# Map & Filter¶

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

## 1. Map¶

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

Blueprint

```map(functions_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 = 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)
return (x+x)

for i in range(5):
value = map(lambda x: x(i), funcs)
print(value)

# Output:
# [0, 0]
# [1, 2]
# [4, 4]
# [9, 6]
# [16, 8]
```

## 2. Filter¶

As the name suggests, filter creats a list of elements for which a function returns true. Here is a short and consise 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.