Finalize dropped albums list with correct 8 albums and balance new albums

- 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>

scripts/correct_new_albums.py

@@ -0,0 +1,86 @@
+#!/usr/bin/env python3
+"""
+Correct the "New in 2023" markings to show only truly new albums.
+Should be 8 new albums to balance the 8 dropped albums.
+"""
+
+import csv
+
+def normalize_text(text):
+    """Normalize text for comparison"""
+    return text.lower().strip().replace('&', 'and').replace('  ', ' ')
+
+def main():
+    # Read 2020 albums for comparison
+    albums_2020 = set()
+    with open('rolling_stone_2020_simplified.csv', 'r', encoding='utf-8') as file:
+        reader = csv.DictReader(file)
+        for row in reader:
+            key = (normalize_text(row['Artist']), normalize_text(row['Album']))
+            albums_2020.add(key)
+    
+    print(f"📊 Loaded {len(albums_2020)} albums from 2020 list")
+    
+    # Read current 2023 data
+    albums_2023 = []
+    with open('top_500_albums_2023.csv', 'r', encoding='utf-8') as file:
+        reader = csv.DictReader(file)
+        for row in reader:
+            albums_2023.append(row)
+    
+    # Analyze albums marked as "New in 2023"
+    currently_marked_new = []
+    truly_new = []
+    incorrectly_marked = []
+    
+    for album in albums_2023:
+        if 'New in 2023' in album.get('Status', ''):
+            currently_marked_new.append(album)
+            
+            # Check if this album was actually in 2020
+            key = (normalize_text(album['Artist']), normalize_text(album['Album']))
+            if key in albums_2020:
+                incorrectly_marked.append(album)
+            else:
+                truly_new.append(album)
+    
+    print(f"\\n📊 Analysis of albums marked as 'New in 2023':")
+    print(f"   Total marked as new: {len(currently_marked_new)}")
+    print(f"   Truly new (not in 2020): {len(truly_new)}")
+    print(f"   Incorrectly marked (were in 2020): {len(incorrectly_marked)}")
+    
+    print(f"\\n✅ Truly new albums in 2023:")
+    for album in truly_new:
+        print(f"   #{album['Rank']} - {album['Artist']} - {album['Album']}")
+    
+    print(f"\\n❌ Incorrectly marked as new (were in 2020):")
+    for album in incorrectly_marked:
+        print(f"   #{album['Rank']} - {album['Artist']} - {album['Album']}")
+    
+    # Update the CSV to correct the statuses
+    updated_albums = []
+    for album in albums_2023:
+        updated_album = album.copy()
+        
+        # If this album is marked as "New in 2023" but was actually in 2020, correct it
+        if 'New in 2023' in album.get('Status', ''):
+            key = (normalize_text(album['Artist']), normalize_text(album['Album']))
+            if key in albums_2020:
+                # This was incorrectly marked - change to "No change" or appropriate status
+                updated_album['Status'] = 'No change'
+        
+        updated_albums.append(updated_album)
+    
+    # Write corrected CSV
+    with open('top_500_albums_2023.csv', 'w', newline='', encoding='utf-8') as file:
+        fieldnames = ['Rank', 'Artist', 'Album', 'Status', 'Info', 'Description']
+        writer = csv.DictWriter(file, fieldnames=fieldnames)
+        writer.writeheader()
+        writer.writerows(updated_albums)
+    
+    print(f"\\n✅ Corrected CSV written with proper 'New in 2023' markings")
+    print(f"📁 Updated: top_500_albums_2023.csv")
+    print(f"\\n📊 Final count: {len(truly_new)} truly new albums in 2023")
+
+if __name__ == "__main__":
+    main()