# 4. Map, Filter and Reduce¶

These are three functions which facilitate a functional approach to programming. We will discuss them one by one and understand their use cases.

## 4.1. Map¶

`Map`

applies a function to all the items in an input_list. Here is
the blueprint:

**Blueprint**

```
map(function_to_apply, list_of_inputs)
```

Most of the times we want to pass all the list elements to a function one-by-one and then collect the output. For instance:

```
items = [1, 2, 3, 4, 5]
squared = []
for i in items:
squared.append(i**2)
```

`Map`

allows us to implement this in a much simpler and nicer way.
Here you go:

```
items = [1, 2, 3, 4, 5]
squared = list(map(lambda x: x**2, items))
```

Most of the times we use lambdas with `map`

so I did the same. Instead
of a list of inputs we can even have a list of functions!

```
def multiply(x):
return (x*x)
def add(x):
return (x+x)
funcs = [multiply, add]
for i in range(5):
value = list(map(lambda x: x(i), funcs))
print(value)
# Output:
# [0, 0]
# [1, 2]
# [4, 4]
# [9, 6]
# [16, 8]
```

## 4.2. Filter¶

As the name suggests, `filter`

creates a list of elements for which a
function returns true. Here is a short and concise example:

```
number_list = range(-5, 5)
less_than_zero = list(filter(lambda x: x < 0, number_list))
print(less_than_zero)
# Output: [-5, -4, -3, -2, -1]
```

The filter resembles a for loop but it is a builtin function and faster.

**Note:** If map & filter do not appear beautiful to you then you can
read about `list/dict/tuple`

comprehensions.

## 4.3. Reduce¶

`Reduce`

is a really useful function for performing some computation on
a list and returning the result. For example, if you wanted to compute
the product of a list of integers.

So the normal way you might go about doing this task in python is using a basic for loop:

Now let’s try it with reduce:

```
from functools import reduce
product = reduce((lambda x, y: x * y), [1, 2, 3, 4])
# Output: 24
```