To transform the compressed table into the desired expanded format, each row must be unpacked based on the Count field by generating consecutive hourly timestamps starting from the given Datetime. For each row, we replicate the Value for the number of hours specified by Count, incrementing the timestamp by one hour for each replication. This can be efficiently done using Python with Pandas by iterating through each row, creating new entries with updated timestamps, and compiling the results into a new DataFrame. Sorting the final output by Category and Datetime ensures the structure aligns with the expected chronological order. This approach effectively restores the original granularity of the time series data while maintaining category-wise separation.