# 3.3. Functools#

functools is a built-in Python library to work with functions efficiently. This section will show you some useful methods of functools.

## 3.3.1. functools.partial: Generate a New Function with Fewer Arguments#

If you want to fix some arguments of a function and generate a new function with fewer arguments, use `functools.partial`.

In the code below, I use `partial` to create a new function with only `x` as the argument.

```from functools import partial

def linear_func(x, a, b):
return a * x + b

linear_func_partial = partial(linear_func, a=2, b=3)
print(linear_func_partial(2))
print(linear_func_partial(4))
```
```7
11
```

## 3.3.2. functools.singledispatch: Call Another Function Based on the Type of the Current Function’s Argument#

Normally, to call another function based on the type of the current function’s argument, we use an if-else statement:

```data = {"a": [1, 2, 3], "b": [4, 5, 6]}
data2 = [{"a": [1, 2, 3]}, {"b": [4, 5, 6]}]
```
```def process_data(data):
if isinstance(data, dict):
process_dict(data)

else:
process_list(data)

def process_dict(data: dict):
print("Dict is processed")

def process_list(data: list):
print("List is processed")
```
```process_data(data)
```
```Dict is processed
```
```process_data(data2)
```
```List is processed
```

With `singledispatch`, you don’t need to use an if-else statement to call an appropriate function. `singledispatch` will choose the right function based on the type of current function’s first argument.

```from functools import singledispatch

@singledispatch
def process_data2(data):

@process_data2.register
def process_dict2(data: dict):
print("Dict is processed")

@process_data2.register
def process_list2(data: list):
print("List is processed")
```
```process_data2(data)
```
```Dict is processed
```
```process_data2(data2)
```
```List is processed
```

## 3.3.3. functools.reduce: Apply Function Cumulatively to the Items of Iterable#

If you want to apply a function of two arguments cumulatively to the items of iterable from left to right, use functools’s reduce. This method reduces the iterable to a single value.

In the code below, `3` is the result of the function `add_nums(2, 1)`. `3` is then used as the first argument of the function `add_nums(3, 2)`.

```from functools import reduce

res = num1 + num2
print(f"{num1} + {num2} = {res}")
return res

```
```2 + 1 = 3
3 + 2 = 5
5 + 3 = 8
8
```

## 3.3.4. Combine Reduce and Operator Methods#

You can combine `functools.reduce` with a method from operator to achieve the similar functionality and make the code more readable.

```import functools
import operator

# 2+1=3, 3+2=5, 5+3=8
```8