增加去除每天第一个盲盒，以及增加模拟投盲盒

blindbox_analysis.py

@@ -50,6 +50,31 @@ blindbox_config: dict[str, BlindboxInfo] = {
             }
         }
     },
+    "幸运盲盒": {
+        "price": 50,
+        "items": {
+            "幸运泡泡": {
+                "price": 15,
+                "probability": 49.6
+            },
+            "星光铃铛": {
+                "price": 52,
+                "probability": 42.2
+            },
+            "梦雾纸签": {
+                "price": 100,
+                "probability": 5
+            },
+            "福灵小兽": {
+                "price": 200,
+                "probability": 2.4
+            },
+            "星愿花园": {
+                "price": 600,
+                "probability": 0.8
+            }
+        }
+    },
     "心动盲盒": {
         "price": 150,  # 请根据实际盲盒价格填写
         "items": {
@@ -250,13 +275,33 @@ def profit_statistic(df: pd.DataFrame) -> dict[str, dict[str, Any]]:
 def run_statistics(df: pd.DataFrame) -> dict[str, dict[str, Any]]:
     """运行统计分析"""
     stats = {}
+
     # 统计整体数据
     total_stats = profit_statistic(df)
     stats["整体"] = total_stats
+
+    # 统计去掉每个用户每天第一次开盲盒的数据
+    def remove_first_record_per_group(group):
+        """去掉每组的第一条记录"""
+        return group.iloc[1:]  # 从第二条开始返回
+
+    # 确保时间列是datetime格式
+    if 'time' in df.columns:
+        df['time'] = pd.to_datetime(df['time'])
+        # 提取日期部分（去掉时间）
+        df['date'] = df['time'].dt.date
+    # 按日期、用户ID、盲盒名称分组
+    grouped = df.groupby(['date', 'username', 'blindbox_name'])
+    # 去掉每组的第一条数据
+    df_filtered = grouped.apply(remove_first_record_per_group).reset_index(drop=True)
+    filtered_stats = profit_statistic(df_filtered)
+    stats["去除保底"] = filtered_stats
+
     # 统计周五以外的数据
     non_friday_df = df[pd.to_datetime(df["time"]).dt.weekday != 4]
     non_friday_stats = profit_statistic(non_friday_df)
     stats["非周五"] = non_friday_stats
+    
     # 统计周五的数据
     friday_df = df[pd.to_datetime(df["time"]).dt.weekday == 4]
     friday_stats = profit_statistic(friday_df)
@@ -336,4 +381,6 @@ if __name__ == "__main__":
         # 运行统计分析
         stats = run_statistics(result_df)
         print("\n统计结果:")
-        print_tree(stats)
+        print_tree(stats)
+    else:
+        print("未找到任何盲盒数据")

blindbox_simulation.py

@@ -0,0 +1,54 @@
+import numpy as np
+import pandas as pd
+from blindbox_analysis import blindbox_config, print_tree, profit_statistic
+
+def generate_random_data(blindbox_name: str, num_records: int):
+    """
+    生成随机盲盒数据
+    """
+    np.random.seed(42)  # 固定随机种子，确保结果可复现
+
+    dataset = []
+    for i in range(num_records):
+        # 随机生成用户ID和用户名
+        uid = np.random.randint(1000, 2000)
+        username = f'user_{uid}'
+
+        blindbox_info = blindbox_config[blindbox_name]
+
+        # 抽选盲盒物品
+        rand_num = np.random.rand()
+        sum_prob = 0
+        selected_item = None
+        for item, item_info in blindbox_info['items'].items():
+            prob = item_info['probability'] / 100
+            if rand_num > sum_prob and rand_num <= sum_prob + prob:
+                selected_item = (item, item_info)
+                break
+            sum_prob += prob
+
+        if selected_item is None:
+            raise ValueError("未能选中任何物品")
+
+        # 构建随机数据记录
+        dataset.append({
+            'uid': uid,
+            'username': username,
+            'blindbox_name': blindbox_name,
+            'blindbox_price': blindbox_info['price'],
+            'item_name': selected_item[0],
+            'item_price': selected_item[1]['price'],
+            'profit': selected_item[1]['price'] - blindbox_info['price'],
+            'time': pd.Timestamp.now()
+        })
+
+    return pd.DataFrame(dataset)
+
+if __name__ == "__main__":
+    # 生成随机数据
+    stats = {}
+    for box_name in blindbox_config.keys():
+        df = generate_random_data(box_name, 10000)
+        stats[box_name] = profit_statistic(df)
+
+    print_tree(stats)