88a6434132
- Corrected dropped albums to exactly 8 albums through detailed comparison analysis - Updated dropped albums list (ranks 501-508) with proper albums that were truly removed - Fixed "New in 2023" markings to show only 8 albums (balancing the 8 dropped) - Downloaded cover art for all 8 dropped albums - Removed incorrect cover art files for albums that weren't actually dropped - Updated data files with corrected artist/album name formatting for accurate matching 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
86 lines
No EOL
3.2 KiB
Python
86 lines
No EOL
3.2 KiB
Python
#!/usr/bin/env python3
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"""
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Correct the "New in 2023" markings to show only truly new albums.
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Should be 8 new albums to balance the 8 dropped albums.
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"""
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import csv
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def normalize_text(text):
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"""Normalize text for comparison"""
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return text.lower().strip().replace('&', 'and').replace(' ', ' ')
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def main():
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# Read 2020 albums for comparison
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albums_2020 = set()
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with open('rolling_stone_2020_simplified.csv', 'r', encoding='utf-8') as file:
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reader = csv.DictReader(file)
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for row in reader:
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key = (normalize_text(row['Artist']), normalize_text(row['Album']))
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albums_2020.add(key)
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print(f"📊 Loaded {len(albums_2020)} albums from 2020 list")
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# Read current 2023 data
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albums_2023 = []
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with open('top_500_albums_2023.csv', 'r', encoding='utf-8') as file:
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reader = csv.DictReader(file)
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for row in reader:
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albums_2023.append(row)
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# Analyze albums marked as "New in 2023"
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currently_marked_new = []
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truly_new = []
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incorrectly_marked = []
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for album in albums_2023:
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if 'New in 2023' in album.get('Status', ''):
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currently_marked_new.append(album)
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# Check if this album was actually in 2020
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key = (normalize_text(album['Artist']), normalize_text(album['Album']))
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if key in albums_2020:
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incorrectly_marked.append(album)
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else:
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truly_new.append(album)
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print(f"\\n📊 Analysis of albums marked as 'New in 2023':")
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print(f" Total marked as new: {len(currently_marked_new)}")
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print(f" Truly new (not in 2020): {len(truly_new)}")
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print(f" Incorrectly marked (were in 2020): {len(incorrectly_marked)}")
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print(f"\\n✅ Truly new albums in 2023:")
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for album in truly_new:
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print(f" #{album['Rank']} - {album['Artist']} - {album['Album']}")
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print(f"\\n❌ Incorrectly marked as new (were in 2020):")
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for album in incorrectly_marked:
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print(f" #{album['Rank']} - {album['Artist']} - {album['Album']}")
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# Update the CSV to correct the statuses
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updated_albums = []
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for album in albums_2023:
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updated_album = album.copy()
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# If this album is marked as "New in 2023" but was actually in 2020, correct it
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if 'New in 2023' in album.get('Status', ''):
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key = (normalize_text(album['Artist']), normalize_text(album['Album']))
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if key in albums_2020:
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# This was incorrectly marked - change to "No change" or appropriate status
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updated_album['Status'] = 'No change'
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updated_albums.append(updated_album)
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# Write corrected CSV
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with open('top_500_albums_2023.csv', 'w', newline='', encoding='utf-8') as file:
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fieldnames = ['Rank', 'Artist', 'Album', 'Status', 'Info', 'Description']
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writer = csv.DictWriter(file, fieldnames=fieldnames)
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writer.writeheader()
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writer.writerows(updated_albums)
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print(f"\\n✅ Corrected CSV written with proper 'New in 2023' markings")
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print(f"📁 Updated: top_500_albums_2023.csv")
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print(f"\\n📊 Final count: {len(truly_new)} truly new albums in 2023")
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if __name__ == "__main__":
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main() |