DataFrame df1
+-------------+--------+
| Column Name | Type   |
+-------------+--------+
| student_id  | int    |
| name        | object |
| age         | int    |
+-------------+--------+

DataFrame df2
+-------------+--------+
| Column Name | Type   |
+-------------+--------+
| student_id  | int    |
| name        | object |
| age         | int    |
+-------------+--------+

El formato del resultado se muestra en el siguiente ejemplo.

Ejemplo 1:

Entrada:

df1
+------------+---------+-----+
| student_id | name    | age |
+------------+---------+-----+
| 1          | Mason   | 8   |
| 2          | Ava     | 6   |
| 3          | Taylor  | 15  |
| 4          | Georgia | 17  |
+------------+---------+-----+

df2
+------------+------+-----+
| student_id | name | age |
+------------+------+-----+
| 5          | Leo  | 7   |
| 6          | Alex | 7   |
+------------+------+-----+

Salida:

+------------+---------+-----+
| student_id | name    | age |
+------------+---------+-----+
| 1          | Mason   | 8   |
| 2          | Ava     | 6   |
| 3          | Taylor  | 15  |
| 4          | Georgia | 17  |
| 5          | Leo     | 7   |
| 6          | Alex    | 7   |
+------------+---------+-----+

Explicación:
Los dos DataFramess son apilados verticalmente, y sus filas se fusionan.

Solución

import pandas as pd


def concatenateTables(df1: pd.DataFrame, df2: pd.DataFrame) -> pd.DataFrame:
    return pd.concat([df1, df2])