Within the Pandas library in Python, indexed-based selection with integer positions using `.iloc` operates on the existing structure of a DataFrame or Series. Attempting to assign values outside the current bounds of the object, such as adding new rows or columns through `.iloc` indexing, will result in an error. For instance, if a DataFrame has five rows, accessing and assigning a value to the sixth row using `.iloc[5]` is not permitted. Instead, methods like `.loc` with label-based indexing, or operations such as concatenation and appending, should be employed for expanding the data structure.
This constraint is essential for maintaining data integrity and predictability. It prevents inadvertent modifications beyond the defined dimensions of the object, ensuring that operations using integer-based indexing remain within the expected boundaries. This behavior differs from some other indexing methods, which might automatically expand the data structure if an out-of-bounds index is accessed. This clear distinction in functionality between indexers contributes to more robust and less error-prone code. Historically, this behavior has been consistent within Pandas, reflecting a design choice that prioritizes explicit data manipulation over implicit expansion.
