diff --git a/src/UserGuide/develop/QuickStart/InterfaceDefinition/InterfaceDefinition-Python.md b/src/UserGuide/develop/QuickStart/InterfaceDefinition/InterfaceDefinition-Python.md
index 2ed66d8f5..746e9910e 100644
--- a/src/UserGuide/develop/QuickStart/InterfaceDefinition/InterfaceDefinition-Python.md
+++ b/src/UserGuide/develop/QuickStart/InterfaceDefinition/InterfaceDefinition-Python.md
@@ -354,3 +354,44 @@ def to_dataframe(file_path: str,
            If any specified column does not exist in the table schema.
        """
 ```
+
+### TsFileDataFrame
+TsFileDataFrame is built around three core types:
+
+* **TsFileDataFrame**: The entry object that loads one or more TsFiles and provides a unified view. Only metadata is scanned during initialization; actual data values are **not** read.
+* **Timeseries**: A lazy-loaded handle for a single time series. Obtained via array-style indexing with `df[...]`, it contains series metadata but does not load data immediately – data reading is only triggered when indexed by row number.
+* **AlignedTimeseries**: The time-aligned result of multiple series. Obtained via `df.loc[...]`, it aligns multiple specified series to the same timeline within a given time range and loads them into memory in one operation.
+
+#### TsFileDataFrame
+
+| Example                                      | Operation                              | Return Type       |
+| -------------------------------------------- | -------------------------------------- | ----------------- |
+| `TsFileDataFrame(paths)`                     | Load file(s) / directory               | TsFileDataFrame   |
+| `len(df)`                                    | Get total number of time series        | int               |
+| `df.list_timeseries("weather")`              | Get / filter series names by prefix    | List[str]         |
+| `df["weather.beijing.humidity"], df[0], df[-1]` | Get a single time series          | Timeseries        |
+| `df[0:3], df[[0,2,5]]`                       | Get multiple time series               | List[Timeseries]  |
+| `df.loc[start:end, series_list]`              | Query with timestamp alignment         | AlignedTimeseries |
+
+#### Timeseries
+
+| Example             | Operation               | Return Type |
+| ------------------- | ----------------------- | ----------- |
+| `ts.name`           | Series name             | str         |
+| `len(ts)`           | Number of data points   | int         |
+| `ts.stats`          | Series statistics       | dict        |
+| `ts[20]`            | Read single value       | float       |
+| `ts[20:100]`        | Slice by row range      | np.ndarray  |
+| `ts.timestamps`     | Timestamps array        | np.ndarray  |
+
+#### AlignedTimeseries
+
+| Example                                      | Operation               | Return Type                     |
+| -------------------------------------------- | ----------------------- | ------------------------------- |
+| `data.timestamps`                            | Timestamps array        | `np.ndarray`                    |
+| `data.values`                                | Value matrix            | `np.ndarray, shape=(N, M)`      |
+| `data.series_names`                          | Series names list       | `List[str]`                     |
+| `data.shape`                                 | Shape                   | `(N, M)`                        |
+| `len(data)`                                  | Number of rows          | `int`                           |
+| `data[0]`, `data[0:10]`, `data[0, 1]`        | Row / element indexing  | `np.ndarray` / scalar           |
+| `print(data)`, `data.show(50)`               | Formatted output        | Auto-truncated table            |
\ No newline at end of file
diff --git a/src/UserGuide/develop/QuickStart/QuickStart-PYTHON.md b/src/UserGuide/develop/QuickStart/QuickStart-PYTHON.md
index c29dfba4c..30dd23271 100644
--- a/src/UserGuide/develop/QuickStart/QuickStart-PYTHON.md
+++ b/src/UserGuide/develop/QuickStart/QuickStart-PYTHON.md
@@ -154,6 +154,24 @@ table_data_dir = os.path.join(os.path.dirname(__file__), "table_data.tsfile")
 print(ts.to_dataframe(table_data_dir))
 ```
 
+`TsFileDataFrame` allows you to read time series data from TsFile just like operating a DataFrame, without worrying about the underlying file format and data loading details.
+
+```Python
+from iotdb_ai import TsFileDataFrame
+
+df = TsFileDataFrame("data/")                # Load all TsFiles in the directory
+                                            # Browse all time series
+
+ts = df["weather.Beijing.humidity"]         # Retrieve a single time series
+window = ts[20:100]                         # Slice by row index -> np.ndarray
+
+data = df.loc[start:end, [                  # Align multiple time series by timestamp
+    "weather.Beijing.temperature",
+    "weather.Beijing.humidity",
+]]
+data.values                                  # -> np.ndarray, shape=(N, 2)
+```
+
 ## Sample Code
 
 The sample code of using these interfaces is in：https://github.com/apache/tsfile/blob/develop/python/examples/example.py
diff --git a/src/UserGuide/latest/QuickStart/InterfaceDefinition/InterfaceDefinition-Python.md b/src/UserGuide/latest/QuickStart/InterfaceDefinition/InterfaceDefinition-Python.md
index 849f1c000..f6d07bdc8 100644
--- a/src/UserGuide/latest/QuickStart/InterfaceDefinition/InterfaceDefinition-Python.md
+++ b/src/UserGuide/latest/QuickStart/InterfaceDefinition/InterfaceDefinition-Python.md
@@ -352,4 +352,45 @@ def to_dataframe(file_path: str,
        ColumnNotExistError
            If any specified column does not exist in the table schema.
        """
-```
\ No newline at end of file
+```
+
+### TsFileDataFrame
+TsFileDataFrame is built around three core types:
+
+* **TsFileDataFrame**: The entry object that loads one or more TsFiles and provides a unified view. Only metadata is scanned during initialization; actual data values are **not** read.
+* **Timeseries**: A lazy-loaded handle for a single time series. Obtained via array-style indexing with `df[...]`, it contains series metadata but does not load data immediately – data reading is only triggered when indexed by row number.
+* **AlignedTimeseries**: The time-aligned result of multiple series. Obtained via `df.loc[...]`, it aligns multiple specified series to the same timeline within a given time range and loads them into memory in one operation.
+
+#### TsFileDataFrame
+
+| Example                                      | Operation                              | Return Type       |
+| -------------------------------------------- | -------------------------------------- | ----------------- |
+| `TsFileDataFrame(paths)`                     | Load file(s) / directory               | TsFileDataFrame   |
+| `len(df)`                                    | Get total number of time series        | int               |
+| `df.list_timeseries("weather")`              | Get / filter series names by prefix    | List[str]         |
+| `df["weather.beijing.humidity"], df[0], df[-1]` | Get a single time series          | Timeseries        |
+| `df[0:3], df[[0,2,5]]`                       | Get multiple time series               | List[Timeseries]  |
+| `df.loc[start:end, series_list]`              | Query with timestamp alignment         | AlignedTimeseries |
+
+#### Timeseries
+
+| Example             | Operation               | Return Type |
+| ------------------- | ----------------------- | ----------- |
+| `ts.name`           | Series name             | str         |
+| `len(ts)`           | Number of data points   | int         |
+| `ts.stats`          | Series statistics       | dict        |
+| `ts[20]`            | Read single value       | float       |
+| `ts[20:100]`        | Slice by row range      | np.ndarray  |
+| `ts.timestamps`     | Timestamps array        | np.ndarray  |
+
+#### AlignedTimeseries
+
+| Example                                      | Operation               | Return Type                     |
+| -------------------------------------------- | ----------------------- | ------------------------------- |
+| `data.timestamps`                            | Timestamps array        | `np.ndarray`                    |
+| `data.values`                                | Value matrix            | `np.ndarray, shape=(N, M)`      |
+| `data.series_names`                          | Series names list       | `List[str]`                     |
+| `data.shape`                                 | Shape                   | `(N, M)`                        |
+| `len(data)`                                  | Number of rows          | `int`                           |
+| `data[0]`, `data[0:10]`, `data[0, 1]`        | Row / element indexing  | `np.ndarray` / scalar           |
+| `print(data)`, `data.show(50)`               | Formatted output        | Auto-truncated table            |
\ No newline at end of file
diff --git a/src/UserGuide/latest/QuickStart/QuickStart-PYTHON.md b/src/UserGuide/latest/QuickStart/QuickStart-PYTHON.md
index c29dfba4c..30dd23271 100644
--- a/src/UserGuide/latest/QuickStart/QuickStart-PYTHON.md
+++ b/src/UserGuide/latest/QuickStart/QuickStart-PYTHON.md
@@ -154,6 +154,24 @@ table_data_dir = os.path.join(os.path.dirname(__file__), "table_data.tsfile")
 print(ts.to_dataframe(table_data_dir))
 ```
 
+`TsFileDataFrame` allows you to read time series data from TsFile just like operating a DataFrame, without worrying about the underlying file format and data loading details.
+
+```Python
+from iotdb_ai import TsFileDataFrame
+
+df = TsFileDataFrame("data/")                # Load all TsFiles in the directory
+                                            # Browse all time series
+
+ts = df["weather.Beijing.humidity"]         # Retrieve a single time series
+window = ts[20:100]                         # Slice by row index -> np.ndarray
+
+data = df.loc[start:end, [                  # Align multiple time series by timestamp
+    "weather.Beijing.temperature",
+    "weather.Beijing.humidity",
+]]
+data.values                                  # -> np.ndarray, shape=(N, 2)
+```
+
 ## Sample Code
 
 The sample code of using these interfaces is in：https://github.com/apache/tsfile/blob/develop/python/examples/example.py
diff --git a/src/zh/UserGuide/develop/QuickStart/InterfaceDefinition/InterfaceDefinition-Python.md b/src/zh/UserGuide/develop/QuickStart/InterfaceDefinition/InterfaceDefinition-Python.md
index 60515bff4..102a74555 100644
--- a/src/zh/UserGuide/develop/QuickStart/InterfaceDefinition/InterfaceDefinition-Python.md
+++ b/src/zh/UserGuide/develop/QuickStart/InterfaceDefinition/InterfaceDefinition-Python.md
@@ -265,6 +265,7 @@ class ResultSet:
 
 ```
 
+
 ### to_dataframe
 
 ```Python
@@ -331,6 +332,46 @@ def to_dataframe(file_path: str,
        ColumnNotExistError
            当指定的列在表结构中不存在时抛出。
     """
-
 ```
 
+### TsFileDataFrame
+
+TsFileDataFrame 围绕着三个核心类型：
+
+* **TsFileDataFrame:** 入口对象，加载一至多个 TsFile 并提供统一视图。初始化时只扫描元数据，不读取实际数值。
+* **Timeseries:** 单条时间序列的懒加载句柄。通过 `df[...]`的数组操作获得，包含序列元信息但不立即读取，仅在行号索引时才触发数据读取
+* **AlignedTimeseries:** 多条序列的时间对齐结果。通过`df.loc[...]`获取，一次性将指定时间范围内的多条序列对齐到同一时间轴并读入内存
+
+#### TsFileDataFrame
+
+| 示例                                                | 操作                  | 返回类型          |
+| ----------------------------------------------------- | ----------------------- | ------------------- |
+| `TsFileDataFrame(paths)`                        | 加载文件/目录         | TsFileDataFrame   |
+| `len(df)`                                       | 获取时间序列总数      | int               |
+| `df.list_timeseries("weather")`                 | 获取/按前缀筛选序列名 | List[str]         |
+| `df["weather.beijing.humidity"]，df[0], df[-1]` | 获取单条序列          | Timeseries        |
+| `df[0:3], df[[0,2,5]]`                          | 获取多条序列          | List[Timeseries]  |
+| `df.loc[start:end, serlies_list]`               | 按时间戳对齐查询      | AlignedTimeseries |
+
+#### Timeseries
+
+| 示例                        | 操作         | 返回类型   |
+| ----------------------------- | -------------- | ------------ |
+| `ts.name`               | 序列名       | str        |
+| `len(ts)`               | 序列点数     | int        |
+| `ts.stats`              | 序列统计信息 | dict       |
+| `ts[20]`                | 单值读取     | float      |
+| `ts[20:100]`            | 行范围切片   | np.ndarray |
+| `ts.``timestamps` | 时间戳数组   | np.ndarray |
+
+#### AlignedTimeseries
+
+| 示例                                              | 操作          | 返回类型                       |
+| --------------------------------------------------- | --------------- | -------------------------------- |
+| `data.timestamps`                             | 时间戳数组    | `np.ndarray`               |
+| `data.values`                                 | 值矩阵        | `np.ndarray, shape=(N, M)` |
+| `data.series_names`                           | 序列名列表    | `List[str]`                |
+| `data.shape`                                  | 形状          | `(N, M)`                   |
+| `len(data)`                                   | 行数          | `int`                      |
+| `data[0]`、`data[0:10]`、`data[0, 1]` | 行 / 元素索引 | `np.ndarray`/ scalar       |
+| `print(data)`、`data.show(50)`            | 格式化输出    | 自动截断的表格                 |
diff --git a/src/zh/UserGuide/develop/QuickStart/QuickStart-PYTHON.md b/src/zh/UserGuide/develop/QuickStart/QuickStart-PYTHON.md
index 8b435f37c..1ed7790d0 100644
--- a/src/zh/UserGuide/develop/QuickStart/QuickStart-PYTHON.md
+++ b/src/zh/UserGuide/develop/QuickStart/QuickStart-PYTHON.md
@@ -91,7 +91,7 @@ mvn clean install -P with-python -DskipTests
     mvnw.cmd clean install -P with-python -DskipTests
     ```
 
-* 编译成功后，wheel 文件将位于  `tsfile/python/dist` 目录下, 可通过 pip install 命令进行本地安装（假设他的名字是 tsfile.wheel）
+* 编译成功后，wheel 文件将位于  `tsfile/python/dist` 目录下, 可通过 pip install 命令进行本地安装（假设他的名字是 `tsfile.wheel`）
 
 ```bash
 pip install tsfile.wheel
@@ -158,6 +158,22 @@ table_data_dir = os.path.join(os.path.dirname(__file__), "table_data.tsfile")
 print(ts.to_dataframe(table_data_dir))
 ```
 
+TsFileDataFrame能够让你像操作 DataFrame 一样读取TsFile 中的时序数据，无需关心底层文件格式和数据加载细节。
+
+```Python
+from iotdb_ai import TsFileDataFrame
+
+df = TsFileDataFrame("data/")                # 加载目录下所有 TsFile                                   # 浏览所有序列
+
+ts = df["weather.Beijing.humidity"]           # 取一条序列
+window = ts[20:100]                           # 按行号切片 -> np.ndarray
+
+data = df.loc[start:end, [                    # 按时间戳对齐多条序列"weather.Beijing.humidity",
+    "weather.Beijing.temperature",
+    "weather.Beijing.humidity",
+]]
+data.values                                   # -> np.ndarray, shape=(N, 2)
+```
 
 ## 示例代码
 
diff --git a/src/zh/UserGuide/latest/QuickStart/InterfaceDefinition/InterfaceDefinition-Python.md b/src/zh/UserGuide/latest/QuickStart/InterfaceDefinition/InterfaceDefinition-Python.md
index 08a4b2f6c..102a74555 100644
--- a/src/zh/UserGuide/latest/QuickStart/InterfaceDefinition/InterfaceDefinition-Python.md
+++ b/src/zh/UserGuide/latest/QuickStart/InterfaceDefinition/InterfaceDefinition-Python.md
@@ -332,5 +332,46 @@ def to_dataframe(file_path: str,
        ColumnNotExistError
            当指定的列在表结构中不存在时抛出。
     """
-
 ```
+
+### TsFileDataFrame
+
+TsFileDataFrame 围绕着三个核心类型：
+
+* **TsFileDataFrame:** 入口对象，加载一至多个 TsFile 并提供统一视图。初始化时只扫描元数据，不读取实际数值。
+* **Timeseries:** 单条时间序列的懒加载句柄。通过 `df[...]`的数组操作获得，包含序列元信息但不立即读取，仅在行号索引时才触发数据读取
+* **AlignedTimeseries:** 多条序列的时间对齐结果。通过`df.loc[...]`获取，一次性将指定时间范围内的多条序列对齐到同一时间轴并读入内存
+
+#### TsFileDataFrame
+
+| 示例                                                | 操作                  | 返回类型          |
+| ----------------------------------------------------- | ----------------------- | ------------------- |
+| `TsFileDataFrame(paths)`                        | 加载文件/目录         | TsFileDataFrame   |
+| `len(df)`                                       | 获取时间序列总数      | int               |
+| `df.list_timeseries("weather")`                 | 获取/按前缀筛选序列名 | List[str]         |
+| `df["weather.beijing.humidity"]，df[0], df[-1]` | 获取单条序列          | Timeseries        |
+| `df[0:3], df[[0,2,5]]`                          | 获取多条序列          | List[Timeseries]  |
+| `df.loc[start:end, serlies_list]`               | 按时间戳对齐查询      | AlignedTimeseries |
+
+#### Timeseries
+
+| 示例                        | 操作         | 返回类型   |
+| ----------------------------- | -------------- | ------------ |
+| `ts.name`               | 序列名       | str        |
+| `len(ts)`               | 序列点数     | int        |
+| `ts.stats`              | 序列统计信息 | dict       |
+| `ts[20]`                | 单值读取     | float      |
+| `ts[20:100]`            | 行范围切片   | np.ndarray |
+| `ts.``timestamps` | 时间戳数组   | np.ndarray |
+
+#### AlignedTimeseries
+
+| 示例                                              | 操作          | 返回类型                       |
+| --------------------------------------------------- | --------------- | -------------------------------- |
+| `data.timestamps`                             | 时间戳数组    | `np.ndarray`               |
+| `data.values`                                 | 值矩阵        | `np.ndarray, shape=(N, M)` |
+| `data.series_names`                           | 序列名列表    | `List[str]`                |
+| `data.shape`                                  | 形状          | `(N, M)`                   |
+| `len(data)`                                   | 行数          | `int`                      |
+| `data[0]`、`data[0:10]`、`data[0, 1]` | 行 / 元素索引 | `np.ndarray`/ scalar       |
+| `print(data)`、`data.show(50)`            | 格式化输出    | 自动截断的表格                 |
diff --git a/src/zh/UserGuide/latest/QuickStart/QuickStart-PYTHON.md b/src/zh/UserGuide/latest/QuickStart/QuickStart-PYTHON.md
index 430a324ef..d2a8fe53b 100644
--- a/src/zh/UserGuide/latest/QuickStart/QuickStart-PYTHON.md
+++ b/src/zh/UserGuide/latest/QuickStart/QuickStart-PYTHON.md
@@ -158,6 +158,22 @@ table_data_dir = os.path.join(os.path.dirname(__file__), "table_data.tsfile")
 print(ts.to_dataframe(table_data_dir))
 ```
 
+TsFileDataFrame 能够让你像操作 DataFrame 一样读取 TsFile 中的时序数据，无需关心底层文件格式和数据加载细节。
+
+```Python
+from iotdb_ai import TsFileDataFrame
+
+df = TsFileDataFrame("data/")                # 加载目录下所有 TsFile                                   # 浏览所有序列
+
+ts = df["weather.Beijing.humidity"]           # 取一条序列
+window = ts[20:100]                           # 按行号切片 -> np.ndarray
+
+data = df.loc[start:end, [                    # 按时间戳对齐多条序列"weather.Beijing.humidity",
+    "weather.Beijing.temperature",
+    "weather.Beijing.humidity",
+]]
+data.values                                   # -> np.ndarray, shape=(N, 2)
+```
 
 ## 示例代码