> ## Documentation Index
> Fetch the complete documentation index at: https://leetcode-py.wisl.dev/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> leetcode-py is a Python LeetCode practice environment generator with one CLI: lcpy. It is not a service or platform.
> Each problem is a directory under leetcode/ with README.md, solution.py, test_solution.py, helpers.py, and playground.ipynb. lcpy gen creates them from JSON templates bundled with the package.
> Examples are backed by tests; copy them verbatim.

# Subsets Python Solution with Tests

> Tested Python solution for LeetCode 78 with 12 pytest cases. Generate a practice environment with lcpy.

LeetCode 78, Medium. Topics: Array, Backtracking, Bit Manipulation. [View on LeetCode](https://leetcode.com/problems/subsets/description/).

Generate this problem as a practice environment: tested reference solution, 12 [parametrized pytest cases](/practice/testing), and a playground notebook:

```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
lcpy gen -n 78   # by problem number
lcpy gen -s subsets   # by problem name
```

## Problem

Given an integer array `nums` of **unique** elements, return *all possible* subsets (the power set).

The solution set **must not** contain duplicate subsets. Return the solution in **any order**.

### Examples

```
Input: nums = [1,2,3]
Output: [[],[1],[2],[1,2],[3],[1,3],[2,3],[1,2,3]]
```

```
Input: nums = [0]
Output: [[],[0]]
```

### Constraints

* 1 \<= nums.length \<= 10
* -10 \<= nums\[i] \<= 10
* All the numbers of nums are unique.

## Solution

Reference implementation from [solution.py on GitHub](https://github.com/wislertt/leetcode-py/blob/main/leetcode/subsets/solution.py), full suite in [test\_solution.py](https://github.com/wislertt/leetcode-py/blob/main/leetcode/subsets/test_solution.py):

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
class Solution:
    # Time: O(2^n)
    # Space: O(2^n)
    def subsets(self, nums: list[int]) -> list[list[int]]:
        result = []

        def backtrack(start: int, path: list[int]) -> None:
            result.append(path[:])
            for i in range(start, len(nums)):
                path.append(nums[i])
                backtrack(i + 1, path)
                path.pop()

        backtrack(0, [])
        return result
```

## Complexity

| Time   | Space  |
| ------ | ------ |
| O(2^n) | O(2^n) |

## Tags

[Grind 75](/catalog/grind-75), [Grind](/catalog/grind), [NeetCode 150](/catalog/neetcode-150), [NeetCode 250](/catalog/neetcode-250), [NeetCode All](/catalog/neetcode), [AlgoMaster 75](/catalog/algo-master-75).