Meaning
Transposes a two‑dimensional list (matrix) by converting its rows into columns using a nested list comprehension. It addresses the need to reorganize tabular data when algorithms expect column‑wise input. Use this when you have a rectangular matrix represented as a list of lists and require its transpose.
Primary Function
Data transformation
Communicative Purpose
Reorganize tabular data so that columns become rows and vice‑versa.
Pattern
[[row[col_idx] for row in matrix] for col_idx in range(len(matrix[0]))]
Core Structure
[[... for ... in ...] for ... in range(len(...[0]))]
Função primária
Data transformation
Propósito comunicativo
Reorganize tabular data so that columns become rows and vice‑versa.
Situações de gatilho
Data processing: preparing data for algorithms that expect column‑wise input; Algorithmic challenges: performing matrix operations that require transposed representation; Linear algebra utilities: converting a list of rows into a list of columns.
Contextos
Data processing scripts, algorithmic challenges, linear algebra utilities, any code handling 2‑D tables represented as lists of lists.
Padrão
[[row[col_idx] for row in matrix] for col_idx in range(len(matrix[0]))]
Estrutura central
[[... for ... in ...] for ... in range(len(...[0]))]
Slots de substituição
matrix: list of lists (rectangular), row: element of matrix, col_idx: integer index for column
Colocados típicos
- zip(*matrix)
- numpy.transpose
- map(list
- zip(*matrix))
Substituições comuns
- list(map(list
- zip(*matrix))) or using NumPy: np.transpose(matrix)
Erros comuns
Assuming all rows have equal length (causing IndexError), confusing inner and outer loop variables, using len(matrix) instead of len(matrix[0]) for the range.
Similar / contraste
zip(*matrix) produces an iterator of tuples; numpy.transpose returns an ndarray; both avoid explicit comprehension.
Interferências
Coming from C or Java: expecting an in‑place transpose → in Python you must build a new list unless using libraries that mutate.
Família do chunk
- matrix transposition
- list comprehension
- zip
Nuance
Only works for rectangular matrices; jagged rows raise IndexError or produce truncated columns. For large matrices, list comprehensions may be slower than NumPy.
Efeito pragmático
Provides a concise, readable way to transpose data without external dependencies.
Dica de memória
Imagine turning a spreadsheet on its side so that the column headers become the row labels. It’s like rotating a picture 90 degrees clockwise.
Nota
Works only for rectangular matrices; for ragged lists consider using itertools.zip_longest to avoid IndexError.
Upgrade path
Replace with numpy.transpose for performance on large numeric data.
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