42

# Get the property
i.age  # => 42

# Delete the property
del i.age
i.age  # => raises an AttributeError

####################################################
# 6. Modules
####################################################

# You can import modules
import math

print math.sqrt(16)  # => 4

# You can get specific functions from a module
from math import ceil, floor

print ceil(3.7)  # => 4.0
print floor(3.7)  # => 3.0

# You can import all functions from a module.
# Warning: this is not recommended
from math import *

# You can shorten module names
import math as m

math.sqrt(16) == m.sqrt(16)  # => True
# you can also test that the functions are equivalent
from math import sqrt

math.sqrt == m.sqrt == sqrt  # => True

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

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

dir(math)

# 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.

####################################################
# 7. Advanced
####################################################

# Generators
# A generator "generates" values as they are requested instead of storing
# everything up front

# The following method (*NOT* a generator) will double all values and store it
# in `double_arr`. For large size of iterables, that might get huge!
def double_numbers(iterable):
    double_arr = []
    for i in iterable:
        double_arr.append(i + i)
    return double_arr

# Running the following would mean we'll double all values first and return all
# of them back to be checked by our condition
for value in double_numbers(range(1000000)):  # `test_non_generator`
    print value
    if value > 5:
        break

# We could instead use a generator to "generate" the doubled value as the item
# is being requested
def double_numbers_generator(iterable):
    for i in iterable:
        yield i + i

# Running the same code as before, but with a generator, now allows us to iterate
# over the values and doubling them one by one as they are being consumed by
# our logic. Hence as soon as we see a value > 5, we break out of the
# loop and don't need to double most of the values sent in (MUCH FASTER!)
for value in double_numbers_generator(xrange(1000000)):  # `test_generator`
    print value
    if value > 5:
        break

# BTW: did you notice the use of `range` in `test_non_generator` and `xrange` in `test_generator`?
# Just as `double_numbers_generator` is the generator version of `double_numbers`
# We have `xrange` as the generator version of `range`
# `range` would return back and array with 1000000 values for us to use
# `xrange` would generate 1000000 values for us as we request / iterate over those items

# 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
# A decorator is a higher order function, which accepts and returns a function.
# Simple usage example – add_apples decorator will add 'Apple' element into
# fruits list returned by get_fruits target function.
def add_apples(func):
    def get_fruits():
        fruits = func()
        fruits.append('Apple')
        return fruits
    return get_fruits

@add_apples
def get_fruits():
    return ['Banana', 'Mango', 'Orange']

# Prints out the list of fruits with 'Apple' element in it:
# Banana, Mango, Orange, Apple
print ', '.join(get_fruits())

# 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 :(