python
            
            
# Single line remark 
""" Multi-line strings can use
    three quotation marks to pack, which can also be regarded as 
    multi-line remarks 
"""

####################################################
## 1. Raw Data Types & Operators 
####################################################

# Number type
3  # => 3

# Simple operations 
1 + 1  # => 2
8 - 1  # => 7
10 * 2  # => 20
35 / 5  # => 7

# The division of integer will automatically have integer numbers
5 / 2  # => 2

# If we want to perform a precise division operation, we need to introduce the float 
2.0     # the float 
11.0 / 4.0  # => 2.75 more precise 

# The grouping operator can bring the highest precedence 
(1 + 3) * 2  # => 8

# Boolean value is also a basic data type
True
False

# Use not to perform the Logical NOT operation 
not True  # => False
not False  # => True

# Equality  
1 == 1  # => True
2 == 1  # => False

# Inequality
1 != 1  # => False
2 != 1  # => True

# More comparison operators 
1 < 10  # => True
1 > 10  # => False
2 <= 2  # => True
2 >= 2  # => True

# Comparison operations can be chained together to write
1 < 2 < 3  # => True
2 < 3 < 2  # => False

# Use " or ' to pack the strings 
"This is a string."
'This is also a string.'

# Use + to connect strings 
"Hello " + "world!"  # => "Hello world!"

# A string can be regarded as a list of characters 
"This is a string"[0]  # => 'T'

# Use % to format strings 
"%s can be %s" % ("strings", "interpolated")

# You can also use the "format" method to format the strings  
# The method is recommended 
"{0} can be {1}".format("strings", "formatted")
# You can also use the variable name to replace the number 
"{name} wants to eat {food}".format(name="Bob", food="lasagna")

# None is a object 
None  # => None

# Do not use the equality mark `==` to compare with None
# but use `is` to compare 
"etc" is None  # => False
None is None  # => True

# 'is' can be used to compare the equality of objects 
# this operator is not very useful when comparing raw data, but it is necessary when comparing objects 

# None, 0 and null string are all regarded as False
# Others are regarded as True
0 == False  # => True
"" == False  # => True


####################################################
## 2. Variables & Collections 
####################################################

# Convenient Export
print "I'm Python. Nice to meet you!"


# No declaration is needed before the variable assignment 
some_var = 5    # It is generally recommended to use a combination of lowercase letters and underscores as variable names
some_var  # => 5

# Accessing a unassigned variable will raise an error 
# You can read the section of Control Flow to know how to deal with the error
some_other_var  # Raise NameError

# The statement of if can be used as an expression 
"yahoo!" if 3 > 2 else 2  # => "yahoo!"

# The list is used to save sequences  
li = []
# You can directly use the initialized list 
other_li = [4, 5, 6]

# You can add elements in the end of the list 
li.append(1)    # li now is [1]
li.append(2)    # li now is [1, 2]
li.append(4)    # li now is [1, 2, 4]
li.append(3)    # li now is [1, 2, 4, 3]
# Remove the elements in the end of the list 
li.pop()        # => 3 li now is [1, 2, 4]
# Add again 
li.append(3)    # li is now [1, 2, 4, 3] again.

# The way of accessing the list is like the way of accessing an array in other languages 
li[0]  # => 1
# Access the last element 
li[-1]  # => 3

# Crossing the bounds will cause the exception 
li[4]  # Raise out of bounds exception 

# Slicing needs to be used to the index access of the list  
# It can be regarded as the left closed and right open interval in mathematics
li[1:3]  # => [2, 4]
# Omit the element at the beginning 
li[2:]  # => [4, 3]
# Omit the elements in the end 
li[:3]  # => [1, 2, 4]

# Delete the specified elements 
del li[2]  # li now is [1, 2, 3]

# Merge lists
li + other_li  # => [1, 2, 3, 4, 5, 6] - that will not change the two lists

# Use extend to merge lists
li.extend(other_li)  # li is [1, 2, 3, 4, 5, 6]

# Use in to return whether the elements are in the list 
1 in li  # => True

# Return the list length 
len(li)  # => 6


# A tuple is similar to a list, but it cannot be modified 
tup = (1, 2, 3)
tup[0]  # => 1
tup[0] = 3  # type error

# Most list operations are applicable to tuples 
len(tup)  # => 3
tup + (4, 5, 6)  # => (1, 2, 3, 4, 5, 6)
tup[:2]  # => (1, 2)
2 in tup  # => True

# You can unpack the tuple and assign it to multiple variables 
a, b, c = (1, 2, 3)     # a is 1, b is 2, c is 3
# If the brackets are not added, it will be regarded as tuple automatically 
d, e, f = 4, 5, 6
# Now we can see how easy it is to exchange two numbers 
e, d = d, e     # d is 5, e is 4


# Use dictionary to store the mapping relations 
empty_dict = {}
# dictionary initialization 
filled_dict = {"one": 1, "two": 2, "three": 3}

# The dictionary also uses square brackets to access elements
filled_dict["one"]  # => 1

# Save all keys in the list 
filled_dict.keys()  # => ["three", "two", "one"]
# The order of keys is not unique, and the obtained order is not necessarily the same

# Save all values in the list 
filled_dict.values()  # => [3, 2, 1]
# in the same order of the keys 

# Judge whether a key exists
"one" in filled_dict  # => True
1 in filled_dict  # => False

# Querying a non-existing key will raise KeyError
filled_dict["four"]  # KeyError

# Use get method to avoid KeyError
filled_dict.get("one")  # => 1
filled_dict.get("four")  # => None
# get method supports to return a default when the key does not exist 
filled_dict.get("one", 4)  # => 1
filled_dict.get("four", 4)  # => 4

# setdefault is a safer method to add dictionary elements 
filled_dict.setdefault("five", 5)  # the value of filled_dict["five"] is 5
filled_dict.setdefault("five", 6)  # the value of filled_dict["five"] is still 5


# Collect and store non-ordered elements 
empty_set = set()
# Initialize a collection 
some_set = set([1, 2, 2, 3, 4])  # some_set now is set([1, 2, 3, 4])

# After Python 2.7, braces can be used to express a collection
filled_set = {1, 2, 2, 3, 4}  # => {1 2 3 4}

# Add elements in the collection
filled_set.add(5)  # filled_set now is {1, 2, 3, 4, 5}

# Use & to calculate the intersection of the collection 
other_set = {3, 4, 5, 6}
filled_set & other_set  # => {3, 4, 5}

# Use | to calculate the union of the collection 
filled_set | other_set  # => {1, 2, 3, 4, 5, 6}

# Use - to to calculate the difference of the collection 
{1, 2, 3, 4} - {2, 3, 5}  # => {1, 4}

# Use in to judge whether the elements are in the collection 
2 in filled_set  # => True
10 in filled_set  # => False


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

# Create a new variable 
some_var = 5

# This is a statement of if, indentation is very important in python
# The following code snippet will export "some var is smaller than 10"
if some_var > 10:
    print "some_var is totally bigger than 10."
elif some_var < 10:    # The elif statement is not necessary
    print "some_var is smaller than 10."
else:           # else is not necessary either
    print "some_var is indeed 10."


"""
Use for loop to traverse the list 
exports:
    dog is a mammal
    cat is a mammal
    mouse is a mammal
"""
for animal in ["dog", "cat", "mouse"]:
    # You can use % to format the strings 
    print "%s is a mammal" % animal

"""
`range(number)` returns the list of numbers from 0 to the specified number 
exports:
    0
    1
    2
    3
"""
for i in range(4):
    print i

"""
while loop
exports:
    0
    1
    2
    3
"""
x = 0
while x < 4:
    print x
    x += 1  #  Abbreviation of x = x + 1 

# use try/except block to handle errors

# Python 2.6 and above are applicable to:
try:
    # Use "raise" to throw out an exception
    raise IndexError("This is an index error")
except IndexError as e:
    pass    # pass does nothing, but it usually does some recovery work


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

# Use def to create a new function 
def add(x, y):
    print "x is %s and y is %s" % (x, y)
    return x + y    # Return a value by "return" 

# Call a function with parameters 
add(5, 6)  # => import "x is 5 and y is 6", and return 11

# Use keyword assignment to call a function 
add(y=6, x=5)   # The order doe not matter   

# We can also define a function accepting multiple variables, and those variables are arranged in order  
def varargs(*args):
    return args

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


# We can also define a function accepting multiple variables, and those variables are arranged by keywords 
def keyword_args(**kwargs):
    return kwargs

# Actual result:
keyword_args(big="foot", loch="ness")  # => {"big": "foot", "loch": "ness"}

# You can also define one function in two forms 
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 call a function, we can also perform the converse operation, and unfold the tuple and dictionary as parameters
args = (1, 2, 3, 4)
kwargs = {"a": 3, "b": 4}
all_the_args(*args)  # equal to foo(1, 2, 3, 4)
all_the_args(**kwargs)  # equal to foo(a=3, b=4)
all_the_args(*args, **kwargs)  # equal to foo(1, 2, 3, 4, a=3, b=4)

# Function is the first class in python 
def create_adder(x):
    def adder(y):
        return x + y
    return adder

add_10 = create_adder(10)
add_10(3)  # => 13

# Anonymous function 
(lambda x: x > 2)(3)  # => True

# Built-in higher-order function 
map(add_10, [1, 2, 3])  # => [11, 12, 13]
filter(lambda x: x > 5, [3, 4, 5, 6, 7])  # => [6, 7]

# You can use the list method to quote the higher-order function in a smarter way: 
[add_10(i) for i in [1, 2, 3]]  # => [11, 12, 13]
[x for x in [3, 4, 5, 6, 7] if x > 5]  # => [6, 7]

####################################################
## 5. Class
####################################################

# Our new class is inherited from the object class
class Human(object):

     # Class attributes, shared by the objects of all classes
    species = "H. sapiens"

    # Basic constructors 
    def __init__(self, name):
        # Assign parameters to object member attributes
        self.name = name

    # Member method; self has to be included in the parameters 
    def say(self, msg):
        return "%s: %s" % (self.name, msg)

    # Class methods, shared by all objects of all classes 
    # When this kind of methods are called, they will pass the class to the first parameter 
    @classmethod
    def get_species(cls):
        return cls.species

    # Static method needs the quotation of classes and objects to be called 
    @staticmethod
    def grunt():
        return "*grunt*"


# Instantiation of a class 
i = Human(name="Ian")
print i.say("hi")     # export "Ian: hi"

j = Human("Joel")
print j.say("hello")  # export "Joel: hello"

# Method of accessing a class 
i.get_species()  # => "H. sapiens"

# Modifying the shared attribute 
Human.species = "H. neanderthalensis"
i.get_species()  # => "H. neanderthalensis"
j.get_species()  # => "H. neanderthalensis"

# Accessing static variables 
Human.grunt()  # => "*grunt*"


####################################################
## 6. Module 
####################################################

# We can import other modules 
import math
print math.sqrt(16)  # => 4

# We can also import a specified function from a module 
from math import ceil, floor
print ceil(3.7)   # => 4.0
print floor(3.7)  # => 3.0

# Import all functions from a module 
# Warning: it is not recommended 
from math import *

# Abbreviation of module name 
import math as m
math.sqrt(16) == m.sqrt(16)  # => True

# Modules in Python are actually just ordinary python files
# You can also create your own modules and import them 
# Module names are the same as the file names 

# You can also check the attributes and methods in a module by the following method: 
import math
dir(math)