Force all data in the Frame to be laid out physically.
In datatable, a Frame may contain “virtual” columns, i.e. columns whose data is computed on-the-fly. This allows us to have better performance for certain types of computations, while also reducing the total memory footprint. The use of virtual columns is generally transparent to the user, and datatable will materialize them as needed.
However, there could be situations where you might want to materialize your Frame explicitly. In particular, materialization will carry out all delayed computations and break internal references on other Frames’ data. Thus, for example if you subset a large frame to create a smaller subset, then the new frame will carry an internal reference to the original, preventing it from being garbage-collected. However, if you materialize the small frame, then the data will be physically copied, allowing the original frame’s memory to be freed.
If True, then, in addition to de-virtualizing all columns, this method will also copy all memory-mapped columns into the RAM.
When you open a Jay file, the Frame that is created will contain
memory-mapped columns whose data still resides on disk. Calling
.materialize(to_memory=True) will force the data to be loaded
into the main memory. This may be beneficial if you are concerned
about the disk speed, or if the file is on a removable drive, or
if you want to delete the source file.
This operation modifies the frame in-place.