Python FAQ - Sandeep Kanao

 What are different data types in python – LSD mutable – Sandeep Kanao

 What are immutable and non-immutable objects – Sandeep Kanao

a mutable object can be changed after it is created, and an immutable object can't

How make class immutable – Sandeep Kanao

Option 1 :

The easiest way to do this is using __slots__

class A(object):

    __slots__ = []

Option 2 :

completely disallow __setattr__ and use object.__setattr__ in the constructor

class Point(object):

    def __init__(self, x, y):

        object.__setattr__(self, "x", x)

        object.__setattr__(self, "y", y)

    def __setattr__(self, *args):

        raise TypeError

what is difference between __new__ and __init__ – Sandeep Kanao

__new__ is the first step of instance creation. It's called first, and is responsible for returning a new instance of your class. In contrast,__init__ doesn't return anything; it's only responsible for initializing the instance after it's been created.

In general, you shouldn't need to override __new__ unless you're subclassing an immutable type like str, int, unicode or tuple.

How to find duplicates in List – Sandeep Kanao

>>> l = [1,2,3,4,4,5,5,6,1]

>>> set([x for x in l if l.count(x) > 1])

set([1, 4, 5])

 How to Compare to two lists and get the differences – Sandeep Kanao

temp1 = ['One', 'Two', 'Three', 'Four']

temp2 = ['One', 'Two']

Option 1 :

list(set(temp1) - set(temp2))    -- Use set

Option 2 :

s = set(temp2)

temp3 = [x for x in temp1 if x not in s]

Option 3:

Use python library

pip install deepdiff

Option 4 :

Use NumPy difference

What is Decorator – Sandeep Kanao

Decorators are functions which modify the functionality of another function. They help to make our code shorter

Python allows you to simplify the calling of decorators using the @ symbol (this is called “pie” syntax).

Decorators are callable objects which are used to modify functions or classes.

Callable objects are objects which accepts some arguments and returns some objects. functions and classes are examples of callable objects in Python.

Function decorators are functions which accepts function references as arguments and adds a wrapper around them and returns the function with the wrapper as a new function

classes can also be used to build decorators (example - log, emaillog(log)- change decorator from @long to @emaillog - to get extend log)

Everything in Python is an object

1. Nested functions - Defining functions within functions

def hi(name="sandeep"):

    print("inside the hi() function")

    def greet():

        return "inside the greet() function"

    def welcome():

        return "inside the welcome() function"

>>hi()

output :

inside the hi() function

inside the greet() function

inside the welcome() function

2. Returning functions from within functions

def hi(name="sandeep"):

    def greet():

        return "inside the greet() function"

    def welcome():

        return "inside the welcome() function"

    if name == "sandeep":

        return greet

    else:

        return welcome

>>a = hi()

>>print(a())

inside the greet() function

3. Giving a function as an argument to another function

def hi():

    return "hi sandeep!"

def doSomethingBeforeHi(func):

    print("inside doSomethingBeforeHi")

    print(func())

doSomethingBeforeHi(hi)

#outputs:inside doSomethingBeforeHi

#        hi sandeep!

Decorators :

def decorator_func(some_func):

  # define another wrapper function which modifies some_func

  def wrapper_func():

    print("Wrapper function started")

   

    some_func()

   

    print("Wrapper function ended")

   

  return wrapper_func # Wrapper function add something to the passed function and decorator returns the wrapper function

   

def say_hello():

  print ("Hello")

 

say_hello = decorator_func(say_hello)

say_hello()

# Output:

#  Wrapper function started

#  Hello

#  Wrapper function started

Syntax

@decorator_func

def say_hell():

    print 'Hello'

def say_hello():

    print 'Hello'

say_hello = deocrator_func(say_hello)

Functions with arguments and decorators, inner function must have same number of arguments

Where are decorators used -use case – Sandeep Kanao

1. Authorization - Flask web framework: Decorators can help to check whether someone is authorized to use an endpoint in a web application (@wraps)

2. Logging

3. Flask routing (@app.route(‘/hello’))

What are Generators

There is a lot of overhead in building an iterator in Python; we have to implement a class with __iter__() and __next__() method, keep track of internal states, raise StopIteration when there was no values to be returned etc.

If a function contains at least one yield statement (it may contain other yield or return statements), it becomes a generator function. Both yield and return will return some value from a function.

# A simple generator function

def my_gen():

    n = 1

    print('This is printed first')

    # Generator function contains yield statements

    yield n

    n += 1

    print('This is printed second')

    yield n

    n += 1

    print('This is printed at last')

    yield n

# Using for loop

for item in my_gen():

    print(item)   

output :

This is printed first

1

This is printed second

2

This is printed at last

3

Generator expressions – Sandeep Kanao

The syntax for generator expression is similar to that of a list comprehension in Python. But the square brackets are replaced with round parentheses.

The major difference between a list comprehension and a generator expression is that while list comprehension produces the entire list, generator expression produces one item at a time.

# Intialize the list

my_list = [1, 3, 6, 10]

a = (x**2 for x in my_list)

# Output: 1

print(next(a))

# Output: 9

print(next(a))

Why generators are used in Python – Sandeep Kanao

1. Easy to Implement

2. Memory Efficient

3. Represent Infinite Stream

4. Pipelining Generators

Differences between Generator function and a Normal function – Sandeep Kanao

·        Generator function contains one or more yield statement.

·        When called, it returns an object (iterator) but does not start execution immediately.

·        Methods like __iter__() and __next__() are implemented automatically. So we can iterate through the items using next().

·        Once the function yields, the function is paused and the control is transferred to the caller.

·        Local variables and their states are remembered between successive calls.

·        Finally, when the function terminates, StopIteration is raised automatically on further calls.

 What is Map – Sandeep Kanao

map functions expects a function object and any number of iterables like list, dictionary, etc. It executes the function_object for each element in the sequence and returns a list of the elements modified by the function object.

map(function_object, iterable1, iterable2,...)

def multiply2(x):

  return x * 2

   

map(multiply2, [1, 2, 3, 4])  # Output [2, 4, 6, 8]

example – adding elements in two lists

list_a = [1, 2, 3]

list_b = [10, 20, 30]

 

map(lambda x, y: x + y, list_a, list_b) # Output: [11, 22, 33]

 What is Filter – Sandeep Kanao

filter function expects two arguments, function_object and an iterable. function_object returns a boolean value

filter(function_object, iterable)

Unlike map function filter function can only have one iterable as input.

Example

a = [1, 2, 3, 4, 5, 6]

filter(lambda x : x % 2 == 0, a) # Output: [2, 4, 6]

What is Reduce

reduce(fun,seq)

functools.reduce(lambda a,b : a if a > b else b,lis)

functools.reduce(lambda a,b : a+b,lis)

What are different collection types in Python – Sandeep Kanao

What is tuple and how it is different than list – Sandeep Kanao

List	Tuple
mutable	immutable - Cannot change the values in a tuple once created
[]	{}
variable length	fixed length
lists show order	Tuples show structure
	Better performance

What is difference between list and dictionary

List	Dictionary
mutable	mutable
maintain their ordering	Ordering is not guaranteed
can be of any type, and types can be mixed	Key values can be of any hashtable type (i.e. not a dict) and types can be mixed Values can be of any type (including other dict’s), and types can be mixed
accessed via numeric (zero based) indices	accessed using key values
use a list if you have an ordered collection of items.	Use a dictionary when you have a set of unique keys that map to values

 What is @StaticMethod and  @ClassMethod – Sandeep Kanao

To define a class method in python, we use @classmethod decorator and to define a static method we use @staticmethod decorator.
Let us look at an example to understand the difference between both of them. Let us say we want to create a class Person. Now, python doesn’t support method overloading like C++ or Java so we use class methods to create factory methods.

# use of class method and static method.

from datetime import date

class Person:

    def __init__(self, name, age):

        self.name = name

        self.age = age

     

    # a class method to create a Person object by birth year.

    @classmethod

    def fromBirthYear(cls, name, year):

        return cls(name, date.today().year - year)

     

    # a static method to check if a Person is adult or not.

    @staticmethod

    def isAdult(age):

        return age > 18

person1 = Person('mayank', 21)

person2 = Person.fromBirthYear('mayank', 1996)

print person1.age

print person2.age

# print the result

print Person.isAdult(22)

output

21

21

True

When to use what?

§  We generally use class method to create factory methods. Factory methods return class object ( similar to a constructor ) for different use cases.

§  We generally use static methods to create utility functions.

 What is if __name__ == "__main__" – Sandeep Kanao

When you execute a Python script , it is treated as the main and its __name__ attribute is set to "__main__" . If you import this script as a module in another script, the __name__ is set to the name of the script/module.

By doing the main check, you can have that code only execute when you want to run the module as a program and not have it execute when someone just wants to import your module

 How Python handles multiple inheritance (Diamond problem in Python) - Method Resolution Order (MRO) – Sandeep Kanao

Python 2.x

depth-first and then left-to-right

What is GIL – Sandeep Kanao

A global interpreter lock (GIL) is a mechanism used in computer-language interpreters to synchronize the execution of threads so that only one native thread can execute at a time.[1] An interpreter that uses GIL always allows exactly one thread to execute at a time, even if run on a multi-core processor.

What is __REPR__

Dunder or magic methods in Python are the methods having two prefix and suffix underscores in the method name. Dunder here means “Double Under (Underscores)”. These are commonly used for operator overloading. Few examples for magic methods are: __init__, __add__, __len__, __repr__ etc.

 How to include python module (~/dirone/1.py) to another python module (~/dirtwo/2.py)

1. from ../dir1 import  1.py

2. Update PYTHONPATH env variable

3. import sys

sys.path.insert(1, '.')

 Explain Docstring

Python documentation strings (or docstrings) provide a convenient way of associating documentation with Python modules, functions, classes, and methods.

def sum(x, y):

  """Returns arg1 value add to arg2 value."""

  return a+b

print sum.__doc__

output

Returns arg1 value add to arg2 value.

 What is difference between Global and Return – Sandeep Kanao

Global variable means that we can access that variable outside the function as well

 What is named tuple

namedtuple is a factory function for making a tuple class. With that class we can create tuples that are callable by name also

from collections import namedtuple

Point = namedtuple('Point', 'x y')

pt1 = Point(1.0, 5.0)

pt2 = Point(2.5, 1.5)

from math import sqrt

line_length = sqrt((pt1.x-pt2.x)**2 + (pt1.y-pt2.y)**2)

Explain file manipulation and 'with' – Sandeep Kanao

with open('output.txt', 'w') as f:

     f.write('Hi there!')

The above with statement will automatically close the file after the nested block of code, even if exception occurs                                               

What is the difference between deep and shallow copy? – Sandeep Kanao

In case of deep copy, a copy of object is copied in other object. It means that any changes made to a copy of object do not reflect in the original object.

In python, this is implemented using “deepcopy()” function.

# importing "copy" for copy operations

import copy

# initializing list 1

li1 = [1, 2, [3,5], 4]

# using deepcopy to deep copy

li2 = copy.deepcopy(li1)

Shallow copy

In case of shallow copy, a reference of object is copied in other object. It means that any changes made to a copy of object do reflect in the original object.

In python, this is implemented using “copy()” function

# importing "copy" for copy operations

import copy

# initializing list 1

li1 = [1, 2, [3,5], 4]

# using copy to shallow copy

li2 = copy.copy(li1)

Find by value in dicionary

mydict = {'george':16,'amber':19}

option 1 ;

[name for name, age in mydict.items() if age == search_age]

option2 ;

p = dict(zip(mydict.values(),mydict.keys()))

option 3:

import pandas as pd

mydict = {'george':16,'amber':19}

mydict_lookup = pd.Series(mydict)

lookup_list[mydict_lookup.values == 19]

option 4 :

lookup = {value: key for key, value in mydict }

lookup[value]

How is memory managed in Python? – Sandeep Kanao

Memory management in Python involves a private heap containing all Python objects and data structures. The management of this private heap is ensured internally by the Python memory manager. At the lowest level, a raw memory allocator ensures that there is enough room in the private heap for storing all Python-related data by interacting with the memory manager of the operating system.

What is the usage of help() and dir() function in Python? – Sandeep Kanao

What is monkey patching in Python? – Sandeep Kanao

Monkey patching is changing the behavior of a function or object after it has already been defined. For example:

import datetime

datetime.datetime.now = lambda: datetime.datetime(2012, 12, 12)

Used for testing

Write a one-liner that will count the number of capital letters in a file. Your code should work even if the file is too big to fit in memory.

count sum(1 for line in fh for character in line if character.isupper())

Write a sorting algorithm for a numerical dataset in Python. – Sandeep Kanao

list = ["1", "4", "0", "6", "9"]

list = [int(i) for i in list]

list.sort()

print (list)

What is pickling and unpickling? – Sandeep Kanao

It is used for serializing and de-serializing a Python object structure. Any object in python can be pickled so that it can be saved on disk (dump)

How to get indices of N maximum values in a NumPy array?

Explain zip – Sandeep Kanao

zip takes n number of iterables and returns list of tuples

list_a = [1, 2, 3, 4, 5]

list_b = ['a', 'b', 'c', 'd', 'e']

zipped_list = zip(list_a, list_b)

print zipped_list # [(1, 'a'), (2, 'b'), (3, 'c'), (4, 'd'), (5, 'e')]

If the length of the iterables are not equal, zip creates the list of tuples of length equal to the smallest iterable.

zip always creates the tuple in the order of iterbales from left to right

To unzip a list of tuples we zip(*listP_of_tuples). Unzip creates separate list

zipper_list = [(1, 'a'), (2, 'b'), (3, 'c')]

 list_a, list_b = zip(*zipper_list)

 print list_a # (1, 2, 3)

 print list_b # ('a', 'b', 'c')

Pass – is no-operation / action statement in Python-statement of Python is used whenever a statement is required syntactically but the program needs no action

 find vovels in string – Sandeep Kanao

final = [each for each in string if each in vowels]

How to create an Enum in Python? – Sandeep Kanao

from enum import Enum

class Directions(Enum):

    East, West,North,South = range(4)

print(Directions.North.value) ##output 2

What are the drawbacks of Python? – Sandeep Kanao

Disadvantages of Python are:

Speed

Python is slower than C or C++. But of course, Python is a high-level language, unlike C or C++ it's not closer to hardware.

Mobile Development

Python is not a very good language for mobile development . It is seen as a weak language for mobile computing. This is the reason very few mobile applications are built in it like Carbonnelle.

Memory Consumption

Python is not a good choice for memory intensive tasks. Due to the flexibility of the data-types, Python's memory consumption is also high.

Database Access

Python has limitations with database access . As compared to the popular technologies like JDBC and ODBC, the Python's database access layer is found to be bit underdeveloped and primitive . However, it cannot be applied in the enterprises that need smooth interaction of complex legacy data .

Runtime Errors

Python programmers cited several issues with the design of the language. Because the language is dynamically typed , it requires more testing and has errors that only show up at runtime .