**What is “generator” in Python?**

**What is a generator?**

**In Python, a generator is a function that returns an iterator that produces a sequence of values when iterated over.**

Generators are useful when we want to produce a large sequence of values, but we don’t want to store all of them in memory at once.

**Basic Syntax**

To define a generator in Python, we use the `def`

keyword, similar to defining a normal function.

**However, instead of the return statement, we use the yield statement.**

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

In this syntax, the `yield`

keyword is used to produce a value from the generator.

When the generator function is called, it does not execute the function body immediately.

Instead, it returns a generator object that can be iterated over to produce the values.

**Simple Example of a Generator**

Here’s an example of a generator function that produces a sequence of numbers,

```
def my_generator(n):
# initialize counter
value = 0
# loop until counter is less than n
while value < n:
# produce the current value of the counter
yield value
# increment the counter
value += 1
# iterate over the generator object produced by my_generator
for value in my_generator(3):
# print each value produced by generator
print(value)
```

In the above example, the `my_generator()`

generator function takes an integer `n`

as an argument and produces a sequence of numbers from 0 to `n-1`

.

The `yield`

keyword is used to produce a value from the generator and pause the generator function’s execution until the next value is requested.

**Benefit of Generators**

There are several reasons that make generators a powerful implementation.

**Easy to Implement**: Generators can be implemented in a clear and concise way as compared to their iterator class counterpart.**Memory Efficient**: A normal function to return a sequence will create the entire sequence in memory before returning the result. This is an overkill if the number of items in the sequence is very large. Generator implementation of such sequences is memory friendly and is preferred since it only produces one item at a time.**Represent Infinite Stream**: Generators are excellent mediums to represent an infinite stream of data. Infinite streams cannot be stored in memory, and since generators produce only one item at a time, they can represent an infinite stream of data.**Pipelining Generators**: Multiple generators can be used to pipeline a series of operations. This is efficient and easy to read.

**Difference from List Comprehension**

List comprehension and generator expressions look very similar in their syntax, but there are some crucial differences.

**Major Differences**

**Memory Usage**: List comprehensions can lead to significant memory usage if the resulting list is large, as they generate the entire list in memory at once. On the other hand, generators generate one item at a time and hence are more memory efficient.**Usage**: List comprehensions are used when the resulting list is needed, while generators are used when the items are needed one at a time, which is useful for large sequences of data.

**Example Showing the Difference**

Here’s an example of a list comprehension and a generator expression that do the same thing: squaring a range of numbers.

```
# List comprehension
squares_list = [i * i for i in range(5)]
for i in squares_list:
print(i)
# Generator expression
squares_generator = (i * i for i in range(5))
for i in squares_generator:
print(i)
```

In both cases, the output will be the squares of the numbers 0 through 4.

**However, the list comprehension will create the entire list in memory at once, while the generator expression will create the items one at a time.**

**Example of Generators**

Let’s look at a more complex example of a generator.

This generator function generates the Fibonacci series, which is a series of numbers in which each number is the sum of the two preceding ones, usually starting with 0 and 1.

```
def fibonacci_numbers(nums):
x, y = 0, 1
for _ in range(nums):
x, y = y, x+y
yield x
# iterate over the generator object produced by fibonacci_numbers
for value in fibonacci_numbers(10):
# print each value produced by generator
print(value)
```

In this example, the `fibonacci_numbers()`

generator function takes an integer `nums`

as an argument and produces a sequence of Fibonacci numbers.

The `yield`

keyword is used to produce a value from the generator and pause the generator function’s execution until the next value is requested.

We can also pipeline generators together. For example, if we want to find out the sum of squares of numbers in the Fibonacci series, we cando it in the following way by pipelining the output of generator functions together.

```
def square(nums):
for num in nums:
yield num**2
print(sum(square(fibonacci_numbers(10))))
```

In this example, the `square()`

generator takes a sequence of numbers and yields their squares. The `sum()`

function then sums up these squares. This pipelining is efficient and easy to read.