Python Revision P4 (Scopes Namespace, Python Standard Library, Classes, OOPs, Error Exceptions, Date and Times)

Python Summary Cheat Sheet - 4

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Scopes & NameSpaces

Object: In general, anything that can be assigned to a variable in Python is referred to as an object. Strings, Integers, Floats, Lists, Functions, Module etc. are all objects.

Namespaces: A namespace is a collection of currently defined names along with information about the object that the name references. It ensures that names are unique and won’t lead to any conflict.

def greet_1():
    a = "Hello"
    print(a)
    print(id(a))

def greet_2():
    a = "Hey"
    print(a)
    print(id(a))

print("Namespace - 1")
greet_1()
print("Namespace - 2")
greet_2()

# Output is:
Namespace - 1
Hello
140639382368176
Namespace - 2
Hey
140639382570608

Types of namespaces:

Built-in Namespace: Created when we start executing a Python program and exists as long as the program is running. This is the reason that built-in functions like id(), print() etc. are always available to us from any part of the program.

Global Namespace: This namespace includes all names defined directly in a module (outside of all functions). It is created when the module is loaded, and it lasts until the program ends.

Local Namespace: Modules can have various functions and classes. A new local namespace is created when a function is called, which lasts until the function returns.

Scope of a Name:

In Python, the scope of a name refers to where it can be used. The name is searched for in the local, global, and built-in namespaces in that order.

Global variables: In Python, a variable defined outside of all functions is known as a global variable. This variable name will be part of Global Namespace.

x = "Global Variable"
print(x) # Global Variable

def foo():
   print(x) # Global Variable

foo()

Local Variables: In Python, a variable defined inside a function is a local variable. This variable name will be part of the Local Namespace.

def foo():
   x = "Local Variable"
   print(x) # Local Variable

foo()
print(x) # NameError: name 'x' is not defined

Local Variables & Global Variables:

x = "Global Variable"

def foo():
    x = "Local Variable"
    print(x)

print(x)
foo()
print(x) 

# Output is:
Global Variable
Local Variable
Global Variable

Modifying Global Variables: global keyword is used to define a name to refer to the value in Global Namespace.

x = "Global Variable"

def foo():
    global x    
    x = "Global Change"
    print(x)

print(x)
foo()
print(x)

# Output is:
Global Variable
Global Change
Global Change

Python Standard Library

The collection of predefined utilities is referred as the Python Standard Library. All these functionalities are organized into different modules.

Module: In Python context, any file containing Python code is called a module.

Package: These modules are further organized into folders known as packages.

Importing module: To use a functionality defined in a module we need to import that module in our program.

import module_name

Importing from a Module: We can import just a specific definition from a module.

from math import factorial
print(factorial(5)) # 120

Aliasing Imports: We can also import a specific definition from a module and alias it.

from math import factorial as fact
print(fact(5)) # 120

Random module: Randomness is useful in whenever uncertainty is required.
Example: Rolling a dice, flipping a coin, etc,.

random module provides us utilities to create randomness.

Randint: randint() is a function in random module which returns a random integer in the given interval.

import random
random_integer = random.randint(1, 10)
print(random_integer) # 8

Choice: choice() is a function in random module which returns a random element from the sequence.

import random
random_ele = random.choice(["A","B","C"])
print(random_ele) # B

Classes

Classes: Classes can be used to bundle related attributes and methods. To create a class, use the keyword class

class className:
    attributes
    methods

Self: self passed to method contains the object, which is an instance of class.

Special Method: In Python, a special method __init__ is used to assign values to attributes.

class Mobile:
    def __init__(self, model):
        self.model = model

Instance of Class: Syntax for creating an instance of class looks similar to function call. An instance of class is an Object.

class Mobile:
    def __init__(self, model):
        self.model = model

mobile_obj = Mobile("iPhone 12 Pro")

Class Object: An object is simply a collection of attributes and methods that act on those data.

class Mobile:
    def __init__(self, model):
        self.model = model
    def make_call(self,number):
        return "calling..{}".format(number)

Attributes of an Object: Attributes can be set or accessed using . (dot) character.

class Mobile:
    def __init__(self, model):
        self.model = model

obj = Mobile("iPhone 12 Pro")
print(obj.model) # iPhone 12 Pro

Accessing in Other Methods: We can also access and update attributes in other methods.

class Mobile:
    def __init__(self, model):
        self.model = model

    def get_model(self):
        print(self.model) # iPhone 12 Pro

obj_1 = Mobile("iPhone 12 Pro")
obj_1.get_model()

Updating Attributes: It is recommended to update attributes through methods.

class Mobile:
    def __init__(self, model):
        self.model = model

    def update_model(self, model):
        self.model = model

obj_1 = Mobile("iPhone 12")
obj_1.update_model("iPhone 12 Pro")
print(obj_1.model) # iPhone 12 Pro

Instance Attributes: Attributes whose value can differ for each instance of class are modelled as instance attributes.

Accessing Instance Attributes: Instance attributes can only be accessed using instance of class.

class Cart:
  def __init__(self):
      self.items = {'book': 3}
  def display_items(self):
      print(self.items)  # {'book': 3}

a = Cart()
a.display_items()

Class Attributes: Attributes whose values stay common for all the objects are modelled as Class Attributes.

Accessing Class Attributes:

class Cart:
   flat_discount = 0
   min_bill = 100
   def __init__(self):
       self.items = {}

print(Cart.min_bill) # 100

Updating Class Attribute:

class Cart:
   flat_discount = 0
   min_bill = 100
   def print_min_bill(self):
       print(Cart.min_bill) # 200
a = Cart()
b = Cart()
Cart.min_bill = 200
b.print_min_bill() 

Methods: Broadly, methods can be categorized as

• Instance Methods
• Class Methods
• Static Methods

Instance Methods: Instance methods can access all attributes of the instance and have self as a parameter.

class Cart:
   def __init__(self):
       self.items = {}
   def add_item(self, item_name,quantity):
       self.items[item_name] = quantity
   def display_items(self):
       print(self.items) # {'book': 3}

a = Cart()
a.add_item("book", 3)
a.display_items()

Class Methods: Methods which need access to class attributes but not instance attributes are marked as Class Methods. For class methods, we send cls as a parameter indicating we are passing the class.

class Cart:
   flat_discount = 0
   @classmethod
   def update_flat_discount(cls, new_flat_discount):
       cls.flat_discount = new_flat_discount

Cart.update_flat_discount(25)
print(Cart.flat_discount) # 25

Static Method: Usually, static methods are used to create utility functions which make more sense to be part of the class. @staticmethod decorator marks the method below it as a static method.

class Cart:
   @staticmethod
   def greet():
       print("Have a Great Shopping") # Have a Great Shopping
 
Cart.greet()

Instance Methods	Class Methods	Methods
self as parameter	cls as parameter	No cls or self as parameters
No decorator required	Need decorator @classmethod	Need decorator @staticmethod
Can be accessed through object(instance of class)	Can be accessed through class	Can be accessed through class

OOPS

OOPS: Object-Oriented Programming System (OOPS) is a way of approaching, designing, developing software that is easy to change.

Bundling Data: While modeling real-life objects with object oriented programming, we ensure to bundle related information together to clearly separate information of different objects.

Encapsulation: Bundling of related properties and actions together is called Encapsulation. Classes can be used to bundle related attributes and methods.

Inheritance: Inheritance is a mechanism by which a class inherits attributes and methods from another class. Prefer modeling with inheritance when the classes have an IS-A relationship.

class Product:
    def __init__(self, name):
        self.name = name

    def display_product_details(self):
        print("Product: {}".format(self.name)) # Product: TV
   
       
class ElectronicItem(Product):
    pass

e = ElectronicItem("TV")
e.display_product_details()

Super Class & Sub Class:

• Superclass cannot access the methods and attributes of the subclass.
• The subclass automatically inherits all the attributes & methods from its superclass.

class Product:
   def __init__(self, name):
       self.name = name
   def display_product_details(self):
       print("Product: {}".format(self.name)) # Product: TV

class ElectronicItem(Product):
    def set_warranty(self, warranty_in_months):
        self.warranty_in_months = warranty_in_months

e = ElectronicItem("TV")
e.display_product_details()

Calling Super Class Method: We can call methods defined in the superclass from the methods in the subclass.

class Product:
    def __init__(self, name):
       self.name = name
    def display_product_details(self):
       print("Product: {}".format(self.name)) # Product: TV

class ElectronicItem(Product):
    def set_warranty(self, warranty_in_months):
        self.warranty_in_months = warranty_in_months
   
    def display_electronic_product_details(self):
        self.display_product_details()

e = ElectronicItem("TV")
e.display_electronic_product_details()

Composition: Modeling instances of one class as attributes of another class is called Composition. Prefer modeling with inheritance when the classes have an HAS-A relationship.

class Product:
    def __init__(self, name):
       self.name = name
       self.deal_price = deal_price

    def display_product_details(self):
        print("Product: {}".format(self.name)) # Product: Milk
           
    def get_deal_price(self):
        return self.deal_price

class GroceryItem(Product):
    pass
   
class Order:
    def __init__(self):
         self.items_in_cart = []
     
    def add_item(self, product, quantity):
        self.items_in_cart.append((product, quantity))

    def display_order_details(self):
        for product, quantity in self.items_in_cart:
            product.display_product_details()
            
milk = GroceryItem("Milk")
order.add_item(milk, 2)
order.display_order_details()

Overriding Methods: Sometimes, we require a method in the instances of a sub class to behave differently from the method in instance of a superclass.

class Product:
    def __init__(self, name):
        self.name = name

    def display_product_details(self): 
        print("Superclass Method")

class ElectronicItem(Product):
    def display_product_details(self): # same method name as superclass
        print("Subclass Method")
 
e = ElectronicItem("Laptop")
e.display_product_details()

# Output is:
Subclass Method

Accessing Super Class’s Method: super() allows us to call methods of the superclass (Product) from the subclass. Instead of writing and methods to access and modify warranty we can override __init__.

class Product:
  def __init__(self, name):
      self.name = name
   
  def display_product_details(self):
      print("Product: {}".format(self.name)) # Product: Laptop

class ElectronicItem(Product):
   
  def display_product_details(self):
      super().display_product_details()    
      print("Warranty {} months".format(self.warranty_in_months)) # Warranty 10 months
    
  def set_warranty(self, warranty_in_months):
      self.warranty_in_months = warranty_in_months

e = ElectronicItem("Laptop")
e.set_warranty(10)
e.display_product_details()

MultiLevel Inheritance: We can also inherit from a subclass. This is called MultiLevel Inheritance.

class Product:
    pass

class ElectronicItem(Product):
    pass

class Laptop(ElectronicItem):
    pass

Inheritance & Composition:

Inheritance	Composition
Car is a vehicle	Car has a Tyre
Truck is a vehicle	Order has a product

Errors & Exceptions

Errors & Exceptions: There are two major kinds of errors:

• Syntax Errors
• Exceptions

Syntax Errors: Syntax errors are parsing errors which occur when the code is not adhering to Python Syntax.

if True print("Hello") # SyntaxError: invalid syntax

• When there is a syntax error, the program will not execute even if that part of code is not used.

Exceptions: Errors detected during execution are called exceptions.

Division Example: Input given by the user is not within expected values.

def divide(a, b):  
    return  a / b  

divide(5, 0)

# Output is:
ZeroDivisionError: division by zero

Working With Exceptions:

Raising Exceptions:

raise ValueError("Unexpected Value!!") # ValueError:Unexpected Value

def divide(x, y):
    if y == 0:
        raise ValueError("Cannot divide by zero")
    return x / y

print(divide(10, 0))  # ValueError: Cannot divide by zero

Handling Exceptions: Exceptions can be handled with try-except block. Whenever an exception occurs at some line in try block, the execution stops at that line and jumps to except block.

try:
 # Write code that
 # might cause exceptions.
except:
 # The code to be run when
 # there is an exception.

def divide(x, y):
    try:
        result = x / y
    except TypeError:
        return "Invalid input"
    return result
print(divide(10, 5)) # 2.0
print(divide(10, "a"))  # Invalid input

Handling Specific Exceptions: We can specifically mention the name of exception to catch all exceptions of that specific type.

try:
    # Write code that
    # might cause exceptions.
except Exception:
    # The code to be run when
    # there is an exception.

try:  
    result = 5/0
    print(result)  
except ZeroDivisionError:  
    print("Denominator can't be 0")  
except:  
    print("Unhandled Exception")

# Output is:
Denominator can't be 0

Handling Multiple Exceptions: We can write multiple exception blocks to handle different types of exceptions differently.

try:
    # Write code that
    # might cause exceptions.
except Exception1:
    # The code to be run when
    # there is an exception.
except Exception2:
    # The code to be run when
    # there is an exception.

try:  
    result = 12/"a"
    print(result)  
except ZeroDivisionError:  
    print("Denominator can't be 0")  
except ValueError:  
    print("Input should be an integer")  
except:  
    print("Something went wrong")

# Output is:
Denominator can't be 0

Working With Dates & Times

Datetime: Python has a built-in datetime module which provides convenient objects to work with dates and times.

import datetime

Datetime classes: Commonly used classes in the datetime module are:

1. date class

2. time class

3. datetime class

4. timedelta class

Representing Date: A date object can be used to represent any valid date (year, month and day).

import datetime

date_object = datetime.date(2022, 12, 17)
print(date_object) # 2022-12-17

Attributes of Date Object:

from datetime import date

date_object = date(2019, 4, 13)
print(date_object.year) # 2019
print(date_object.month) # 4
print(date_object.day) # 13

Today’s Date: Class method today() returns a date object with today’s date.

import datetime

date_object = datetime.date.today()
print(date_object) # 2022-12-17

Representing Time: A time object can be used to represent any valid time (hours, minutes and seconds).

from datetime import time

time_object = time(11, 34, 56)
print(time_object) # 11:34:56

Attributes of Time Object:

from datetime import time

time_object = time(11, 34, 56)
print(time_object.hour) # 11
print(time_object.minute) # 34
print(time_object.second) # 56

Datetime: The datetime class represents a valid date and time together.

from datetime import datetime

date_time_obj = datetime(2018, 11, 28, 10, 15, 26)
print(date_time_obj.year) # 2018
print(date_time_obj.month) # 11
print(date_time_obj.hour) # 10
print(date_time_obj.minute) # 15

Timedelta: Timedelta object represents duration.

from datetime import timedelta

delta = timedelta(days=365, hours=4)
print(delta) # 365 days, 4:00:00

Calculating Time Difference:

import datetime

dt1 = datetime.datetime(2021, 2, 5)
dt2 = datetime.datetime(2022, 1, 1)
duration = dt2 - dt1
print(duration) # 330 days, 0:00:00
print(type(duration)) # <class 'datetime.timedelta'>

Formatting Datetime: The datetime classes have strftime(format) method to format the datetime into any required format like

• mm/dd/yyyy
• dd-mm-yyyy

from datetime import datetime

now = datetime.now()
formatted_datetime_1 = now.strftime("%d %b %Y %I:%M:%S %p")
print(formatted_datetime_1) # 05 Feb 2021 09:26:50 AM

formatted_datetime_2 = now.strftime("%d/%m/%Y, %H:%M:%S")
print(formatted_datetime_2) # 05/02/2021, 09:26:50

Parsing Datetime: The class method strptime() creates a datetime object from a given string representing date and time.

from datetime import datetime

date_string = "28 November, 2018"
print(date_string) # 28 November, 2018

date_object = datetime.strptime(date_string, "%d %B, %Y")
print(date_object) # 2018-11-28 00:00:00