Aherobo Ovie Victor
58 lines
2.5 KiB
Python
58 lines
2.5 KiB
Python
import pandas as pd
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def aggregate_results(matched_names, expert_data, community_data, max_results=30):
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"""
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Aggregate similar images, appraisal values, years, and status for matched tag names.
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Args:
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matched_names (list): List of tag names to match.
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expert_data (pd.DataFrame): Expert dataset.
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community_data (pd.DataFrame): Community dataset.
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max_results (int): Maximum number of results to return.
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Returns:
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dict: Aggregated results with images, appraisal values, years, and status.
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"""
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similar_data = []
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for title in matched_names:
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# Handle expert data (no 'year' column)
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community_items = community_data[community_data['brand_name'] == title]
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expert_items = expert_data[expert_data['brand_name'] == title]
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# Process community data (has 'year' column)
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if not community_items.empty:
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# Use 'year' column if it exists, otherwise use 'year_start'
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year_col = 'year' if 'year' in community_items.columns else 'year_start'
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community_records = community_items[['front_tag', 'appraisal_value', 'key', 'status', year_col]].to_dict('records')
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# Rename year column to 'year' for consistency
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for record in community_records:
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record['year'] = record.pop(year_col) if year_col in record else None
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similar_data.extend(community_records)
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# Process expert data (no 'year' column)
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if not expert_items.empty:
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expert_records = expert_items[['front_tag', 'appraisal_value', 'key', 'status']].to_dict('records')
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# Add None for year since expert data doesn't have it
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for record in expert_records:
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record['year'] = None
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similar_data.extend(expert_records)
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# Remove duplicates by key, preserving order
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seen_keys = set()
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unique_data = []
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for item in similar_data:
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if item['key'] not in seen_keys:
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seen_keys.add(item['key'])
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unique_data.append(item)
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# Prepare results
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similar_images = [item['front_tag'] for item in unique_data][:max_results]
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appraisal_values = [item['appraisal_value'] for item in unique_data][:max_results]
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years = [item.get('year') for item in unique_data][:max_results]
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statuses = [item['status'] for item in unique_data][:max_results]
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return {
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'similar_images': similar_images,
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'appraisal_values': appraisal_values,
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'years': years,
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'statuses': statuses
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} |