L2 · Working engineerDictionaries & hashing~4 min · 4 tests#31

Merge Two Dictionaries in Python

Merge two Python dictionaries into a new one without mutating either, with the second dict winning conflicts. Covers |, ** unpacking and update().

The problem

Return a new dictionary containing every key from d1 and d2. If a key is in both, the value from d2 wins. Neither input may be modified.

Examples

Example 1

Input

merge_dicts({'a': 1, 'b': 2}, {'c': 3, 'd': 4})

Expected output

{'a': 1, 'b': 2, 'c': 3, 'd': 4}

Example 2
Input
```
merge_dicts({'a': 1}, {'a': 9})
```
Expected output
```
{'a': 9}
```

+ 2 hidden tests on Submit — inputs not mutated.

Edge cases to ask about

Overlapping keys
Empty dicts
Inputs must not change

Hints

0/3

How an interviewer scores this

0/9

1. Understand the problemRestate it, ask about edge cases and input size.
2. Write a brute-force solutionCorrect first, clever later.
3. Get the correct outputautoAll visible examples pass.
4. Analyse time complexityautoCommit to a Big-O in the Complexity Lab.
5. Analyse space complexityautoExtra memory, not the input.
6. Identify the bottleneckautoMeasure your code — where does it bend?
7. OptimiseUsually: a set or dict instead of a scan.
8. Explain why it worksSay it out loud to Solvi.
9. Test the edge casesautoSubmit passes every hidden test.

Python 3.13 · merge_dicts

def merge_dicts(d1, d2):
    """New dict with keys of both; d2 wins conflicts; inputs unchanged."""
    # Write your solution here
    pass

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Complexity Lab

What does this cost as n grows?

Interviewers score the analysis as much as the code. Commit to an answer first — then check it, and read why.

Time complexity of the optimal solution

Space complexity (extra memory)

Pick both to reveal the answer.

From brute force to optimal

The progression an interviewer wants to hear, one step at a time.

Approach	Time	Space	Idea
Copy + update()	O(n + m)	O(n + m)	Works on every Python 3 version.
{d1, d2}	O(n + m)	O(n + m)	Unpacking, Python 3.5+.
bestd1 \| d2	O(n + m)	O(n + m)	The merge operator, Python 3.9+.

Walkthrough of the optimal approach (try it yourself first)

Copy d1 (so it is not mutated) and update with d2; later keys overwrite earlier ones. {**d1, **d2} and d1 | d2 do the same thing — knowing which Python version introduced each is a common follow-up.

Complexity: O(n + m) time, O(n + m) space. Every key from both dicts is copied once into the new dict.

Reveal the reference solution

def merge_dicts(d1, d2):
    merged = dict(d1)
    merged.update(d2)
    return merged

Follow-ups interviewers ask

Deep-merge nested dicts.
Sum values on conflicting keys.

Frequently asked interview questions

Core interview concepts, complexities, and follow-ups scored by hiring teams.

What is the time complexity of Merge Two Dictionaries in Python?

The optimal solution runs in O(n + m) time and O(n + m) auxiliary space. Every key from both dicts is copied once into the new dict.

What is the brute-force approach, and how do you optimise it?

Copy + update(): O(n + m) time, O(n + m) space. Works on every Python 3 version. {**d1, **d2}: O(n + m) time, O(n + m) space. Unpacking, Python 3.5+. d1 | d2: O(n + m) time, O(n + m) space. The merge operator, Python 3.9+.

What follow-up questions do interviewers ask about Merge Two Dictionaries in Python?

Deep-merge nested dicts. Sum values on conflicting keys.