---
tags: [python]
---

# List comprehension

List comprehension is a syntactic shorthand for applying a function to each
element in a list without explicitly using loop syntax.

Since its introduction to the language, the same functionality has become
achievable by using functional methods like
[`map` and `filter`](Map_and_filter_in_Python.md),
utilising [lambdas](Lambdas_in_Python.md)
however list comprehension is often more straightforward and easier to read.

## Syntax

Here is a basic example which applies `+ 1` to each integer in a list:

```python
values = [1, 2, 4, 6, 8, 9]

new_values = [i + 1 for i in values]
print('new_values', new_values)
# new_values [2, 3, 5, 7, 9, 10]
```

The basic syntax is as follows:

```
new_list  = [expression for each member in an iterable]
```

- The _expression_ can be the member itself, a call to a method, or any other
  valid expression that returns a value. In the example above, the expression
  `i + i` adds one to each member value.

- The _member_ is the object or value in the list or iterable. In the example
  above, the member value is _i_.

- The _iterable_ is a list, set, dictionary or any other object that can return
  its elements one at a time. In the example above, the iterable is each value
  in `values`.

This is a much more condensed way of achieving the same outcome with a
traditional loop:

```py
values = [1, 2, 4, 6, 8, 9]
new_list = []
for i in values:
  values.append(i+1)
```

### Another example

In the following example, we apply list comprehension with a `in range` loop
structure:

```py
new_list = [i * i for i in range(10) ]
print(new_list)
# [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

```

### Adding a condition

We can apply a conditional to a comprehension:

```py
new_list = [item + 1 for item in values if item % 2 == 0]

print('new_list:', new_list)
# new_list: [3, 5, 7, 9]
```

### Filters

By applying a condition (and no execution to each element) we effectively create
a filter:

```py
numbers = [1, 2, 3, 4, 4, 4]
numbers_filtered = [i for i in numbers if i > 2]
print(numbers_filtered)
# [3, 4, 5]
```

For comparison, the same outcome could be achieved with a filter and lambda:

```py
with_filter = list(filter(lambda x: x > 2, numbers))
print(with_filter)
# [3, 4, 5]
```

## Set comprehension

We can also apply comprehension to sets. The syntax is practically identical but
the difference is the resultant data structure will not contain duplicates.

```py
numbers = [1, 2, 3, 4, 4, 4]
unique =  {i for i in numbers}
print(unique)
# {1,2,3,4}
```

## Dictionary comprehension

```py
squares = {i: i * i for i in range(5)}
print(squares)
{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25}
```