| import pandas as pd | |
| import os | |
| # Load the data | |
| metadata = pd.read_csv("gaps_metadata.csv") | |
| splits = pd.read_csv("gaps_split_data.csv") | |
| # Extract path stems from the splits data (filename without extension) | |
| splits['path_stem'] = splits['path'].apply(lambda x: os.path.splitext(os.path.basename(x))[0]) | |
| # Merge the splits information with the metadata based on scorehash = path_stem | |
| merged_metadata = metadata.merge( | |
| splits[['path_stem', 'split']], | |
| left_on='scorehash', | |
| right_on='path_stem', | |
| how='left' | |
| ) | |
| # Drop the temporary path_stem column | |
| merged_metadata = merged_metadata.drop('path_stem', axis=1) | |
| # Save the updated metadata to a new CSV file | |
| merged_metadata.to_csv("gaps_metadata_with_splits.csv", index=False) | |
| print(f"Merged metadata saved to gaps_metadata_with_splits.csv") | |
| print(f"Added split information to {merged_metadata['split'].notna().sum()} out of {len(merged_metadata)} records") | |