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I am trying to write a function has input:

```given_num=6, n=3
```

How to return a result of all possible combinations as list with n elements and sum = given_num nested in a list:

```[[0,0,6],[0,1,5],[0,2,4],[0,3,3],[0,4,2],[0,5,1],[0,6,0],
[1,0,5],[1,1,4],[1,2,3],[1,3,2],[1, 4,1],[1,5,0],
...
[6,0,0]]
```

solved, and the possible combination is calculated as

`factorial(given_num+n-1)/(factorial(given_num) * factorial(n-1))`

Here a try using `itertools product` :

```from itertools import product

given_num = 6
n = 3
all_comb = product(range(given_num+1), repeat=n)
final_lst = [i for i in all_comb if sum(i) == given_num]
print(final_lst)
```

Find all combinations of a set of lists with itertools.product, The same effect can be achieved in Python by combining map() and count() to So, if the input iterable is sorted, the combination tuples will be produced in  Elite Programming Professionals Ready to Teach Online. Anytime, Anywhere.

```from itertools import product
import numpy as np

tuples = []
given_num = 6
n = 3

for numbers in product(np.arange(given_num+1), repeat=n):

if sum(numbers) == given_num:
tuples.append(numbers)

print(tuples)
```

9.7. itertools, This is a function which generates random pairs using itertools.combinations [1] answers we randomly shuffle these pairs after getting the exhaustive list o. Fast, Free 2-Day Shipping on Orders Over \$40. Expert Gear Advice From Riders.

In case you want to have some fun with recursive functions

```def summands(num, N):
if N==1:
return [[num]]
else:
return [[i] + j for i in range(num+1) for j in summands(num - i, N-1)]
```

How to create random, nonrepetitive pairs from a list in Python, This exhaustive feature selection algorithm is a wrapper approach for will evaluate all 15 feature combinations (if min_features=1 and max_features=4 ). Exhaustive combinations of lists in python. Ask Question Asked 6 years, Is this possible using the itertools library or sets or a combination of the above?

Exhaustive Feature Selector, The method we are going to use is called exhaustive grid search; it is a way to try all possible combinations of the different values of the hyperparameters that we  Python has a cool technique to make this easier, using lru_cache from the functools package. In a nutshell, all you need is a function with hashable arguments, and then you can simply annotate it with @lru_cache to benefit from easy memoization.

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