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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Iterators
In Python most container objects can be looped using a for statement.
For example we can use for statement for looping over a list.
Step1:  If we use it with a string, it loops over its characters.
Step2:  If use it with a dictionary, it loops over its keys
Step3:  So there are many types of objects which can be used with a for loop. These are called iterable objects.
There are many functions which consume these iterables.
Step4:  Iteration Protocol
Step6:  Having seen the mechanics behind the iterator protocol, it is easy to add iterator behavior to your classes. Define an _iter_() method which returns an object with a _next_() method. If the class defines _next_(), then _iter_() can just return self
Step7:  Generators
Generators are a simple and powerful tool for creating iterators. 
They are written like regular functions but use the yield statement whenever they want to return data. Each time next() is called on it, the generator resumes where it left off (it remembers all the data values and which statement was last executed). An example shows that generators can be trivially easy to create
Step8:  Anything that can be done with generators can also be done with class-based iterators as described in the previous section. What makes generators so compact is that the iter() and next() methods are created automatically.
Another key feature is that the local variables and execution state are automatically saved between calls. This made the function easier to write and much more clear than an approach using instance variables like self.index and self.data.
In addition to automatic method creation and saving program state, when generators terminate, they automatically raise StopIteration. In combination, these features make it easy to create iterators with no more effort than writing a regular function.
The following examples shows how generators work.
Step9:  Generator Expressions
Generator Expressions are generator version of list comprehensions. They look like list comprehensions, but returns a generator back instead of a list.
Step10:  Generator expressions are more compact but less versatile than full generator definitions and tend to be more memory friendly than equivalent list comprehensions.
Step11:  Note that generators provide another way to deal with infinity, for example
Step12:  Use of Generators
1. Easy to Implement
Generators can be implemented in a clear and concise way as compared to their iterator class counterpart.
Step13:  This was lengthy. Now lets do the same using a generator function.
Step14:  Since generators keep track of details automatically, it was concise and much cleaner in implementation.
2. Memory Efficient
A normal function to return a sequence will create the entire sequence in memory before returning the result. This is an overkill if the number of items in the sequence is very large.
3. Represent Infinite Stream
Generators are excellent medium to represent an infinite stream of data. Infinite streams cannot be stored in memory and since generators produce only one item at a time, it can represent infinite stream of data.
Step15:  4. Pipelining Generators
Generators can be used to pipeline a series of operations.
If we are analysing a log file and if the log file has a 3rd column that keeps track of the ips every hour and we want to sum it to find unique ips in last 5 months.
Step16:  Using Itertools
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Python Code:
for i in [1,2,3]:
    print(i)
Explanation: Iterators
In Python most container objects can be looped using a for statement.
For example we can use for statement for looping over a list.
End of explanation
for ch in 'test':
    print(ch)
Explanation: If we use it with a string, it loops over its characters.
End of explanation
for k in {1:'test1',2:'test'}:
    print(k)
Explanation: If use it with a dictionary, it loops over its keys
End of explanation
",".join(["a","b","c"])
",".join(('this','is','a','test'))
",".join({'key1':'value','key2':'value2'})
Explanation: So there are many types of objects which can be used with a for loop. These are called iterable objects.
There are many functions which consume these iterables.
End of explanation
x = iter([1,2,3])
print(x)
print(next(x))
print(next(x))
print(next(x))
print(next(x))  # <-- will create an error
Explanation: Iteration Protocol
End of explanation
class Reverse:
    Iterator for looping over a sequence backwards.
    def __init__(self, data):
        self.data = data
        self.index = len(data)
    def __iter__(self):
        return self
    def __next__(self):
        if self.index == 0:
            raise StopIteration
        self.index = self.index - 1
        return self.data[self.index]
rev = Reverse('spam')
for i in rev:
    print(i)
Explanation: Having seen the mechanics behind the iterator protocol, it is easy to add iterator behavior to your classes. Define an _iter_() method which returns an object with a _next_() method. If the class defines _next_(), then _iter_() can just return self:
End of explanation
def reverse(data):
    for index in range(len(data)-1, -1, -1):
        yield data[index]
for ch in reverse('shallow'):
    print(ch)
Explanation: Generators
Generators are a simple and powerful tool for creating iterators. 
They are written like regular functions but use the yield statement whenever they want to return data. Each time next() is called on it, the generator resumes where it left off (it remembers all the data values and which statement was last executed). An example shows that generators can be trivially easy to create:
End of explanation
def samplegen():
    print("begin")
    for i in range(3):
        print("before yield", i)
        yield i
        print("after yield", i)
    print("end")
    
f = samplegen()
print(next(f))
print(next(f))
print(next(f))
print(next(f))
Explanation: Anything that can be done with generators can also be done with class-based iterators as described in the previous section. What makes generators so compact is that the iter() and next() methods are created automatically.
Another key feature is that the local variables and execution state are automatically saved between calls. This made the function easier to write and much more clear than an approach using instance variables like self.index and self.data.
In addition to automatic method creation and saving program state, when generators terminate, they automatically raise StopIteration. In combination, these features make it easy to create iterators with no more effort than writing a regular function.
The following examples shows how generators work.
End of explanation
a = (x * x for x in range(10))
sum(a)
Explanation: Generator Expressions
Generator Expressions are generator version of list comprehensions. They look like list comprehensions, but returns a generator back instead of a list.
End of explanation
xvec = [5,16,7]
yvec = [4,12,18]
sum(x * y for x,y in zip(xvec,yvec))
data = 'golf'
list(data[i] for i in range(len(data)-1, -1, -1))
# unique_words = set(word  for line in page  for word in line.split())
# valedictorian = max((student.gpa, student.name) for student in graduates)
Explanation: Generator expressions are more compact but less versatile than full generator definitions and tend to be more memory friendly than equivalent list comprehensions.
End of explanation
from time import gmtime, strftime
def myGen():
    while True:
        yield strftime("%a, %d %b %Y %H:%M:%S +0000", gmtime())    
myGeneratorInstance = myGen()
next(myGeneratorInstance)
next(myGeneratorInstance)
Explanation: Note that generators provide another way to deal with infinity, for example:
End of explanation
# Iterator Class
class PowTwo:
    def __init__(self, max = 0):
        self.max = max
    def __iter__(self):
        self.n = 0
        return self
    def __next__(self):
        if self.n > self.max:
            raise StopIteration
        
        result = 2 ** self.n
        self.n += 1
        return result
Explanation: Use of Generators
1. Easy to Implement
Generators can be implemented in a clear and concise way as compared to their iterator class counterpart.
End of explanation
def PowTwoGen(max = 0):
    n = 0
    while n < max:
        yield 2 ** n
        n += 1
Explanation: This was lengthy. Now lets do the same using a generator function.
End of explanation
def all_event():
    n = 0
    while True:
        yield n
        n += 2
Explanation: Since generators keep track of details automatically, it was concise and much cleaner in implementation.
2. Memory Efficient
A normal function to return a sequence will create the entire sequence in memory before returning the result. This is an overkill if the number of items in the sequence is very large.
3. Represent Infinite Stream
Generators are excellent medium to represent an infinite stream of data. Infinite streams cannot be stored in memory and since generators produce only one item at a time, it can represent infinite stream of data.
End of explanation
with open('sells.log') as file:
    ip_col = (line[3] for line in file)
    per_hr = (int(x) for x in ip_col if x != 'N/A')
    print("IPs =", sum(per_hr))
Explanation: 4. Pipelining Generators
Generators can be used to pipeline a series of operations.
If we are analysing a log file and if the log file has a 3rd column that keeps track of the ips every hour and we want to sum it to find unique ips in last 5 months.
End of explanation
import itertools
horses = [1,2,3,4]
races = itertools.permutations(horses)
print(list(races))
Explanation: Using Itertools
End of explanation
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