feat: merge datetime fields in DataFrame output, hide MinuteBar internal fields

- K-line: daily+ periods output 'date' only, minute periods output 'datetime'
- Transactions (tick-by-tick): combine date param + hour/minute into 'datetime'
- XdxrRecord, HistoricalFundFlow: year/month/day merged to 'date'
- MinuteBar: rename unknown_1 to _unknown_1 (hidden from DataFrame)
- MinuteBar: add datetime column computed from bar index (A-share 240-bar pattern)
- get_minute_time_data: use history endpoint only (current-day endpoint broken in pytdx too)
- Update all examples to reflect new DataFrame column names

examples/01_connection/async_connect.py

@@ -1,7 +1,8 @@
 """演示：异步客户端连接与基本用法。"""
 
 import asyncio
-from easy_tdx import AsyncTdxClient, Market, KlineCategory
+
+from easy_tdx import AsyncTdxClient, KlineCategory, Market
 
 
 async def main():
@@ -12,13 +13,8 @@ async def main():
 
     # 自动优选服务器
     async with AsyncTdxClient.from_best_host() as c:
-        bars = await c.get_security_bars(
-            Market.SH, "600000", KlineCategory.DAY, 0, 5
-        )
-        for bar in bars:
-            print(f"{bar.year}-{bar.month:02d}-{bar.day:02d} "
-                  f"开:{bar.open:.2f} 高:{bar.high:.2f} "
-                  f"低:{bar.low:.2f} 收:{bar.close:.2f}")
+        df = await c.get_security_bars(Market.SH, "600000", KlineCategory.DAY, 0, 5)
+        print(df.to_string(index=False))
 
 
 asyncio.run(main())

examples/02_market_info/market_stat.py

@@ -1,19 +1,7 @@
 """演示：获取全市场涨跌统计概况。"""
 
-import pandas as pd
 from easy_tdx import TdxClient
 
 with TdxClient.from_best_host() as c:
     stat = c.get_market_stat()
-    df = pd.DataFrame([{
-        "上涨": stat.up_count,
-        "下跌": stat.down_count,
-        "平盘": stat.neutral_count,
-        "停牌(估算)": stat.suspended_count,
-        "总计": stat.total_count,
-        "成交额(亿)": round(stat.total_amount / 1e8, 2),
-        "总市值(万亿)": round(stat.total_market_cap / 1e12, 4),
-        "涨停": stat.limit_up_count,
-        "跌停": stat.limit_down_count,
-    }])
-    print(df.T.to_string(header=False))
+    print(stat)

examples/02_market_info/security_list.py

@@ -1,40 +1,63 @@
-"""演示：获取市场证券列表（分页）。
-
-展示 SecurityInfo 全部字段的中文映射与表结构。
-"""
+"""演示：获取市场证券列表（分页）。"""
 
 import pandas as pd
-from easy_tdx import TdxClient, Market
+from easy_tdx import Market, TdxClient
 
 with TdxClient.from_best_host() as c:
-    stocks = c.get_security_list(Market.SH, start=0)
+    df = c.get_security_list(Market.SH, start=0)
 
     # 表结构说明
     print("=" * 70)
     print("SecurityInfo 表结构（字段中英文对照）")
     print("=" * 70)
-    schema = pd.DataFrame([
-        {"英文字段": "market",        "中文含义": "市场",      "类型": "Market",   "说明": "SZ=深圳 SH=上海 BJ=北京"},
-        {"英文字段": "code",          "中文含义": "证券代码",   "类型": "str",      "说明": "6位代码，如 600000"},
-        {"英文字段": "name",          "中文含义": "证券名称",   "类型": "str",      "说明": "GBK 解码"},
-        {"英文字段": "volunit",       "中文含义": "成交量单位", "类型": "int",      "说明": "1手 = volunit 股"},
-        {"英文字段": "decimal_point", "中文含义": "价格小数位", "类型": "int",      "说明": "通常为 2"},
-        {"英文字段": "pre_close",     "中文含义": "昨收价",     "类型": "float",    "说明": "通达信自定义浮点"},
-        {"英文字段": "industry_tdx",  "中文含义": "通达信行业", "类型": "str",      "说明": "需 get_security_list_all()"},
-        {"英文字段": "industry_sw",   "中文含义": "申万行业",   "类型": "str",      "说明": "需 get_security_list_all()"},
-    ])
+    schema = pd.DataFrame(
+        [
+            {
+                "英文字段": "market",
+                "中文含义": "市场",
+                "类型": "Market",
+                "说明": "SZ=深圳 SH=上海 BJ=北京",
+            },
+            {
+                "英文字段": "code",
+                "中文含义": "证券代码",
+                "类型": "str",
+                "说明": "6位代码，如 600000",
+            },
+            {"英文字段": "name", "中文含义": "证券名称", "类型": "str", "说明": "GBK 解码"},
+            {
+                "英文字段": "volunit",
+                "中文含义": "成交量单位",
+                "类型": "int",
+                "说明": "1手 = volunit 股",
+            },
+            {
+                "英文字段": "decimal_point",
+                "中文含义": "价格小数位",
+                "类型": "int",
+                "说明": "通常为 2",
+            },
+            {
+                "英文字段": "pre_close",
+                "中文含义": "昨收价",
+                "类型": "float",
+                "说明": "通达信自定义浮点",
+            },
+            {
+                "英文字段": "industry_tdx",
+                "中文含义": "通达信行业",
+                "类型": "str",
+                "说明": "需 get_security_list_all()",
+            },
+            {
+                "英文字段": "industry_sw",
+                "中文含义": "申万行业",
+                "类型": "str",
+                "说明": "需 get_security_list_all()",
+            },
+        ]
+    )
     print(schema.to_string(index=False))
 
-    # 全字段中文 DataFrame
-    print(f"\n沪市第 1 页，共 {len(stocks)} 只:")
-    df = pd.DataFrame([{
-        "市场": s.market.name,
-        "代码": s.code,
-        "名称": s.name,
-        "成交量单位(股/手)": s.volunit,
-        "价格小数位": s.decimal_point,
-        "昨收价": s.pre_close,
-        "通达信行业": s.industry_tdx or "",
-        "申万行业": s.industry_sw or "",
-    } for s in stocks])
+    print(f"\n沪市第 1 页，共 {len(df)} 只:")
     print(df.head(20).to_string(index=False))

examples/02_market_info/security_list_all.py

@@ -1,10 +1,10 @@
 """演示：获取沪深 A 股完整列表（含行业映射）。
 
-展示 SecurityInfo 全部字段（含扩展行业字段）的中文映射。
 注意：此方法需要拉取 tdxhy.cfg 并遍历全部证券，耗时较长。
 """
 
 import logging
+
 import pandas as pd
 from easy_tdx import TdxClient
 
@@ -13,34 +13,60 @@ logging.basicConfig(level=logging.INFO, format="%(message)s")
 
 # timeout 调大到 30 秒，避免全量拉取时分页请求超时
 with TdxClient.from_best_host(timeout=30.0) as c:
-    all_stocks = c.get_security_list_all()
+    df = c.get_security_list_all()
 
     # 表结构说明
     print("=" * 70)
     print("SecurityInfo 表结构（字段中英文对照）")
     print("=" * 70)
-    schema = pd.DataFrame([
-        {"英文字段": "market",        "中文含义": "市场",      "类型": "Market",   "说明": "SZ=深圳 SH=上海 BJ=北京"},
-        {"英文字段": "code",          "中文含义": "证券代码",   "类型": "str",      "说明": "6位代码，如 600000"},
-        {"英文字段": "name",          "中文含义": "证券名称",   "类型": "str",      "说明": "GBK 解码"},
-        {"英文字段": "volunit",       "中文含义": "成交量单位", "类型": "int",      "说明": "1手 = volunit 股"},
-        {"英文字段": "decimal_point", "中文含义": "价格小数位", "类型": "int",      "说明": "通常为 2"},
-        {"英文字段": "pre_close",     "中文含义": "昨收价",     "类型": "float",    "说明": "通达信自定义浮点"},
-        {"英文字段": "industry_tdx",  "中文含义": "通达信行业", "类型": "str",      "说明": "如 T1001，来自 tdxhy.cfg"},
-        {"英文字段": "industry_sw",   "中文含义": "申万行业",   "类型": "str",      "说明": "如 X500102，来自 tdxhy.cfg"},
-    ])
+    schema = pd.DataFrame(
+        [
+            {
+                "英文字段": "market",
+                "中文含义": "市场",
+                "类型": "Market",
+                "说明": "SZ=深圳 SH=上海 BJ=北京",
+            },
+            {
+                "英文字段": "code",
+                "中文含义": "证券代码",
+                "类型": "str",
+                "说明": "6位代码，如 600000",
+            },
+            {"英文字段": "name", "中文含义": "证券名称", "类型": "str", "说明": "GBK 解码"},
+            {
+                "英文字段": "volunit",
+                "中文含义": "成交量单位",
+                "类型": "int",
+                "说明": "1手 = volunit 股",
+            },
+            {
+                "英文字段": "decimal_point",
+                "中文含义": "价格小数位",
+                "类型": "int",
+                "说明": "通常为 2",
+            },
+            {
+                "英文字段": "pre_close",
+                "中文含义": "昨收价",
+                "类型": "float",
+                "说明": "通达信自定义浮点",
+            },
+            {
+                "英文字段": "industry_tdx",
+                "中文含义": "通达信行业",
+                "类型": "str",
+                "说明": "如 T1001，来自 tdxhy.cfg",
+            },
+            {
+                "英文字段": "industry_sw",
+                "中文含义": "申万行业",
+                "类型": "str",
+                "说明": "如 X500102，来自 tdxhy.cfg",
+            },
+        ]
+    )
     print(schema.to_string(index=False))
 
-    # 全字段中文 DataFrame
-    print(f"\n沪深 A 股总数: {len(all_stocks)}")
-    df = pd.DataFrame([{
-        "市场": s.market.name,
-        "代码": s.code,
-        "名称": s.name,
-        "成交量单位(股/手)": s.volunit,
-        "价格小数位": s.decimal_point,
-        "昨收价": s.pre_close,
-        "通达信行业": s.industry_tdx or "",
-        "申万行业": s.industry_sw or "",
-    } for s in all_stocks])
+    print(f"\n沪深 A 股总数: {len(df)}")
     print(df.head(20).to_string(index=False))

examples/02_market_info/security_quotes.py

@@ -1,7 +1,6 @@
 """演示：批量获取实时五档行情。最多支持 80 只/次。"""
 
-import pandas as pd
-from easy_tdx import TdxClient, Market
+from easy_tdx import Market, TdxClient
 
 with TdxClient.from_best_host() as c:
     stocks = [
@@ -10,16 +9,10 @@ with TdxClient.from_best_host() as c:
         (Market.SZ, "000001"),  # 平安银行
         (Market.SZ, "000858"),  # 五粮液
     ]
-    quotes = c.get_security_quotes(stocks)
-    df = pd.DataFrame([{
-        "代码": q.code,
-        "现价": q.price,
-        "涨跌幅%": (q.price - q.pre_close) / q.pre_close * 100,
-        "今开": q.open,
-        "最高": q.high,
-        "最低": q.low,
-        "昨收": q.pre_close,
-        "成交量(手)": q.vol,
-        "成交额": q.amount,
-    } for q in quotes])
-    print(df.to_string(index=False))
+    df = c.get_security_quotes(stocks)
+    df["change_pct"] = (df["price"] - df["pre_close"]) / df["pre_close"] * 100
+    print(
+        df[
+            ["code", "price", "change_pct", "open", "high", "low", "pre_close", "vol", "amount"]
+        ].to_string(index=False)
+    )

examples/03_kline/index_bars.py

@@ -6,20 +6,9 @@
   创业板指: Market.SZ, "399006"
 """
 
-import pandas as pd
-from easy_tdx import TdxClient, Market, KlineCategory
+from easy_tdx import KlineCategory, Market, TdxClient
 
 with TdxClient.from_best_host() as c:
-    bars = c.get_index_bars(Market.SH, "999999", KlineCategory.DAY, 0, 10)
-    df = pd.DataFrame([{
-        "日期": f"{b.year}-{b.month:02d}-{b.day:02d}",
-        "开盘": b.open,
-        "最高": b.high,
-        "最低": b.low,
-        "收盘": b.close,
-        "成交量": b.vol,
-        "成交额": b.amount,
-    } for b in reversed(bars)])
+    df = c.get_index_bars(Market.SH, "999999", KlineCategory.DAY, 0, 10)
     print("上证指数 日K线:")
-    fmt = {"成交量": lambda x: f"{x:,.0f}", "成交额": lambda x: f"{x:,.0f}"}
-    print(df.to_string(index=False, formatters=fmt))
+    print(df.to_string(index=False))

examples/03_kline/security_bars.py

@@ -5,19 +5,9 @@ K 线类别:
   KlineCategory.DAY    / WEEK   / MONTH  / YEAR
 """
 
-import pandas as pd
-from easy_tdx import TdxClient, Market, KlineCategory
+from easy_tdx import KlineCategory, Market, TdxClient
 
 with TdxClient.from_best_host() as c:
-    bars = c.get_security_bars(Market.SZ, "002176", KlineCategory.DAY, 0, 100)
-    df = pd.DataFrame([{
-        "日期": f"{b.year}-{b.month:02d}-{b.day:02d}",
-        "开盘": b.open,
-        "最高": b.high,
-        "最低": b.low,
-        "收盘": b.close,
-        "成交量": b.vol,
-        "成交额": b.amount,
-    } for b in reversed(bars)])
-    print("上证指数 日K线:")
+    df = c.get_security_bars(Market.SZ, "002176", KlineCategory.DAY, 0, 100)
+    print("江特电机 日K线:")
     print(df.to_string(index=False))

examples/04_minute/history_minute_data.py

@@ -1,15 +1,9 @@
 """演示：获取历史某日分时数据。date 参数为 YYYYMMDD 格式的整数。"""
 
-import pandas as pd
-from easy_tdx import TdxClient, Market
+from easy_tdx import Market, TdxClient
 
 with TdxClient.from_best_host() as c:
     date = 20250110
-    bars = c.get_history_minute_time_data(Market.SH, "600000", date)
-    df = pd.DataFrame([{
-        "序号": i + 1,
-        "价格": bar.price,
-        "成交量": bar.vol,
-    } for i, bar in enumerate(bars)])
+    df = c.get_history_minute_time_data(Market.SH, "600000", date)
     print(f"浦发银行 {date} 分时数据，共 {len(df)} 条:")
-    print(df.to_string(index=False))
+    print(df.head(20).to_string(index=False))

examples/04_minute/minute_time_data.py

@@ -1,14 +1,8 @@
 """演示：获取今日分时数据（240 条）。"""
 
-import pandas as pd
-from easy_tdx import TdxClient, Market
+from easy_tdx import Market, TdxClient
 
 with TdxClient.from_best_host() as c:
-    bars = c.get_minute_time_data(Market.SH, "600000")
-    df = pd.DataFrame([{
-        "序号": i + 1,
-        "价格": bar.price,
-        "成交量": bar.vol,
-    } for i, bar in enumerate(bars)])
+    df = c.get_minute_time_data(Market.SH, "600000")
     print(f"浦发银行今日分时，共 {len(df)} 条:")
-    print(df.to_string(index=False))
+    print(df.head(20).to_string(index=False))

examples/05_transaction/history_transaction.py

@@ -1,16 +1,10 @@
 """演示：获取历史逐笔成交数据。date 参数为 YYYYMMDD 格式的整数。"""
 
-import pandas as pd
-from easy_tdx import TdxClient, Market
+from easy_tdx import Market, TdxClient
 
 with TdxClient.from_best_host() as c:
     date = 20250110
-    records = c.get_history_transaction_data(Market.SH, "600000", date, 0, 20)
-    df = pd.DataFrame([{
-        "时间": f"{r.hour:02d}:{r.minute:02d}",
-        "成交价": r.price,
-        "成交量": r.vol,
-        "方向": "买" if r.buyorsell == 0 else "卖",
-    } for r in records])
+    df = c.get_history_transaction_data(Market.SH, "600000", date, 0, 20)
+    df["方向"] = df["buyorsell"].map({0: "买", 1: "卖", 2: "中性", 8: "集合竞价"})
     print(f"浦发银行 {date} 最近 {len(df)} 笔成交:")
-    print(df.to_string(index=False))
+    print(df[["datetime", "price", "vol", "方向"]].to_string(index=False))

examples/05_transaction/transaction_data.py

@@ -1,15 +1,9 @@
 """演示：获取当日逐笔成交数据。"""
 
-import pandas as pd
-from easy_tdx import TdxClient, Market
+from easy_tdx import Market, TdxClient
 
 with TdxClient.from_best_host() as c:
-    records = c.get_transaction_data(Market.SH, "600000", 0, 20)
-    df = pd.DataFrame([{
-        "时间": f"{r.hour:02d}:{r.minute:02d}",
-        "成交价": r.price,
-        "成交量": r.vol,
-        "方向": "买" if r.buyorsell == 0 else "卖",
-    } for r in records])
+    df = c.get_transaction_data(Market.SH, "600000", 0, 20)
+    df["方向"] = df["buyorsell"].map({0: "买", 1: "卖", 2: "中性", 8: "集合竞价"})
     print(f"浦发银行最近 {len(df)} 笔成交:")
-    print(df.to_string(index=False))
+    print(df[["datetime", "price", "vol", "方向"]].to_string(index=False))

examples/06_finance/company_info.py

@@ -1,7 +1,6 @@
 """演示：获取公司信息目录与各个分类的详细内容。"""
 
-import pandas as pd
-from easy_tdx import TdxClient, Market
+from easy_tdx import Market, TdxClient
 
 CODE = "600519"
 NAME = "贵州茅台"
@@ -28,51 +27,36 @@ SHOW_CATEGORIES = [
 ]
 
 
-def show_categories(categories):
-    """显示公司信息目录。"""
-    df = pd.DataFrame([{
-        "目录名": cat.name,
-        "文件名": cat.filename,
-        "起始偏移": cat.start,
-        "内容长度": cat.length,
-    } for cat in categories])
-    print(f"{NAME} 公司信息目录:")
-    print(df.to_string(index=False))
-
-
 def show_category_content(client, categories, category_name, max_chars=500):
     """获取并展示指定分类的内容。"""
-    cat = next((c for c in categories if c.name == category_name), None)
-    if not cat:
+    row = categories[categories["name"] == category_name]
+    if row.empty:
         print(f"  未找到分类: {category_name}")
         return
 
+    r = row.iloc[0]
     content = client.get_company_info_content(
-        MARKET, CODE, cat.filename, cat.start, cat.length
+        MARKET, CODE, r["filename"], int(r["start"]), int(r["length"])
     )
     text = content.strip()
     if len(text) > max_chars:
         text = text[:max_chars] + f"\n... (共 {len(content.strip())} 字，仅显示前 {max_chars} 字)"
-    print(f"\n{'='*60}")
-    print(f"【{cat.name}】 (共 {cat.length} 字节)")
-    print(f"{'='*60}")
+    print(f"\n{'=' * 60}")
+    print(f"【{r['name']}】 (共 {r['length']} 字节)")
+    print(f"{'=' * 60}")
     print(text)
 
 
-def show_all_categories(client, categories):
-    """依次展示所有 SHOW_CATEGORIES 中列出的分类内容。"""
-    for name in SHOW_CATEGORIES:
-        show_category_content(client, categories, name)
-
-
 with TdxClient.from_best_host() as c:
     categories = c.get_company_info_category(MARKET, CODE)
 
     # 1. 显示目录
-    show_categories(categories)
+    print(f"{NAME} 公司信息目录:")
+    print(categories.to_string(index=False))
 
     # 2. 显示所有分类内容（每个分类默认只显示前500字）
-    show_all_categories(c, categories)
+    for name in SHOW_CATEGORIES:
+        show_category_content(c, categories, name)
 
     # 3. 也可以单独获取某个分类的完整内容，例如：
     # show_category_content(c, categories, "公司概况", max_chars=99999)

examples/06_finance/finance_info.py

@@ -1,21 +1,8 @@
 """演示：获取最新财务数据。"""
 
-import pandas as pd
-from easy_tdx import TdxClient, Market
+from easy_tdx import Market, TdxClient
 
 with TdxClient.from_best_host() as c:
     info = c.get_finance_info(Market.SH, "600519")
-    df = pd.DataFrame([
-        {"项目": "总股本(万股)", "数值": info.zong_guben},
-        {"项目": "流通股本(万股)", "数值": info.liutong_guben},
-        {"项目": "每股净资产", "数值": info.meigujing_zichan},
-        {"项目": "净利润(元)", "数值": info.jing_lirun},
-        {"项目": "主营收入(元)", "数值": info.zhuying_shouru},
-        {"项目": "主营利润(元)", "数值": info.zhuying_lirun},
-        {"项目": "净资产(元)", "数值": info.jing_zichan},
-        {"项目": "总资产(元)", "数值": info.zong_zichan},
-        {"项目": "股东人数", "数值": info.gudong_renshu},
-        {"项目": "上市日期", "数值": info.ipo_date},
-    ])
     print("贵州茅台 最新财务数据:")
-    print(df.to_string(index=False, formatters={"数值": lambda x: f"{x:,.0f}"}))
+    print(info.T.to_string(header=False))

examples/06_finance/price_limits.py

@@ -1,23 +1,16 @@
 """演示：计算个股涨跌停价格。"""
 
-import pandas as pd
-from easy_tdx import TdxClient, Market
+from easy_tdx import Market, TdxClient
 
 CODE = "600519"
 NAME = "贵州茅台"
 
 with TdxClient.from_best_host() as c:
     quotes = c.get_security_quotes([(Market.SH, CODE)])
-    if quotes:
-        q = quotes[0]
-        limit_up, limit_down = c.get_price_limits(
-            Market.SH, CODE, NAME, q.pre_close
-        )
-        df = pd.DataFrame([{
-            "代码": CODE,
-            "名称": NAME,
-            "昨收": q.pre_close,
-            "涨停价": limit_up,
-            "跌停价": limit_down,
-        }])
-        print(df.to_string(index=False))
+    if not quotes.empty:
+        q = quotes.iloc[0]
+        limit_up, limit_down = c.get_price_limits(Market.SH, CODE, NAME, q["pre_close"])
+        print(f"代码: {CODE}  名称: {NAME}")
+        print(f"昨收: {q['pre_close']}")
+        print(f"涨停价: {limit_up}")
+        print(f"跌停价: {limit_down}")

examples/06_finance/xdxr_info.py

@@ -1,17 +1,8 @@
 """演示：获取除权除息历史记录。"""
 
-import pandas as pd
-from easy_tdx import TdxClient, Market, XDXR_CATEGORY_NAMES
+from easy_tdx import Market, TdxClient
 
 with TdxClient.from_best_host() as c:
-    records = c.get_xdxr_info(Market.SH, "600519")
-    df = pd.DataFrame([{
-        "日期": f"{r.year}-{r.month:02d}-{r.day:02d}",
-        "类型": XDXR_CATEGORY_NAMES.get(r.category, f"未知({r.category})"),
-        "每股分红(元)": r.fenhong,
-        "送转股比例": r.songzhuangu,
-        "配股价": r.peigujia,
-        "配股比例": r.peigu,
-    } for r in records])
+    df = c.get_xdxr_info(Market.SH, "600519")
     print(f"贵州茅台 除权除息记录，共 {len(df)} 条:")
     print(df.tail(10).to_string(index=False))

examples/07_block/block_info.py

@@ -6,16 +6,9 @@
   'block_fg.dat' - 风格板块
 """
 
-import pandas as pd
 from easy_tdx import TdxClient
 
 with TdxClient.from_best_host() as c:
-    blocks = c.get_block_info("block_gn.dat")
-    df = pd.DataFrame([{
-        "板块名称": b.name,
-        "分类": b.category,
-        "成分股数": b.count,
-        "代码(前5)": ", ".join(b.codes[:5]),
-    } for b in blocks])
+    df = c.get_block_info("block_gn.dat")
     print(f"概念板块，共 {len(df)} 个:")
-    print(df.head(20).to_string(index=False))
+    print(df[["name", "category", "count"]].head(20).to_string(index=False))

examples/08_fund_flow/fund_flow.py

@@ -4,16 +4,20 @@
 """
 
 import pandas as pd
-from easy_tdx import TdxClient, Market
+from easy_tdx import Market, TdxClient
 
 with TdxClient.from_best_host() as c:
     flow = c.get_fund_flow(Market.SH, "600519")
-    df = pd.DataFrame([
-        {"级别": "超大单", "流入(亿)": flow.super_in / 1e8, "流出(亿)": flow.super_out / 1e8},
-        {"级别": "大单",   "流入(亿)": flow.large_in / 1e8, "流出(亿)": flow.large_out / 1e8},
-        {"级别": "中单",   "流入(亿)": flow.medium_in / 1e8, "流出(亿)": flow.medium_out / 1e8},
-        {"级别": "小单",   "流入(亿)": flow.small_in / 1e8, "流出(亿)": flow.small_out / 1e8},
-    ])
+    # flow 是单行 DataFrame，转换为万元便于阅读
+    in_cols = ["super_in", "large_in", "medium_in", "small_in"]
+    out_cols = ["super_out", "large_out", "medium_out", "small_out"]
+    df = pd.DataFrame(
+        {
+            "级别": ["超大单", "大单", "中单", "小单"],
+            "流入(亿)": [flow[c].iloc[0] / 1e8 for c in in_cols],
+            "流出(亿)": [flow[c].iloc[0] / 1e8 for c in out_cols],
+        }
+    )
     df["净流入(亿)"] = df["流入(亿)"] - df["流出(亿)"]
     print("贵州茅台 当日资金流向:")
     print(df.to_string(index=False))

examples/08_fund_flow/history_fund_flow.py

@@ -1,15 +1,8 @@
 """演示：获取个股历史日线资金流向序列。"""
 
-import pandas as pd
-from easy_tdx import TdxClient, Market
+from easy_tdx import Market, TdxClient
 
 with TdxClient.from_best_host() as c:
-    flows = c.get_history_fund_flow(Market.SH, "600519", 0, 10)
-    df = pd.DataFrame([{
-        "日期": f"{f.year}-{f.month:02d}-{f.day:02d}",
-        "超大单净流入(亿)": (f.super_in - f.super_out) / 1e8,
-        "大单净流入(亿)": (f.large_in - f.large_out) / 1e8,
-        "主力净流入(亿)": f.main_net_inflow / 1e8,
-    } for f in flows])
+    df = c.get_history_fund_flow(Market.SH, "600519", 0, 10)
     print(f"贵州茅台 历史资金流向，共 {len(df)} 天:")
     print(df.to_string(index=False))

examples/09_file_download/report_file.py

@@ -59,8 +59,7 @@ with TdxClient.from_best_host() as c:
     print("行业板块 (block_zs.dat)")
     print("=" * 50)
     blocks = c.get_block_info("block_zs.dat")
-    for b in blocks[:5]:
-        print(f"  {b.name:<10} 分类={b.category}  成分={b.count}")
+    print(blocks[["name", "category", "count"]].head(5).to_string(index=False))
     print(f"  ... 共 {len(blocks)} 个")
 
 # ── 2. 计算服务器：专业财务数据 ────────────────────────
@@ -74,29 +73,25 @@ calc_host = CALC_HOSTS[0]
 with TdxClient(calc_host) as c:
     # 获取文件列表
     file_list = c.get_financial_file_list()
-    for fi in file_list[:5]:
-        print(f"  {fi.filename}  {fi.filesize:>12,} 字节  hash={fi.hash[:8]}...")
+    print(file_list.head(5).to_string(index=False))
     print(f"  ... 共 {len(file_list)} 个文件")
 
     # 下载并解析最近一期有实际数据的财报
-    real_files = [f for f in file_list if f.filesize > 10000]
-    if real_files:
-        latest = real_files[0]
-        fname = f"tdxfin/{latest.filename}"
-        print(f"\n下载: {fname} ({latest.filesize:,} 字节)")
+    real_files = file_list[file_list["filesize"] > 10000]
+    if not real_files.empty:
+        latest = real_files.iloc[0]
+        fname = f"tdxfin/{latest['filename']}"
+        print(f"\n下载: {fname} ({latest['filesize']:,} 字节)")
 
         # 保存原始 .zip
         zip_data = c.get_financial_file(fname)
-        zip_path = OUTPUT_DIR / latest.filename
+        zip_path = OUTPUT_DIR / latest["filename"]
         zip_path.write_bytes(zip_data)
         print(f"  .zip 已保存到 {zip_path}")
 
         # 解析财报记录
         records = c.get_financial_records(fname)
         print(f"  解析出 {len(records)} 只股票")
-        if records:
-            for r in records[:5]:
-                print(f"    {r.market.name} {r.code}  报告期={r.report_date}  字段数={len(r.fields)}")
+        if not records.empty:
+            print(records[["market", "code", "report_date"]].head(5).to_string(index=False))
             print(f"    ... 共 {len(records)} 只")
-            r = records[0]
-            print(f"  示例: {r.market.name} {r.code}, 报告期={r.report_date}, 字段数={len(r.fields)}")

examples/10_offline/block_data.py

@@ -54,8 +54,15 @@ if need_fetch:
     print(f"\n正在连接服务器获取 {len(need_fetch)} 个板块文件...")
     with TdxClient.from_best_host() as c:
         for name in need_fetch:
-            blocks = c.get_block_info(name)
-            _print_blocks(blocks, f"{block_labels[name]} ({name}, 网络)")
+            df = c.get_block_info(name)
+            print(f"\n{block_labels[name]} ({name}, 网络) ({len(df)} 个板块):")
+            for _, row in df.head(5).iterrows():
+                codes = row["codes"]
+                codes_preview = ", ".join(str(c) for c in codes[:5])
+                suffix = "..." if len(codes) > 5 else ""
+                print(f"  {row['name']} ({row['count']}只): {codes_preview}{suffix}")
+            if len(df) > 5:
+                print(f"  ... 还有 {len(df) - 5} 个板块")
 
 # --- 自定义板块 ---
 print(f"\n{'=' * 60}")