feat: Add recent performance stability stats (matches/days) to player profile

2026-01-28 14:04:32 +08:00
commit 48f1f71d3a
104 changed files with 17572 additions and 0 deletions
--- a/ETL/verify/verify_deep.py
+++ b/ETL/verify/verify_deep.py
@@ -0,0 +1,82 @@
+import sqlite3
+import pandas as pd
+import numpy as np
+import sys
+
+# 设置pandas显示选项，确保不省略任何行和列
+pd.set_option('display.max_rows', None)
+pd.set_option('display.max_columns', None)
+pd.set_option('display.width', 2000)
+pd.set_option('display.float_format', '{:.2f}'.format)
+pd.set_option('display.max_colwidth', None)
+
+db_path = 'database/L2/L2_Main.sqlite'
+
+def check_all_tables():
+    conn = sqlite3.connect(db_path)
+    
+    # 获取所有表名
+    tables = pd.read_sql("SELECT name FROM sqlite_master WHERE type='table' AND name NOT LIKE 'sqlite_%'", conn)['name'].tolist()
+    
+    for table in tables:
+        print(f"\n{'='*20} Table: {table} {'='*20}")
+        
+        # 获取表的所有列
+        cols_info = pd.read_sql(f"PRAGMA table_info({table})", conn)
+        cols = cols_info['name'].tolist()
+        
+        # 读取全表数据
+        df = pd.read_sql(f"SELECT * FROM {table}", conn)
+        total = len(df)
+        
+        if total == 0:
+            print(f"Table is empty (0 rows)")
+            continue
+            
+        print(f"Total Rows: {total}")
+        print("-" * 60)
+        
+        stats = []
+        for col in cols:
+            # 1. Null Check
+            nulls = df[col].isnull().sum()
+            
+            # 2. Zero Check (仅对数值型或可转换为数值的列)
+            zeros = 0
+            try:
+                # 尝试转为数值，无法转换的变为NaN
+                numeric_series = pd.to_numeric(df[col], errors='coerce')
+                # 统计0值 (排除原本就是NaN的)
+                zeros = (numeric_series == 0).sum()
+            except:
+                zeros = 0
+                
+            # 3. Unique Count (基数)
+            unique_count = df[col].nunique()
+            
+            # 4. Example Value (取第一个非空值)
+            example = df[col].dropna().iloc[0] if df[col].count() > 0 else 'ALL NULL'
+            
+            stats.append({
+                'Field': col,
+                'Nulls': nulls,
+                'Null%': (nulls/total)*100,
+                'Zeros': zeros,
+                'Zero%': (zeros/total)*100,
+                'Unique': unique_count,
+                'Example': str(example)[:50] # 截断过长示例
+            })
+            
+        # 输出完整统计表
+        df_stats = pd.DataFrame(stats)
+        # 按 Zero% 降序排列，但保证 Null% 高的也显眼，这里默认不排序直接按字段序，或者按关注度排序
+        # 用户要求全面探查，按字段原序输出可能更符合直觉，或者按Zero%排序
+        # 这里为了排查问题，按 Zero% 降序输出
+        df_stats = df_stats.sort_values('Zero%', ascending=False)
+        print(df_stats.to_string(index=False))
+        print("\n")
+        
+    conn.close()
+
+if __name__ == "__main__":
+    check_all_tables()