Generators in Python are a special type of function that returns an iterator. Instead of returning a single value at a time, generators return an iterator object that can be used to iterate over a sequence of values. This is done using the yield keyword.

Key characteristics of generators:

Lazy evaluation: Generators don't evaluate all values at once. Instead, they produce values on-demand as needed, which can be beneficial for memory-intensive operations.
Iterability: Generators can be used in loops like for loops to iterate over the values they produce.
Pausable execution: The yield keyword pauses the execution of the generator function and returns the value. When the generator is resumed, it continues from where it left off. Python Course in Mumbai
Python
def my_generator():
for i in range(5):
yield i * i

for num in my_generator():
print(num)

Benefits of using generators:

Memory efficiency: Generators can be more memory efficient than creating a list of all values upfront, especially when dealing with large datasets.
Infinite sequences: Generators can be used to create infinite sequences, which are useful for tasks like generating numbers or producing values on-demand.
Simplified code: Generators can make code more readable and concise by encapsulating the logic for producing a sequence of values.
Common use cases for generators:

Data processing: Generators can be used to process large datasets efficiently, as they produce values on-demand and avoid loading the entire dataset into memory at once.Python Training in Mumbai
Infinite sequences: Generators can be used to create infinite sequences like Fibonacci numbers or prime numbers.
Custom iterators: Generators can be used to create custom iterators for specific use cases.
In summary, generators are a powerful tool in Python that provide a flexible and efficient way to produce sequences of values. By understanding their characteristics and use cases, you can leverage them to write more concise and memory-efficient code.