Frequency maps & counting

A hash map gives O(1) average insert and lookup, which lets you trade memory for time — the single most common optimization in coding interviews. A frequency map counts how many times each value appears, unlocking anagrams, majority elements, and top-K.

def char_counts(s):
    counts = {}
    for c in s:
        counts[c] = counts.get(c, 0) + 1
    return counts

# two strings are anagrams iff their frequency maps are equal
def is_anagram(a, b):
    return char_counts(a) == char_counts(b)

• Anagram grouping: use a canonical key (sorted string, or a 26-length count tuple) so anagrams collide into the same bucket.
• Majority / mode: count occurrences, then take the max — or, when a strict majority (> n/2) element is guaranteed, Boyer-Moore voting for O(1) space (it finds the majority, not the mode).
• Top-K frequent: build the frequency map, then bucket or heap by count.