Tabla: Seat

+-------------+---------+
| Column Name | Type    |
+-------------+---------+
| id          | int     |
| student     | varchar |
+-------------+---------+

- id es la llave primaria (valor único).
- Cada fila indica el nombre y el ID de un alumno.
- id es una columna auto incrementable.

El formato del resultado se muestra en el siguiente ejemplo.

Ejemplo 1:

Entrada:

Tabla Seat:
+----+---------+
| id | student |
+----+---------+
| 1  | Abbot   |
| 2  | Doris   |
| 3  | Emerson |
| 4  | Green   |
| 5  | Jeames  |
+----+---------+

Salida:

+----+---------+
| id | student |
+----+---------+
| 1  | Doris   |
| 2  | Abbot   |
| 3  | Green   |
| 4  | Emerson |
| 5  | Jeames  |
+----+---------+

Explicación:
- Nota que si el número de alumnos es impar, no hay necesitad de intercambiar el último alumno.

Solución:

import pandas as pd


def exchange_seats(seat: pd.DataFrame) -> pd.DataFrame:
    students = seat['student'].to_list()
    if len(students) == 0:
        return pd.DataFrame({'id': [], 'student': []})

    for i in range(0, len(students) - 1, 2):
        students[i], students[i + 1] = (students[i + 1],
                                        students[i])

    return pd.DataFrame({
        'id': range(1, seat.index.max() + 2),
        'student': students
    })


def test_exchange_seats():
    data = [
        [1, 'Abbot'],
        [2, 'Doris'],
        [3, 'Emerson'],
        [4, 'Green'],
        [5, 'Jeames']
    ]
    seat = pd.DataFrame(
        data, columns=['id', 'student']
    ).astype({'id':'Int64', 'student':'object'})

    data = [
        [1, 'Doris'],
        [2, 'Abbot'],
        [3, 'Green'],
        [4, 'Emerson'],
        [5, 'Jeames']
    ]
    expected = pd.DataFrame(
        data, columns=['id', 'student']
    ).astype({'id':'Int64', 'student':'object'})

    got = exchange_seats(seat)

    pd.testing.assert_frame_equal(
        got, expected, check_dtype=False)