Python Concepts

January 16, 2016
Development

Most of the explaination is coming from the references below. This is just a collection of refrances and thir comments to make it easy for me and for a python programmer to learn the consepts.

ContextLib: Descriptors: MetaClasses: Properties:

Generators

These are good references:

Generator comprehension

Example of a list comprehension:

>>> mylist = [num * num for num in range(3)]
>>> for i in mylist:
>>>     print(i)

Problem with list is that it stores all the values in memory where as generator is store only the current value and iterate on the next value when called again. With Generator you don’t have history of old values.

>>> mygenerator = (num * num for num in range(3))
>>> for i in mygenerator:
...     print(i)

This is an example of a generator comprehension inside a function that will return a generator:

def gen_square(nums):
    return (num * num for num in nums)

Generator function

A function with a yield is a generator function. It returns a generator. It is not like return statment. It has diffrant behavior than return:

#!/usr/bin/env python

def gen_square(nums):
    print('begin generator')
    for num in nums:
        yield num * num
        print('after generator')

squares = gen_square([4, 2])  # create a generator
print('made generator')
for square in squares:
    print('control in caller')
    print(square)

Output would be:

made generator
begin generator
control in caller
16
after generator
control in caller
4
after generator

When creating a generator no code will be run until you iterate over it and then it will reach yield and stop there and when called another time it will just read the code that is insie the loop until finished and then it will stop iteration.

lambda

Anonymous functions

>>> f = lambda x,y: x+y
>>> f(1,2)
3

Ideas:

a = [1, 2]
if 3 not in a:
    return False
======
a = [1, 2]
assert 3 in a

Closures

More Resourses:

Optimize Python with Closures

https://wrongsideofmemphis.wordpress.com/2015/05/08/optimise-python-with-closures/

tooling:

gofmt
golint,govet
goimports
go test-race

Decorators

A decorator is just a callable that takes a function as an argument and returns a replacement function. Reasons for using decorators:

Flexible and generic
DRY
Testable

http://stackoverflow.com/a/309000

from functools import wraps
def logged(func):
    @wraps(func)
    def with_logging(*args, **kwargs):
        print func.__name__ + " was called"
        return func(*args, **kwargs)
    return with_logging

Context Managers

Two examples:

class CustomOpen(object):
    def __init__(self, filename):
      self.file = open(filename)

    def __enter__(self):
        return self.file

    def __exit__(self, ctx_type, ctx_value, ctx_traceback):
        self.file.close()

with CustomOpen('file') as f:
    contents = f.read()

from contextlib import contextmanager

@contextmanager
def custom_open(filename):
    f = open(filename)
    try:
        yield f
    finally:
        f.close()

with custom_open('file') as f:
    contents = f.read()

More Resourses:

Introspection

It is the ability of a program to examine the type or properties of an object at runtime. Example: dir(var), type(var) …