Equal Row and Column Pairs
A medium-tier problem at 71% community acceptance, tagged with Array, Hash Table, Matrix. Reported in interviews at DE Shaw and 0 others.
Equal Row and Column Pairs shows up in DE Shaw assessments and sits at 70% acceptance, which sounds safe until you're live and realize you misread the problem. You're not just comparing rows to rows. You need to match every row against every column, and a naive nested-loop solution will time out on a 500x500 matrix. This is where hashing the row and column vectors becomes your only move. If you blank on that pattern during your OA, StealthCoder surfaces a working solution in seconds, invisible to the proctor.
Companies that ask "Equal Row and Column Pairs"
Equal Row and Column Pairs is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Built by an engineer who got tired of watching his cohort grind for six months and still get filtered at the OA stage.
Get StealthCoderThe trick is preprocessing: hash each row and each column into a consistent format, then count collisions. Most candidates try to manually compare row[i] to col[j] in nested loops, which is O(n^3) and gets killed on larger inputs. The fast path is to build a hash map of row tuples (or string representations) and another for columns, then iterate once and add up the matches. The gotcha is making sure your hash function treats [1,2,3] as a row and [1,2,3] as a column the same way. You're also simulating the comparison, not using any fancy matrix decomposition. StealthCoder handles the implementation detail of which data structure to use and how to avoid hash collisions on edge cases.
Pattern tags
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Equal Row and Column Pairs recycles across companies for a reason. It's medium-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Built by an engineer who got tired of watching his cohort grind for six months and still get filtered at the OA stage. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Equal Row and Column Pairs interview FAQ
Is this actually asked at big companies, or just DE Shaw?+
De Shaw has reported it. The topics (Array, Hash Table, Matrix, Simulation) are standard, which means similar comparison-based matrix problems show up across FAANG assessments. This specific formulation may be less common, but the pattern is worth knowing.
Why doesn't the obvious nested-loop solution work?+
For an n by n matrix, you'd compare each of n rows to each of n columns, examining each element. That's O(n^3). On a 500x500 matrix, you're doing 125 million element comparisons. Hash-based approach is O(n^2) with preprocessing, and you pass every time.
What's the difference between hashing a row and hashing a column?+
There isn't one if you normalize both. Convert each to a tuple or string the same way, then you're just looking for duplicates across two lists. The trick is consistency: if you hash row[i] as str(list(matrix[i])), do the same for columns.
How do you extract a column from a matrix efficiently?+
Use a list comprehension or zip. For column j, grab matrix[i][j] for all i. In Python, you could do [matrix[i][j] for i in range(len(matrix))]. Convert to tuple and hash it the same way you hash rows.
What if two rows are identical to each other but don't match any column?+
They don't contribute to your count. You only increment the answer when a row's hash matches a column's hash. If rows collide with each other, they still only count matching columns. The hash map lets you count all matches in one pass.
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