Python code interpretation and execution stages

Python code gets compiled in following fashion.
If byte code is generated using Cpython then it is executed using Python Virtual machin (PVM)
If byte code is generated using Jpython then it is executed using Java Virtual machin (JVM)

PIP installation process & usefulness to Python

PIP is software installer. Follow the below mentioned procedure:

1. setup proxy

copy the proxy from internet explorer

set http_proxy=http://your_proxy:your_port
or
set http_proxy=http://username:password@your_proxy:your_port

set https_proxy=https://your_proxy:your_port
or
set https_proxy=https://username:password@your_proxy:your_port

once thi is done, you are done with setup to install softwares from internet via PIP

2. Install PIP..
$python get-pip.py

3. After this start installing using PIP
eg.
$pip install numpy
$pip install scipy

you can also install from the wheel file downloaded
eg.
$pip install setuptools-23.0.0-py2.py3-none-any.whl

----------------------------------------------------------------------------------------------

Why PIP is useful?
--> Because it downloads all the required dependancy for installing any particular piece of library

"yield" function sample code

Following is a sample code where use of yield function is shown. create_generator()  function is defined which basically iterates over a range of numbers 1 to 4. Here the value a  and  b  is summed up and incremented in every iteration.

In the following steps, when this same function create_generator() is used to provide range of values for loop (for i in create_generator() ) every time the program flow stops at yield  statement and provides value to i. This value is printed. Loop continues until the loop inside create_generator() completes.

It can also be seen in the following code where each step is printed using next() functionality. The last print statement is commented. Because, at that point there is no further value produced by generator function. It throws error.

Both the outputs (with and without comment part are provided). you can try it out too ....

#__author__ = 'Sumant'

# use of yield functionality of python

def create_generator():
    a = 1    

    b = 10  

    for count in range(1, 5):

        yield a + b
        a += 1        

        b += 1
for value in create_generator():
    print value

print "going step by step ..."

xx = create_generator()
print xx.next()
print xx.next()
print xx.next()
print xx.next()

#print xx.next()

-------------------------------------------------

OUTPUT (with last line commented)

-------------------------------------------------
11
13
15
17

going step by step ...
11
13
15
17

Process finished with exit code 0




-------------------------------------------------
OUTPUT (uncomment last line)
-------------------------------------------------
11
13
15
17
going step by step ...
11
13
15
17
Traceback (most recent call last):
  File ".../demo_yield.py", line 22, in <module>
    print xx.next()
StopIteration

Process finished with exit code 1

How to use "lambda" in python ??

Few days ago, I came across a very interesting function used in one of the python test scripts. Its called "lambda". 
lambda is like a callback function. Its a alternate to def in situations where 

• function is just one liner & only used once
• when a calling function needs a function as an input parameter

It is a keyword used to define "anonymous function". Its a kind of functionality similar to defining function where you define parameters and what to do with it.

But by using lambda, you can make use of other functions like "filter, map and reduce". I have provided this sample python script where lambda function is defined and shown how it can be used along with filter, map and reduce ...

#__author__ = 'Sumant'

# use of lambda function along with "filter, map & reduce"
xx = lambda a, b: a+b
print xx(2, 3)

lst = range(1, 6)

print "lst = " + str(lst)

print "filter functionality:"

print filter(lambda a: a+1 < 5, lst)

print "lst = " + str(lst)

print "maps functionality:"

print map(lambda a, b: a*b, lst, lst)

print "lst = " + str(lst)

print "reduce functionality:"

print reduce(lambda a, b: a * b, lst)

--------------------------------

OUTPUT

--------------------------------

5
lst = [1, 2, 3, 4, 5]
filter functionality:
[1, 2, 3]
lst = [1, 2, 3, 4, 5]
maps functionality:
[1, 4, 9, 16, 25]
lst = [1, 2, 3, 4, 5]
reduce functionality:
120

Process finished with exit code 0

Hope this link will be helpful ... :)

There are few very wonderful links where it is explained when & where to use "lambda" function:

https://pythonconquerstheuniverse.wordpress.com/2011/08/29/lambda_tutorial/