What's an object? An object in a object-oriented programming language is an entity that contains data along with some methods for operations related to that data.
Everything from numbers, lists, strings, functions and classes are python objects.

>>> a = 10.5
>>> a.is_integer() # Float type has is_integer() method cause a is an object of float class
False
>>> type(a)
<class 'float'>
>>> def func()
....    pass
>>> type(func)
<class 'function'>
>>> # like functions, classes are also objects of 'type' class

Look at the below example

>>> var = 'Tom' # Object 'Tom' is created in memory and name 'var' is binded to it. 
>>> var = 'Harry' # Another object is created however note that name 'var' is now binded to 'Harry' but 'Tom' is still somewhere in memory and is unaffected.

Ref: Nina Zakharenko - Memory Management in Python - The Basics - PyCon 2016

Mutation generally refers to 'change'. So when we say that an object is mutable or immutable we meant to say that the value of object can/cannot change.
When an object is created in Python, it is assigned a type and an id. An object/data type is mutable if with the same id, the value of the object changes after the object is created.

Mutable objects in Python -- Objects that can change after creation. Lists, byte arrays, sets, and dictionaries.

>>> list_var = [17, 10]
>>> list_var
[17, 10]
>>> id(list_var)
2289772854208
>>> list_var += [17]
>>> list_var
[17, 10, 17]
>>> id(list_var) # ID of the object didn't change.
2289772854208

Immutable objects in Python -- Numeric data types, strings, bytes, frozen sets, and tuples.

>>> # Example of tuples
>>> tuple_var = (17,)
>>> tuple_var
(17,)
>>> id(tuple_var)
1753146091504
>>> tuple_var += (10,)
>>> tuple_var
(17,10)
>>> id(tuple_var) # ID changes when made changes in object.
1753153466880

Mutable objects and functon arguments

def sample_func(sample_arg):
    sample_agr.append(10)
    # No need to return the obj since it is utilizing the same memory block

sample_list = [7, 8, 9]
sample_func(sample_list)
print(sample_list) # [7, 8, 9, 10]

Note: It is not required for tuples to have parenthesis, one can also define tuple python a = 2, 3, 4

Tuple does not allot any extra memory during construction because it will be immutable so does not have to worry about addition of elements.

>>> tuple_var = tuple()
>>> tuple_var.__sizeof__() # take 24 bytes for empty tuple
24
>>> tuple_var = (1,2) # additional 8 bytes for each integer element
>>> tuple_var.__sizeof__()
40

List over-allocates memory otherwise list.append would be an O(n) operation.

>>> list_var = list()
>>> list_var.__sizeof__() # take 40 bytes for empty list
40
>>> list_var.append(1)
>>> list_var.__sizeof__() # append operation allots extra memory size considering future appends
72
>>> list_var
[1]
>>> list_var.append(2)
>>> list_var.__sizeof__() # size remains same since list has space available
72
>>> list_var
[1,2]

Tuple literally assigns the same object to the new variable while list basically copies all the elements of the existing list.

>>> # List vs Tuples | Reused vs. Copied
>>> old_list = [1,2]
>>> old_list.append(3)
>>> old_list
[1, 2, 3]
>>> id(old_list) 
2594206915456
>>> old_list.__sizeof__()
88

>>> # Copying list
>>> new_list = list(old_list)
>>> new_list
[1, 2, 3]
>>> id(new_list) # new id so new list is created
2594207110976
>>> new_list.__sizeof__() # size is also not same as old_list
64

>>> Tuple Copy
>>> old_tuple = (1,2)
>>> id(old_tuple)
2594206778048
>>> old_tuple.__sizeof__()
40
>>> new_tuple = tuple(old_tuple)
>>> id(new_tuple) # same id as old_tuple
2594206778048
>>> new_tuple.__sizeof__() # also same size as old_tuple since it is refering to old_tuple
40

Tuples and List takes almost same time in indexing, but for construction, tuple destroys list. See example, 'List vs Tuple'.

Unlike other programming languages, python stores references to an object after it is created. For example, an integer object 17 might have two names(variables are called names in python) a and b. The memory manager in python keeps track of the reference count of each object, this would be 2 for object 17. Once the object reference count reaches 0, object is removed from memory.
The reference count

• increases if an object is assigned a new name or is placed in a container, like tuple or dictionary.
• decreases when the object's reference goes out of scope or when name is assigned to another object. Python's garbage collector handles the job of removing objects & a programmer need not to worry about allocating/de-allocating memory like it is done in C.

>>> import sys
>>> sys.getrefcount(17)
>>> 11
>>> a = 17
>>> b = 17
>>> a is b
>>> True
>>> sys.getrefcount(17)
>>> 13 # addition of two

Exception handling is the way by which a programmer can control an error within the program without breaking out the flow of execution.

try:
    # Part which might cause an error
except TypeError:
    # What happens when error occurs | In this case what happens what a TypeError occurs
else:
    # what happens if there is no exception | Optional
finally:
    # Executed after try and except| always executed | Optional

Examples :- TypeError, ValueError, ImportError, KeyError, IndexError, NameError, PermissionError, EOFError, ZeroDivisionError, StopIteration

Positional arguements representation

def sum(a,b,/,c=10):
    return a+b+c
sum(10,12,c=12)

F String can also do operations

a,b = 10, 12
f"Sum of a and b is {a+b}"
f"Value of c is {(c := a+b)}"

Ref: https://www.youtube.com/watch?v=o1FZ_Bd4DSM

The best way is to load data in chunks, Pandas provides option to define chunk size while loading any file.

for chunk in pd.read_csv(file.csv, chunksize=1000):
    process(chunk)
    
pd.read_csv('file.csv', sep='\t', iterator=True, chunksize=1000)

Another way is to use context manager.

with open("log.txt") as infile:
    for line in infile:
        do_something_with(line)

A function or method which uses the yield statement is called a generator function. Such a function, when called, always returns an iterator object which can be used to execute the body of the function: calling the iterator’s iterator._next_()method will cause the function to execute until it provides a value using the yield statement.
When the function executes a return statement or falls off the end, a StopIteration exception is raised and the iterator will have reached the end of the set of values to be returned. Note that a generator can have n numbers of yield statements

Generators are good for calculating large sets of results where you don't know if you are going to need all results, or where you don't want to allocate the memory for all results at the same time.

# Search function as generator, effective for returning some set as result with functionality like 'Load 10 more items'
def search_result(keyword):
    while keyword in dataset:
        yield matched_data

search_object = search_result('keyword')
# type(search_function)  --> <class 'generator'>

search_object.__next__()

Note: You can only iterate over a generator once, if you try to loop over it second time it will return nothing. Generators also do not store all the values in memory, they generate the values on the fly

mygenerator = (x*x for x in range(3))

When you call a generator function, the code you have written in the function body does not run. The function only returns the generator object. Example: https://github.com/baliyanvinay/Python-Advanced/blob/main/Generator.py

Yes can be defined in a tuple. From left to right will be executed based on the exception raised.

try:
    pass
except (TypeError, IndexError, RuntimeError) as error:
    pass

A closure is a functionality in Python where some data is memorized in the memory for lazy execution. Decorators heavily rely on the concept of closure.
To create a closure in python:-

1. There must be a nested function(function within a enclosing/outer function)
2. The nested function must refer to a value defined in the enclosing function
3. The enclosing function must return(not call it) the nested function.

def enclosing_function(defined_value):
    def nested_function():
        return defined_value+some_operation
    return nested_function

closure_function = enclosing_function(20)
closure_function() # returns 20+some_operation

Objects are data with methods attached, closures are functions with data attached.

def make_bold(fn):
    def wrapped():
        return "<b>" + fn() + "</b>"
    return wrapped

def make_italic(fn):
    def wrapped():
        return "<i>" + fn() + "</i>"
    return wrapped

@make_bold
@make_italic
def index():
    return "hello world"

print(index()) 
## returns "<b><i>hello world</i></b>"

Using index jump

>>> example_list = [0,1,2,3,4,5,6]
>>> example_list = [::3] # returns [0,3,6]

Using list comphrehension

>>> [x for x in example_list if example_list.index(x)%3==0]
>>> [0,3,6]

Using while loop

i = 0
while i < len(example_list):
    print(example_list[i])
    i += 3

Method Resolution Order (MRO) is the order in which Python looks for a method in a hierarchy of classes. Especially it plays vital role in the context of multiple inheritance as single method may be found in multiple super classes.

class A:
    def process(self):
        print('A')
        
class B(A):
    pass
    
class C(A):
    def process(self):
        print('C')

class D(B,C):
    pass
    
obj = D()
obj.process()
# D -> B -> C -> A -> object 

Note: a class can't be called before its superclass in resolving MRO. Super Class has to be called after derived class

Monkey patching refers to the practice of dynamically modifying or extending code at runtime, typically to change the behavior of existing classes, modules, or functions.

Monkey patching can be useful in several scenarios:

1. Testing: It allows to replace parts of code during testing with mock objects or functions.
2. Hotfixes: In situations where you can't immediately deploy a fix to production code, monkey patching can be used as a temporary solution to address critical issues without waiting for a formal release cycle.
3. Extending functionality: It enables to add new features or alter the behavior of existing code without modifying the original source. This can be useful when working with third-party libraries or legacy code that you can't modify directly.

# Original module
class OriginalClass:
    def method(self):
        return "Original method"

# Monkey patching
def new_method(self):
    return "Patched method"

# Patching the class method
OriginalClass.method = new_method
# Using the patched code
obj = OriginalClass()
print(obj.method())  # Output: "Patched method"

class Circle:
    no_of_circles = 0
    def __init__(self, radius):
        self.radius = radius
        Circle.no_of_circles += 1
        
    @staticmethod
    def square(num):
        return num**2
    
    @classmethod
    def getCircleCount(cls):
        return cls.no_of_circles

Python automatically stores the value of last expression in interpreter in single underscore.

>>> 78 + 89
167
>>> _
167

Single underscore used in unpacking to ignore value(s).

>>> a, _, b = (1, 12, 2)
>>> a, *_, b = (1, 12, 13, 14, 15, 16, 2) # only want first and last value
>>> a, *x, b = (1, 12, 13, 14, 15, 16, 2) # note that any other name can also be used
>>> _ # returns a list of elements
[12, 13, 14, 15, 16]

It is most commonly used in loops where we are not interested in the value returned by the iterator.

for _ in range(5):
    print('Some operations')

Note that it is entirely the convention to use single underscore in all these ways to avoid having defined extra variable for such operations.

The module is a Python file that contains collections of functions and global variables and with having a .py extension file.

The package is a directory having collections of modules. This directory contains Python modules and also having init.py file by which the interpreter interprets it as a Package.

Ref: https://www.geeksforgeeks.org/what-is-the-python-global-interpreter-lock-gil/

List comprehensions are generally faster than 'a for loop' because of the implementation of both. One key difference is that 'for loop' generally rounds up more than one statement/operation as compared to 'list comprehension' which has to perform single operation on all the elements. For example, creating a list or update in an existing list is faster when done using list comprehension.

Refer to Python Advanced : Design Patterns

In Python everything is an object, even a class is an object. As a result, a class also must have a type. All classes in Python are of 'type' type. Even the class of 'type' is 'type'. So 'type' is the meta class in Python and to create custom meta class, you would need to inherit from 'type'.

A meta class is the class of a class. A class is an instance of a metaclass. A metaclass is most commonly used as a class-factory. When you create an object by calling the class, Python creates a new class (when it executes the 'class' statement) by calling the metaclass.

>>> type(17) # <class 'int'>
>>> type(int) # <class 'type'>
>>> str.__class__ # <class 'type'>
>>> type.__class__ # <class 'type'>

The metaclass is called with the

• name: name of the class,
• bases: tuple of the parent class (for inheritance, can be empty) and
• attributes: dictionary containing attributes names and values.

def init(self, make):
    self.make = make

# type(name, bases, attrs) 
>>> Car = type('Car', (object,), {'__init__': init, '__repr__': lambda self: self.make,  'wheels': 4})
>>> seltos = Car('Kia')
>>> seltos # Kia

Ref: Stack Overflow : Meta Classes

The best way of appending a string to a string variable is to use + or +=. This is because it's readable and fast. However in most of the codebases we see use of append and join when joining strings together, this is done for readablity and cleaner code. Sometimes it is more important to have code readablity to actually understand the operation.

first_name = 'Max '
last_name = 'Verstappen'
full_name = first_name + last_name
# using join string method
full_name = ''.join( (first_name, last_name) ) # takes in tuple of string in case of multiple values

Map function returns an iterator that applies function to every item in the iterable. In case multiple iterables are passed, the iterator stops when the shortest iterable is exhausted.

def custom_power(x, y):
    return x**y

values = [1,2,3,4]
powers = [2,1,2] 
map_iterator = map(custom_power, values, powers) # will skip the power of 4
print(list(map_iterator)) # [1,2,9]

Lambda expression yields an anonymous function object. Note that functions created with lambda expressions cannot contain statements or annotations. For example, can't assign variables in lambda definition.

>>> # lambda [parameter list] : expression
>>> list(map(lambda x: x+10, [1,2,3])) # [11,12,13]
>>> func =  lambda x: x+10 # <function <lambda> at 0x7fdb99e9c310>
>>> func(25) # returns 35
>>> lambda : 'hello' # with no parameter

An abstract class can be considered as a blueprint for other classes. It allows you to create a set of methods that must be created within any child classes built from the abstract class. Or in more general terms, a class which contains one or more abstract methods is called an abstract class.
By default Python does not provide abstract class, ABC module of Python can be used to define an abstract class.

Abstract method is a method that has a declaration but does not have an implementation. This ensures that any class built from this class will have to implement the method.

from abc import ABC, abstractmethod
class DB_PLugin(ABC):
    @abstractmethod
    def add_source(self):
        pass

When an object of a class is created or a class is instantiated, the _new_() method of class is called. This particular method is resposible for returning a new class object. It can be overriden to implement object creational restrictions on class.

• The constructor of the class is _new_() and
• the initializer of the class is _init_().
  Initializer is called right after the constructor, if the constructor has not returned a class object, the initializer call is useless.
  Note that the reason _init_() could use class object(self) to initialize is because when the code flow reaches _init_() the object of the class is already created.

class Car:
    total_cars, wheels = 0, 4
    def __init__(self, engine_power):
        self.engine_power =  engine_power
        Car.total_cars += 1 # incremented anytime a new car is added. 
        
kia_sonet = Car(120)
print(kia_sonet.wheels) # returns 4
kia_sonet.wheels += 1 # extra wheel
print(kia_sonet.wheels) # returns 5

print(Car.total_cars) # returns 1
print(Car.wheels) # returns 4 not 5
Car.wheel = 6 # two extra wheels

print(kia_sonet.wheels) # returns 6 now

Example: https://github.com/baliyanvinay/Python-Advanced/blob/main/Class%20Variables.py

• A dictionary consists of a collection of key-value pairs. Each key-value pair maps the key to its associated value.
• A key can appear in a dictionary only once. Duplicate keys are not allowed
• Using a key twice in initial dict definition will override the first entry
• Key must be of a type that is immutable. Values can be anything

>>> dict_sample_01 = {1: 12, 2: 14, 1: 16}
>>> dict_sample_02 # {1: 16, 2: 14}
>>> dict_sample_02 = dict.fromkeys('123')
>>> dict_sample_02 # {'1': None, '2': None, '3': None}

A statement is a complete line of code that performs some action, while an expression is any section of the code that evaluates to a value. An expression is also a statement. Note that lambda function in Python only accepts expressions.

You can't add a list to a set because lists are mutable, meaning that you can change the contents of the list after adding it to the set. You can however add tuples to the set, because you cannot change the contents of a tuple.
The objects have to be hashable so that finding, adding and removing elements can be done faster than looking at each individual element every time you perform these operations.
Some unhashable datatypes:

• list: use tuple instead
• set: use frozenset instead

Yes method overloading is possible in Python. It can be achived using different number of arguments.

def increment(value, by=1):
   return value+by

# calling function
increment(5) # returns 6
increment(5, 2) # return 7

Any immutable object type can be used as dictionary key even functions and classes can also be used as dictionary keys.

Dict implementation reduces the average complexity of dictionary lookups to O(1) by requiring that key objects provide a "hash" function. Such a hash function takes the information in a key object and uses it to produce an integer, called a hash value. This hash value is then used to determine which "bucket" this (key, value) pair should be placed into. Mutuable objects like list or dict cannot provide a valid /hash method.

For collections that are mutable or contain mutable items, a copy is sometimes needed so one can change one copy without changing the other.
Python follows a pattern of compiling the original source to byte codes, then interpreting the byte codes on a virtual machine. The .pyc file generated contains byte code.

>>> import copy
>>> sample_1 = [1,2,3]
>>> id(sample_1)
139865052152768
>>> sample_2 = sample_1
>>> id(sample_2)
139865052152768
>>> sample_3 = copy.copy(sample_1)
>>> id(sample_3)
139865052236736

• A shallow copy constructs a new compound object and then (to the extent possible) inserts references into it to the objects found in the original.
• A deep copy constructs a new compound object and then, recursively, inserts copies into it of the objects found in the original.

Ref: Python Docs Copy

.pyc files are created by the Python interpreter when a .py file is imported, and they contain the "compiled bytecode" of the imported module/program, the idea being that the "translation" from source code to bytecode (which only needs to be done once) can be skipped on subsequent imports if the .pyc is newer than the corresponding .py file, thus speeding startup a little. But it's still interpreted.

Python does not have anything called private member however by convention two underscore before a variable or function makes it private.

class XSpecial:
    normal_var = 10
    __private_var = 17

>>> special_obj = XSpecial()
>>> special_obj.normal_var
>>> special_obj.__private_var # AttributeError

An iterator is an object that implements /next, which is expected to return the next element of the iterable object, and raise a StopIteration exception when no more elements are available.

products = {}
products.update(
    dict.keys(['Apple','Mango','Oranges'], 20)
)
products.update(
    dict.keys(['Pizza','Kind Pizza','Bad Pizza'], 30)
)

Namespace is a way to implement scope. In Python, each package, module, class, function and method function owns a "namespace" in which variable names are resolved. Plus there's a global namespace that's used if the name isn't in the local namespace.
Each variable name is checked in the local namespace (the body of the function, the module, etc.), and then checked in the global namespace.

• Local Namespace: The local namespace for a function is created when the function is called, and deleted when the function returns or raises an exception that is not handled within the function.
• Global Namespace: The global namespace for a module is created when the module definition is read in; normally, module namespaces also last until the interpreter quits.
• Built-in Namespace: The namespace containing the built-in names is created when the Python interpreter starts up, and is never deleted.

class Parent:
    variable = 12
    
class Derived(Parent):
    variable = 10

Parent.variable # returns 12

• The expression x and y first evaluates x; if x is false, its value is returned; otherwise, y is evaluated and the resulting value is returned.
• The expression x or y first evaluates x; if x is true, its value is returned; otherwise, y is evaluated and the resulting value is returned.

x = 'Some Value'
y = 24
z = False
x or y # returns x
z or y # returns y
x and y # returns y
z and x # returns z

The threading module uses threads, the multiprocessing module uses processes. The difference is that threads run in the same memory space, while processes have separate memory. This makes it a bit harder to share objects between processes with multiprocessing. Since threads use the same memory, precautions have to be taken or two threads will write to the same memory at the same time.

• Multithreading is concurrent and is used for IO-bound tasks
• Multiprocessing achieves true parallelism and is used for CPU-bound tasks Use Multithreading if most of your task involves waiting on API-calls, because why not start up another request in another thread while you wait, rather than have your CPU sit idly by.

first_dict = {'name': 'Tom', 'age': 44}
second_dict = {'occupation': 'actor', 'nationality': 'British'}
# merging
final_dict = {**first_dict, **second_dict}

In case any key is repeated in both dictionaries, the second key will hold supremacy.

def func(sample_list=[]):
    sample_list.append(12)
    # print(id(sample_list))
    return sample_list

print(func()) # [12]
print(func()) # [12,12]

Since list is mutualable type of data structure, the first time func is called, the list is empty, but when the same function is called twice, the list already has an item. We cans sure of the that by printing the id of the sample_list used in the first, on each subsquent call to the function, the id will return the same value.

filter(function, iterable) # function must return True or False

input_list = ['Delhi', 'Mumbai', 'Noida, 'Gurugram']
to_match = 'Gurugram'

matched_list = list(filter(lambda item: item == to_match, input_list))
matched_list # ['Gurugram']

For every single item in the input_list, the condition is checked in the lambda function which returns either True or False.

• A context manager in Python is a protocol implemented using the with statement.
• It allows you to perform setup and teardown operations around a block of code.
• Commonly used to manage resources like files, database connections, locks, etc.

class MyContextManager:
    def __enter__(self):
        # Perform setup operations
        return self

    def __exit__(self, exc_type, exc_value, traceback):
        # Perform cleanup operations

# Using the context manager
with MyContextManager() as cm:
    # Code inside the context

# Transpose a square matrix of n rows and columns
# Write a function that will place even elements of an array at even indexes and odd at odd indexes. 
# Write a function that checks if an array is a subsequence of first array
# Write a function that unzips nested dicts into expanded dicts.
# What is the output of (0,1,2,3,(4,5,6),7,8)[::3]
# Output of a=[[]]*5, a[0].append(1)
# Of a given string, make it an accept python variable name
# from a given list, return the duplicate elements only in sorted order