Sunday 30 April 2023

What does the “yield” keyword do in python?

 In Python, the yield keyword is used to create a generator function. When a function includes a yield statement, it becomes a generator function, which returns an iterator object that can be iterated over with a loop.


The yield statement suspends the function's execution and sends a value back to the caller, but unlike return, the function state is saved, and the function can be resumed later from where it left off.

Here's an example to demonstrate the use of yield:

def countdown(num):

    while num > 0:

        yield num

        num -= 1


This generator function countdown() will return an iterator that will produce a sequence of numbers from num down to 1. We can use a for loop to iterate over the sequence:

for i in countdown(5):
    print(i)

5
4
3
2
1

Each time the yield statement is reached, the function's state is saved, and the value of num is sent back to the caller. The next time the generator is called, execution continues from where it left off, and num is decremented until it reaches zero.


Saturday 29 April 2023

Generator Vs List in Python

 In Python, there are two main ways to create sequences of values: using a generator or a list. While both have their uses, they have different performance characteristics and memory usage, so it's important to choose the right one for your specific use case.


A generator is a type of iterable, like a list or a tuple, but unlike a list, a generator does not store all the values in memory at once. Instead, it generates the values on-the-fly as they are requested, using a special type of function called a generator function. Generator functions use the yield keyword to return a value, but unlike a regular function that returns a value and then exits, a generator function can be resumed from where it left off, so it can continue generating values until it is done. Because generators don't store all their values in memory, they can be more memory-efficient than lists for large data sets, and can be faster for certain operations.


Here's an example of a simple generator function that generates a sequence of numbers:

def generate_numbers(n):

    for i in range(n):

        yield i

To use this generator, you would typically use it in a loop or with a function like next() to generate values one at a time:


numbers = generate_numbers(5)

print(next(numbers)) # Output: 0

print(next(numbers)) # Output: 1

print(next(numbers)) # Output: 2

print(next(numbers)) # Output: 3

print(next(numbers)) # Output: 4

A list, on the other hand, is a type of sequence that stores all its values in memory at once. Lists can be created using square brackets [] or the list() function. Lists are very versatile and can be modified, sliced, and indexed in various ways. However, because they store all their values in memory at once, they can be memory-intensive for large data sets.


Here's an example of creating a list of numbers:

numbers = [0, 1, 2, 3, 4]

To iterate over a list, you can use a for loop or various other functions and methods:


for number in numbers:

    print(number)

Both generators and lists have their advantages and disadvantages, so choosing the right one depends on the specific use case. If you have a large data set or you only need to generate values one at a time, a generator might be more memory-efficient and faster. If you need to modify the sequence, access its values multiple times, or if the data set is small enough to fit in memory, a list might be more appropriate.

7 Cool Ways To Use F-Strings In Python


 

1. Align strings with f-strings:

You can use f-strings to align strings to the left, right, or center of a field. Here's an example:

name = "Alice"

age = 30

print(f"|{name:<10}|{age:^5}|")  # Output: |Alice     | 30  |

In this example, the < character aligns the name variable to the left of a 10-character field, and the ^ character centers the age variable in a 5-character field.


2. Use f-strings with dictionary variables:

You can use f-strings with dictionary variables to create dynamic strings. Here's an example:

person = {"name": "Alice", "age": 30}

print(f"My name is {person['name']} and I'm {person['age']} years old.")  # Output: My name is Alice and I'm 30 years old.

In this example, the person variable is a dictionary with keys "name" and "age". The f-string uses the values of these keys to create a dynamic string.


3. Use f-strings to format binary and hexadecimal numbers:

You can use f-strings to format binary and hexadecimal numbers. Here's an example:

x = 42

print(f"x = {x:b}")  # Output: x = 101010

print(f"x = {x:x}")  # Output: x = 2a

In this example, the :b format specifier formats the x variable as a binary number, and the :x format specifier formats the x variable as a hexadecimal number.


4. Use f-strings to format dates and times:

You can use f-strings to format dates and times. Here's an example:

import datetime

now = datetime.datetime.now()

print(f"Today is {now:%B %d, %Y}")  # Output: Today is April 29, 2023

In this example, the %B %d, %Y format specifier formats the now variable as a string in the format Month Day, Year.


5. Use f-strings to format currency values:

You can use f-strings to format currency values. Here's an example:

salary = 50000

print(f"My salary is ${salary:,}")  # Output: My salary is $50,000

In this example, the , character formats the salary variable as a string with comma separators.


6. Use f-strings with formatted strings:

You can use f-strings with formatted strings to create complex strings. Here's an example:

name = "Alice"

age = 30

message = f"My name is {name} and I'm {age} years old."

print(f"Message length: {len(message):<10}, Message: '{message:^20}'")

# Output: Message length: 32        , Message: 'My name is Alice and I'm 30 years old.'

In this example, the f-string uses another f-string to create a complex string that includes the length of the message variable and the message itself.


7.Use f-strings to format scientific notation:

You can use f-strings to format numbers in scientific notation. Here's an example:

x = 1234567890.123456789

print(f"x = {x:e}")  # Output: x = 1.234568e+09

Return VS Yield in Python

 



In Python, return and yield are two ways to send a value back from a function or generator to its caller, but they work in different ways.


return is a statement that immediately terminates the execution of a function and returns a value to the caller. When the function is called again, it starts executing from the beginning.


Here's an example:

def square(x):

    return x * x


result = square(5)

print(result)  # Output: 25


In this example, the square() function takes an argument x and returns its square using the return statement.

On the other hand, yield is a keyword that allows a function to return a generator object, which can be used to iterate over a sequence of values. When the function is called, it executes until it reaches a yield statement, which returns a value to the caller and suspends the function's execution. The next time the function is called, it continues executing from where it left off until it reaches another yield statement or the end of the function.

Here's an example:

def squares(n):
    for i in range(n):
        yield i * i

result = squares(5)
for square in result:
    print(square)  # Output: 0 1 4 9 16

In this example, the squares() function generates a sequence of squares using a for loop and the yield statement. The result variable holds a generator object, which is then iterated over using a for loop to print the squares.

In summary, return is used to immediately terminate a function and return a value to the caller, while yield is used to create a generator object that can be iterated over to return a sequence of values one at a time.

Sunday 23 April 2023

Saturday 22 April 2023

Object Oriented Programming in Python

 Object-oriented programming (OOP) is a programming paradigm that is based on the concept of "objects," which can contain data and code to manipulate that data. Python is an object-oriented programming language that supports OOP concepts such as inheritance, encapsulation, and polymorphism. Here are some key concepts and syntax used in Python for OOP:

Class: A class is a blueprint or template for creating objects. It defines a set of attributes and methods that the objects of that class will have.

Syntax:

class ClassName:

    # class attributes

    attribute1 = value1

    attribute2 = value2


    # class methods

    def method1(self):

        # method code


    def method2(self):

        # method code

Object: An object is an instance of a class. It has its own set of attributes and methods that were defined in the class.
Syntax:
# create an object of class ClassName
object_name = ClassName()

Method: A method is a function that is defined inside a class and can be called on an object of that class.
Syntax:
# call method1 on object_name
object_name.method1()

Attribute: An attribute is a variable that is defined inside a class and can be accessed by objects of that class.
Syntax:
# access attribute1 of object_name
object_name.attribute1

Inheritance: Inheritance is a mechanism in which a new class is created from an existing class. The new class, called the subclass or derived class, inherits the attributes and methods of the existing class, called the superclass or base class.
Syntax:
# create a subclass of superclass
class SubclassName(SuperclassName):
    # subclass attributes
    attribute3 = value3

    # subclass methods
    def method3(self):
        # method code

Polymorphism: Polymorphism is the ability of objects of different classes to be used interchangeably. It allows the same method to be called on different objects, and the behavior of the method will depend on the object it is called on.
Syntax:
# create two objects of different classes
object1 = ClassName1()
object2 = ClassName2()

# call the same method on both objects
object1.method()
object2.method()

These are some of the key concepts and syntax used in Python for OOP. By using OOP concepts, you can write modular and reusable code that is easier to maintain and understand.

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