LeetCode 194 - Transpose File

Difficulty: 🟡 Medium
Topics: Shell

Solution

Problem Understanding

This problem asks us to transpose the contents of a text file. Transposing means converting rows into columns and columns into rows. Every word in the input file is separated by a single space, and each row contains the same number of columns.

The input is a file named file.txt. Each line in the file represents a row of data. Each word inside a row represents a column value. The goal is to print the transposed version of the file, where the first output line contains all values from the first column, the second output line contains all values from the second column, and so on.

For example, consider the following input:

name age
alice 21
ryan 30

The original structure is:

Row	Values
0	name age
1	alice 21
2	ryan 30

After transposition:

Column	Values
0	name alice ryan
1	age 21 30

So the output becomes:

name alice ryan
age 21 30

The problem guarantees that every row contains the same number of columns. This guarantee is important because it means we never need to handle irregular row lengths or missing values. Without this guarantee, accessing a column index in every row could produce errors.

The input size is typically small in shell scripting problems, but efficiency still matters because repeatedly scanning the file or rebuilding strings inefficiently can increase runtime.

Some important edge cases include files with only one row, files with only one column, and files containing many rows and columns. A naive implementation may also mishandle spaces or produce trailing whitespace at the end of lines.

Approaches

Brute Force Approach

A brute force solution would repeatedly scan the file for every column. First, we determine how many columns exist by reading the first line. Then, for each column index, we iterate through every row and extract the corresponding field using tools such as awk or cut.

This approach works because every output row corresponds exactly to one input column. By repeatedly visiting every row for each column, we can reconstruct the transposed structure.

However, this solution performs unnecessary repeated passes over the input data. If there are m rows and n columns, the file may effectively be scanned n times. While still acceptable for small files, it is less efficient than processing everything in a single traversal.

Optimal Approach

The key insight is that transposition naturally maps to storing values grouped by column index while reading the file once.

As we process each line, we split it into fields. For every field position i, we append the value to a string representing the i-th output row.

This allows us to build the transposed result incrementally in a single pass through the file. After processing all lines, each accumulated string already contains one row of the transposed output.

This approach is efficient because every value is processed exactly once.

Approach	Time Complexity	Space Complexity	Notes
Brute Force	O(m × n)	O(1)	Repeatedly scans rows for each column
Optimal	O(m × n)	O(n)	Processes file once while grouping by column

Here, m represents the number of rows and n represents the number of columns.

Algorithm Walkthrough

Read the file line by line so that memory usage stays manageable and each row is processed exactly once.
Split the current line into fields using whitespace as the delimiter. In shell scripting, tools like awk automatically handle field splitting.
For each field position i, append the field value to an accumulator corresponding to column i.
If the accumulator already contains text, add a space before appending the new value. This ensures proper formatting without leading or trailing spaces.
Continue processing until all lines have been read.
After processing the entire file, print each accumulator line in order. Each accumulator now represents one row of the transposed output.

The main data structure here is an associative array or indexed array that stores strings grouped by column index. This structure is ideal because each column is built independently as rows are processed.

Python Solution

Even though this is a Shell problem on LeetCode, the following Python implementation demonstrates the same transpose logic clearly.

from typing import List

class Solution:
    def transposeFile(self, lines: List[str]) -> List[str]:
        if not lines:
            return []

        rows = [line.split() for line in lines]
        row_count = len(rows)
        column_count = len(rows[0])

        transposed = []

        for column_index in range(column_count):
            current_row = []

            for row_index in range(row_count):
                current_row.append(rows[row_index][column_index])

            transposed.append(" ".join(current_row))

        return transposed

The implementation first splits every input line into individual fields. This creates a two dimensional structure where each inner list represents one row of the file.

Next, the algorithm iterates column by column. For each column index, it gathers the value from every row at that position. These values are joined together with spaces to form one line of the transposed output.

Finally, the completed transposed rows are returned as a list of strings.

The nested loops directly mirror the mathematical definition of matrix transposition. The outer loop iterates over columns, while the inner loop iterates over rows.

Go Solution

package main

import (
	"strings"
)

func transposeFile(lines []string) []string {
	if len(lines) == 0 {
		return []string{}
	}

	rows := make([][]string, len(lines))

	for i, line := range lines {
		rows[i] = strings.Fields(line)
	}

	rowCount := len(rows)
	columnCount := len(rows[0])

	result := make([]string, 0, columnCount)

	for col := 0; col < columnCount; col++ {
		currentRow := make([]string, 0, rowCount)

		for row := 0; row < rowCount; row++ {
			currentRow = append(currentRow, rows[row][col])
		}

		result = append(result, strings.Join(currentRow, " "))
	}

	return result
}

The Go implementation follows the same overall structure as the Python version. The primary difference is that Go requires explicit slice allocation and uses strings.Fields for splitting lines into fields.

Go slices are dynamically resized arrays, making them suitable for collecting column values incrementally. The strings.Join function efficiently constructs the final output rows.

Unlike Python, Go distinguishes between nil slices and empty slices. Here, an empty slice is returned for empty input to keep the behavior explicit and predictable.

Worked Examples

Example 1

Input:

name age
alice 21
ryan 30

Step 1: Split Input into Rows

Row Index	Parsed Row
0	["name", "age"]
1	["alice", "21"]
2	["ryan", "30"]

Step 2: Process Column 0

We collect:

Row	Value at Column 0
0	name
1	alice
2	ryan

Joined result:

name alice ryan

Step 3: Process Column 1

We collect:

Row	Value at Column 1
0	age
1	21
2	30

Joined result:

age 21 30

Final Output

name alice ryan
age 21 30

Complexity Analysis

Measure	Complexity	Explanation
Time	O(m × n)	Every field is processed exactly once
Space	O(m × n)	Stores the parsed rows and output

The algorithm visits every value in the input exactly once while building the transposed output. Since there are m × n total fields, the runtime is proportional to the total amount of data.

The space complexity is also proportional to the number of fields because the parsed rows and transposed output must be stored in memory.

Edge Cases

Single Row Input

If the input contains only one row:

a b c

The transpose becomes:

a
b
c

A buggy implementation might incorrectly assume multiple rows exist. This solution handles the case naturally because the outer loop iterates over columns regardless of row count.

Single Column Input

If the input is:

a
b
c

The transpose becomes:

a b c

Some implementations incorrectly preserve line breaks instead of combining values into a single row. This algorithm correctly groups all first column values together.

Large Number of Rows

Consider a file with thousands of rows but only a few columns. A repeated scanning approach becomes inefficient because it rereads the file multiple times.

The optimal solution avoids this problem by processing the file in a single pass and incrementally constructing the output.

Extra Spaces Between Fields

If the file contains irregular spacing:

a    b
c    d

Using whitespace aware splitting functions such as split() in Python or strings.Fields() in Go correctly collapses multiple spaces and extracts clean field values. This prevents malformed output formatting.