Python - Assigning Subsequent Rows to Matrix first row elements

Python - Assigning Subsequent Rows to Matrix first row elements

In this tutorial, we'll learn how to assign the subsequent rows of a matrix to the elements of its first row.

Problem:

Given a matrix (2D list), assign each subsequent row of the matrix as a value to the corresponding element in the first row, resulting in a dictionary where keys are the elements of the first row and values are the rest of the rows as lists.

For instance, consider the matrix:

matrix = [    ['apple', 'banana', 'orange'],
    [10, 20, 30],
    ['red', 'yellow', 'orange']
]

We want to transform it to:

{
    'apple': [10, 'red'],
    'banana': [20, 'yellow'],
    'orange': [30, 'orange']
}

Solution:

We can achieve this using a combination of dictionary comprehension and the zip function:

  1. Use the zip function to transpose the matrix, which essentially groups columns together.
  2. The first element of each column is the key (from the first row of the matrix), while the rest of the elements form the value list for that key.

Here's the implementation:

def assign_rows_to_first_row_elements(matrix):
    # Transpose the matrix to group columns together
    transposed = list(zip(*matrix))

    # Create a dictionary with the first row elements as keys
    # and the subsequent rows as their corresponding values
    return {col[0]: list(col[1:]) for col in transposed}

matrix = [
    ['apple', 'banana', 'orange'],
    [10, 20, 30],
    ['red', 'yellow', 'orange']
]
result = assign_rows_to_first_row_elements(matrix)
print(result)  # Output: {'apple': [10, 'red'], 'banana': [20, 'yellow'], 'orange': [30, 'orange']}

Notes:

  • The zip(*matrix) function transposes the matrix. It effectively converts rows into columns.
  • The dictionary comprehension then assigns the first element of each transposed row (which is essentially a column from the original matrix) as the key and the rest of the elements as the value.

This tutorial provided a method to transform a matrix into a dictionary by assigning the subsequent rows as values to the elements of the first row. This kind of transformation can be useful in various data processing scenarios where a matrix representation needs to be converted into a key-value mapping.

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