Watch as video

03:54

Let's make a **generator expression**.

Here we have a list and a list comprehension that loops over that list:

```
>>> numbers = [2, 1, 3, 4, 7, 11, 18]
>>> squares = [n**2 for n in numbers]
```

If we turn the *square brackets* (`[`

and `]`

) in that list comprehension into *parentheses* (`(`

and `)`

):

```
>>> squares = (n**2 for n in numbers)
```

This will turn our list comprehension into a **generator expression**.

List comprehensions give us back new lists.
Generator expressions give us back new **generator objects**:

```
>>> squares
<generator object <genexpr> at 0x7fcb363347b0>
```

A **generator object**, unlike a list, **doesn't have a length**:

```
>>> len(squares)
Traceback (most recent call last):
File "<console>", line 1, in <module>
TypeError: object of type 'generator' has no len()
```

If we try to index a generator object, to get its first item for example, we'll get an error:

```
>>> squares[0]
Traceback (most recent call last):
File "<console>", line 1, in <module>
TypeError: 'generator' object is not subscriptable
```

**You cannot index a generator**.

The only thing we can really do with a generator is loop over it:

```
>>> for n in squares:
... print(n)
...
4
1
9
16
49
121
324
```

It seems like generators have fewer features than lists. So why would we even want to use a generator expression?

The benefit of **generators** is that they are **lazy iterables**, meaning **they don't do work until you start looping over them**.

Right after we evaluate a **generator expression** a generator object will be made:

```
>>> squares = (n**2 for n in numbers)
>>> squares
<generator object <genexpr> at 0x7fd49a500900>
```

But up to this point this generator **hasn't actually computed anything**.
It doesn't contain any values, unlike a list.

So if we change the number `4`

in our list (at index `3`

) to the number `5`

:

```
>>> numbers
[2, 1, 3, 4, 7, 11, 18]
>>> numbers[3] = 5
>>> numbers
[2, 1, 3, 5, 7, 11, 18]
```

And then we loop over our generator object (using a list constructor, `for`

loop, or any other form of looping) we'll see that the fourth item isn't `16`

, it's `25`

:

```
>>> list(squares)
[4, 1, 9, 25, 49, 121, 324]
```

Generators don't do work **until the point that they're looped over**.

And if you loop over a generator a second time it'll be empty:

```
>>> list(squares)
[]
```

Generator objects are **lazy iterables** and they are **single-use** iterables.
Items are *generated* as we loop over a generator (that's what makes them *lazy*) and these items are *consumed* as we loop over the generator, meaning they aren't stored anywhere (that's what makes them single-use).

When all the items in a generator have been consumed (meaning we've fully looped-over it) we say that it's **exhausted**.
That `squares`

generator above was exhausted:

```
>>> list(squares)
[]
```

You don't necessarily need to fully exhaust generators as you loop over them.
If we were to start looping over a generator and then we stopped once a condition was met (`n > 10`

below):

```
>>> numbers = [2, 1, 3, 4, 7, 11, 18]
>>> squares = (n**2 for n in numbers)
>>> for n in squares:
... print(n)
... if n > 10:
... break
...
4
1
9
16
```

If we then started looping again (using the list constructor in this case) our generator would start up where it left off before:

```
>>> list(squares)
[49, 121, 324]
```

Generators **generate values as you loop** over them.

**Generator expressions** are a comprehension-like syntax for creating new generator objects.

The only thing that one can do with a generator object is **loop over it**.
Once you've looped over a generator object *completely* (i.e. you've exhausted it by consuming all the items within it) it doesn't really have a *use* anymore.
Once a generator is exhausted it's empty forever.

There is one more thing we can do with the generators (besides looping over them) though it's a little bit unusual to see.
All generators can be passed to the built-in `next`

function.

The `next`

function gives us the **next item** in a generator:

```
>>> numbers = [2, 1, 3, 4, 7, 11, 18]
>>> squares = (n**2 for n in numbers)
>>> next(squares)
4
```

Generators keep track of **the expression they need to evaluate** on the iterable they're looping over and they keep track of **where they are in the iterable**.

If we call `next`

on a generator repeatedly we'll get each individual item in the generator:

```
>>> next(squares)
1
>>> next(squares)
9
>>> next(squares)
16
>>> next(squares)
49
>>> next(squares)
121
>>> next(squares)
324
```

If we call `next`

on a generator that's exhausted (it's been fully consumed) we'll get a `StopIteration`

exception:

```
>>> next(squares)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
StopIteration
```

That `StopIteration`

exception indicates that there are no more values in this generator (it's empty):

```
>>> list(squares)
[]
```

Just as list comprehensions make new lists, **generator expressions make new generator objects**.

A **generator** is an *iterable* which doesn't actually contain or store values; it **generates values as you loop over it**.

This means **generators are more memory efficient than lists** because they don't really store memory to hold their values.
Instead they generate values **on the fly** as we loop over them.

Generator expressions give us generators which are **lazy single-use iterables**.

List comprehensions make new lists. Generator expressions make new generator objects. Generators are iterators, which are lazy single-use iterables. Unlike lists, generators aren't data structures. Instead they do work *as you loop over them*.

To track your progress on this Python Morsels topic trail, sign in or sign up.

Need to **fill-in gaps** in your Python skills?
I send regular emails designed to do just that.

Sign up for my Python tips emails and **I'll share my favorite Python insights with you every couple weeks**.

✕

↑

Write more Pythonic code

Need to **fill-in gaps** in your **Python skills**? I send regular emails designed to do just that.