Initial commit

pyground/sugar/统计.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import logging
+import sys
+from datetime import datetime
+import pymysql
+
+# ================= 配置部分 =================
+# 日志配置
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(levelname)s - %(message)s",
+    handlers=[
+        logging.StreamHandler(sys.stdout),
+        logging.FileHandler(
+            f'game_stats_{datetime.now().strftime("%Y%m%d")}.log', encoding="utf-8"
+        ),
+    ],
+)
+
+# 数据库连接配置 (请根据实际情况修改)
+DB_CONFIG = {
+    "host": "localhost",
+    "user": "root",
+    "password": "123456",
+    "database": "fire",
+    "charset": "utf8mb4",
+}
+
+# ================= 核心逻辑 =================
+
+
+def get_db_connection():
+    """获取数据库连接"""
+    try:
+        conn = pymysql.connect(**DB_CONFIG)
+        logging.info("数据库连接成功")
+        return conn
+    except Exception as e:
+        logging.error(f"数据库连接失败: {e}")
+        raise
+
+
+def fetch_data(conn, type):
+    """从数据库获取数据"""
+    query = f"SELECT gid, score, feature, extra_free, create_time FROM game_sugar_spin{type}"
+    try:
+        logging.info(f"正在执行查询: {query}")
+        df = pd.read_sql(query, conn)
+        logging.info(f"数据加载完成，共 {len(df)} 行")
+        return df
+    except Exception as e:
+        logging.error(f"数据查询失败: {e}")
+        raise
+
+
+def analyze_data(df):
+    """使用 Pandas 进行统计分析"""
+    logging.info("开始进行统计分析...")
+
+    # 1. 基础统计
+    basic_stats = df["score"].describe().to_frame().T
+    logging.info(f"\n基础统计信息:\n{basic_stats.to_string()}")
+
+    # 2. 统计得分为 0 的数量
+    zero_score_count = (df["score"] == 0).sum()
+    total_count = len(df)
+    zero_ratio = (
+        (zero_score_count / total_count * 100).round(2) if total_count > 0 else 0
+    )
+    logging.info(f"\n得分为 0 的统计: {zero_score_count} 次 (占比: {zero_ratio}%)")
+
+    # 2.1 统计免费旋转大于0的数量
+    extra_free_count = (df["extra_free"] > 0).sum()
+    total_count = len(df)
+    zero_ratio = (
+        (extra_free_count / total_count * 100).round(2) if total_count > 0 else 0
+    )
+    logging.info(
+        f"\n免费旋转次数大于 0 的统计: {extra_free_count} 次 (占比: {zero_ratio}%)"
+    )
+
+    # 3. 得分区间分布
+    bins = [0, 0.1, 0.5, 1, 10, 50, 100, 500, float("inf")]
+    labels = ["0-0.1", "0.1-0.5", "0.5-1", "1-10", "11-50", "51-100", "101-500", "500+"]
+    df["score_range"] = pd.cut(df["score"], bins=bins, labels=labels, right=False)
+
+    # 计算数量
+    range_dist = df["score_range"].value_counts().sort_index().to_frame(name="count")
+
+    # 计算比例 (百分比)
+    range_dist["percentage"] = (range_dist["count"] / len(df) * 100).round(2)
+
+    # 格式化日志输出，拼接百分比
+    # 使用 apply 将每一行转换为 "数量 (百分比%)" 的格式
+    log_output = range_dist.apply(
+        lambda row: f"{row['count']} ({row['percentage']}%)", axis=1
+    ).to_string(name=False)
+
+    logging.info(f"\n得分区间分布 (数量 | 占比):\n{log_output}")
+
+    return basic_stats, range_dist
+
+
+def visualize_data(range_dist):
+    """生成可视化图表"""
+    logging.info("正在生成图表...")
+    plt.rcParams["font.sans-serif"] = ["SimHei"]  # 用来正常显示中文标签
+    plt.rcParams["axes.unicode_minus"] = False  # 用来正常显示负号
+
+    fig, axes = plt.subplots(1, 2, figsize=(16, 6))
+
+    # 图2: 得分区间分布 (饼图)
+    colors = [
+        "#ff9999",
+        "#ffb3e6",
+        "#66b3ff",
+        "#99ff99",
+        "#ffcc99",
+        "#c2c2f0",
+        "#ffb3e6",
+    ]
+    range_dist["count"].plot(
+        kind="pie", ax=axes[1], autopct="%1.1f%%", startangle=90, colors=colors
+    )
+    axes[1].set_title("得分区间分布占比", fontsize=14)
+    axes[1].set_ylabel("")  # 隐藏y轴标签
+
+    plt.tight_layout()
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    filename = f"game_score_stats_{timestamp}.png"
+    plt.savefig(filename)
+    logging.info(f"图表已保存为: {filename}")
+    # plt.show() # 如需弹窗显示可取消注释
+
+
+def main():
+    start_time = datetime.now()
+    logging.info(f"任务开始执行: {start_time}")
+
+    conn = None
+    try:
+        conn = get_db_connection()
+        df = fetch_data(conn, "")
+
+        # 打印前5行数据预览
+        # logging.info("数据预览:\n" + df.head().to_string())
+
+        basic_stats, range_dist = analyze_data(df)
+        # visualize_data(range_dist)
+
+        basic_stats, range_dist = analyze_data(df.query("extra_free > 0"))
+
+    except Exception as e:
+        logging.error(f"任务执行出错: {e}")
+    finally:
+        if conn:
+            conn.close()
+            logging.info("数据库连接已关闭")
+
+    end_time = datetime.now()
+    duration = (end_time - start_time).total_seconds()
+    logging.info(f"任务执行完成，耗时: {duration:.2f} 秒")
+
+
+if __name__ == "__main__":
+    main()