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Group Anagrams - Arrange Code (Python)

Put the Python code in order

Problem Brief

Given an array of strings strs, group the anagrams together. You can return the answer in any order. An Anagram is a word or phrase formed by rearranging the letters of a different word or phrase, typically using all the original letters exactly once.

Puzzle Hints
  1. Ignore visual position first; place the line that creates required state before lines that read it. One candidate is "groups = {}".

  2. Arrange the python code lines in the correct order to form a working solution.

  3. When two lines both look plausible, choose the one whose output is needed by the next line.

Asked at 36 companies
AdobeAffirmAmazon
Group Anagrams — Sorted Key HashMap
hashmap
1
8

Input: ["eat","tea","tan","ate","nat","bat"]. Key = sorted chars

dict & set in Pythonref

Python dict is a hash map with O(1) average access. set stores unique hashable elements. Both are built-in and highly optimized. Use collections.Counter for frequency counting and collections.defaultdict to avoid KeyError.

-dict — {key: value}, O(1) access via d[key]
-set() — unique elements, O(1) in/add/discard
-dict.get(key, default) avoids KeyError
-Counter(iterable) counts element frequencies
from collections import Counter, defaultdict

d = {}
d['a'] = 1
d.get('b', 0)       # 0 (default)

s = set([1, 2, 2])  # {1, 2}
2 in s               # True — O(1)

Counter("aab")       # Counter({'a': 2, 'b': 1})
Official docs →
dict & set in Pythonref

Python dict is a hash map with O(1) average access. set stores unique hashable elements. Both are built-in and highly optimized. Use collections.Counter for frequency counting and collections.defaultdict to avoid KeyError.

-dict — {key: value}, O(1) access via d[key]
-set() — unique elements, O(1) in/add/discard
-dict.get(key, default) avoids KeyError
-Counter(iterable) counts element frequencies
from collections import Counter, defaultdict

d = {}
d['a'] = 1
d.get('b', 0)       # 0 (default)

s = set([1, 2, 2])  # {1, 2}
2 in s               # True — O(1)

Counter("aab")       # Counter({'a': 2, 'b': 1})
Official docs →
How to think: Hash Map / Setguide

You need O(1) lookups — checking if something exists, counting frequencies, or finding pairs.

1.Ask: "Am I searching for something repeatedly?" → Hash Map
2.Ask: "Do I need to check existence?" → Set
3.Ask: "Do I need to count occurrences?" → Map with value = count
4.Ask: "Do I need to find a pair that satisfies a condition?" → Store complement in Map
5.The tradeoff: O(n) extra space buys you O(1) per lookup

vs Nested loops (O(n²)): You're comparing every element against every other — a Map does it in one pass

vs Sorting (O(n log n)): You just need existence/frequency checks, not order

find pairtwo numbers thatfrequencycountduplicateanagramgroup by
How to think: Hash Map / Setguide

You need O(1) lookups — checking if something exists, counting frequencies, or finding pairs.

1.Ask: "Am I searching for something repeatedly?" → Hash Map
2.Ask: "Do I need to check existence?" → Set
3.Ask: "Do I need to count occurrences?" → Map with value = count
4.Ask: "Do I need to find a pair that satisfies a condition?" → Store complement in Map
5.The tradeoff: O(n) extra space buys you O(1) per lookup

vs Nested loops (O(n²)): You're comparing every element against every other — a Map does it in one pass

vs Sorting (O(n log n)): You just need existence/frequency checks, not order

find pairtwo numbers thatfrequencycountduplicateanagramgroup by
  • 1 groups = {}
  • 2def groupAnagrams(strs):
  • 3 groups[key].append(s)
  • 4 if key not in groups:
  • 5 for s in strs:
  • 6 key = ''.join(sorted(s))
  • 7 return list(groups.values())
  • 8 groups[key] = []