In Python, a generator is a special type of iterable, which can be used to create iterators in an efficient and concise manner. Unlike regular functions that return a single value and terminate, generators use the `yield`

statement to return a value and temporarily suspend the function’s execution state.

The generator can be resumed from the point it was paused, and this allows it to produce a sequence of values one at a time, on-the-fly, without holding the entire sequence in memory.

```
def generator_name(arg):
# statements
yield something
```

To create a generator, you define a function with the `yield`

statement instead of using `return`

.

## Python Generator example

Here’s an example of a simple generator that yields numbers from 1 to a specified limit:

def number_generator(limit): num = 1 while num <= limit: yield num num += 1 # Using the generator to iterate through the sequence of numbers gen = number_generator(5) for num in gen: print(num)

**Output:**

You can also use generator expressions, which have a syntax similar to list comprehensions but produce a generator instead of a list:

Here’s an example of a generator expression that generates squares of numbers from 1 to 5:

```
# Generator expression to generate squares of numbers from 1 to 5
squares_gen = (x ** 2 for x in range(1, 6))
# Using the generator to iterate through the sequence of squares
for square in squares_gen:
print(square)
```

Here’s a simple example of a Python generator function that generates Fibonacci numbers:

```
def fibonacci_generator():
a, b = 0, 1
while True:
yield a
a, b = b, a + b
# Using the generator to generate Fibonacci numbers up to a limit
limit = 50
fib_gen = fibonacci_generator()
fibonacci_sequence = []
for num in fib_gen:
if num <= limit:
fibonacci_sequence.append(num)
else:
break
print(fibonacci_sequence)
```

**Generator Function Syntax: **

A generator function is defined using the `def`

keyword and contains one or more `yield`

statements. When the function is called, it returns a generator object, which can be iterated over using a `for`

loop or by calling the `next()`

function. Here’s the syntax for a generator function:

```
def generator_function(arguments):
# Initialization, if needed
while condition:
# Some calculations or processing
yield value_to_return
# Execution will pause here and will resume from this point in the next iteration
```

**Example of Generator Function:**

```
def number_generator(limit):
num = 1
while num <= limit:
yield num
num += 1
```

**Generator Expression Syntax:**

A generator expression is defined using parentheses `()`

and has a similar syntax to list comprehensions. However, instead of using square brackets `[]`

, it uses parentheses `()`

. A generator expression is used to create an anonymous generator without explicitly defining a generator function. Here’s the syntax for a generator expression:

```
generator_expression = (expression for item in iterable if condition)
```

`expression`

: The expression that computes the value to be yielded by the generator.`item`

: The variable representing each item in the`iterable`

.`iterable`

: The source of data from which the generator will produce values.`condition`

(optional): An optional condition to filter items in the`iterable`

. It can be omitted if not needed.

**Example of Generator Expression:**

```
squares_gen = (x ** 2 for x in range(1, 6))
```

Both generator functions and generator expressions are useful for efficiently handling large datasets or infinite sequences, as they generate elements on-the-fly, one at a time, without the need to store the entire sequence in memory.

Note:IDE: PyCharm 2021.3.3 (Community Edition)Windows 10

Python 3.10.1All

Python Examples are in Python 3, so Maybe its different from python 2 or upgraded versions.