Final answer:
To recombine hierarchical data abstracted into multiple dataframes, merging and joining are most commonly used, depending on the data structure requirements.
Step-by-step explanation:
To recombine hierarchical data that was abstracted into multiple dataframes, one must understand the different types of operations that can be performed. Here are the options provided:
- Merging is typically used when you want to combine data on common columns or indices.
- Joining is similar to merging, but it is often used specifically with SQL-like operations primarily involving combining data on a key.
- Stacking involves taking data from multiple columns and stacking them into a single column, often used within multi-indexed dataframes.
- Pivoting is a transformation to reshape data, which can turn unique values from one column in the dataframe into multiple columns, with the need to reorganize the data based on column values and not necessarily on combining dataframes.
In conclusion, merging and joining are the operations most likely to be used for the purpose of recombining hierarchical data that was split into multiple dataframes, depending on the specific requirements of the data structure.