Final answer:
To convert hierarchical data into data frames, the process of flattening is used, which transforms nested structures into a flat table format suitable for a data frame.
Step-by-step explanation:
The process of converting hierarchical data to data frames can be done through flattening. Hierarchical data often has nested structures or parent-child relationships, which do not fit neatly into the tabular format of a data frame. Flattening involves transforming this nested hierarchical structure into a flat table where each row represents a record and the hierarchy is represented through additional columns.
There is no direct process called normalization or denormalization used specifically for converting hierarchical data to data frames, as these terms are more closely associated with database design. Normalization involves organizing a database to reduce redundancy and improve data integrity, while denormalization is the process of trying to improve database read performance by adding redundant data or by grouping data. Indexing is a technique to quickly locate and access the data in a database.