Concatenating DataFrames

When you have datasets spread across multiple DataFrames with similar structures (same columns or same rows) and you want to combine them into a single DataFrame, this is where concatenating shines. The concatenation process can be along a particular axis, either row-wise (axis=0) or column-wise (axis=1).

Let’s deep dive into the row-wise concatenation, also known as append.

Row-wise concatenation

The row-wise concatenation is used to concatenate one DataFrame to another along axis=0. To showcase this operation, two DataFrames, employee_data_1 and employee_data_2, created with the same structure but different data can be seen here:

employee_data_1 = pd.DataFrame({
  'employee_id': np.arange(1, 6),
  'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],
  'department': ['HR', 'IT', 'Marketing', 'Finance...