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참고/instructor-main/docs/examples/pandas_df.md

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+---
+title: Extracting DataFrames from Markdown using Pandas
+description: Learn how to extract and convert Markdown tables directly into Pandas DataFrames in Python.
+---
+
+# Extracting directly to a DataFrame
+
+In this example we'll show you how to extract directly to a `pandas.DataFrame`
+
+```python
+from io import StringIO
+from typing import Annotated, Any
+from pydantic import (
+    BaseModel,
+    BeforeValidator,
+    PlainSerializer,
+    InstanceOf,
+    WithJsonSchema,
+)
+import pandas as pd
+import instructor
+import instructor
+
+
+def md_to_df(data: Any) -> Any:
+    # Convert markdown to DataFrame
+    if isinstance(data, str):
+        return (
+            pd.read_csv(
+                StringIO(data),  # Process data
+                sep="|",
+                index_col=1,
+            )
+            .dropna(axis=1, how="all")
+            .iloc[1:]
+            .applymap(lambda x: x.strip())
+        )
+    return data
+
+
+MarkdownDataFrame = Annotated[
+    # Validates final type
+    InstanceOf[pd.DataFrame],
+    # Converts markdown to DataFrame
+    BeforeValidator(md_to_df),
+    # Converts DataFrame to markdown on model_dump_json
+    PlainSerializer(lambda df: df.to_markdown()),
+    # Adds a description to the type
+    WithJsonSchema(
+        {
+            "type": "string",
+            "description": """
+            The markdown representation of the table,
+            each one should be tidy, do not try to join
+            tables that should be seperate""",
+        }
+    ),
+]
+
+client = instructor.from_provider("openai/gpt-5-nano")
+
+
+def extract_df(data: str) -> pd.DataFrame:
+    return client.create(
+        model="gpt-3.5-turbo",
+        response_model=MarkdownDataFrame,
+        messages=[
+            {
+                "role": "system",
+                "content": "You are a data extraction system, table of writing perfectly formatted markdown tables.",
+            },
+            {
+                "role": "user",
+                "content": f"Extract the data into a table: {data}",
+            },
+        ],
+    )
+
+
+class Table(BaseModel):
+    title: str
+    data: MarkdownDataFrame
+
+
+def extract_table(data: str) -> Table:
+    return client.create(
+        model="gpt-3.5-turbo",
+        response_model=Table,
+        messages=[
+            {
+                "role": "system",
+                "content": "You are a data extraction system, table of writing perfectly formatted markdown tables.",
+            },
+            {
+                "role": "user",
+                "content": f"Extract the data into a table: {data}",
+            },
+        ],
+    )
+
+
+if __name__ == "__main__":
+    df = extract_df(
+        """Create a table of the last 5 presidents of the United States,
+        including their party and the years they served."""
+    )
+    assert isinstance(df, pd.DataFrame)
+    print(df)
+    """
+                         Party          Years Served
+     President
+    Joe Biden                  Democrat  2021 - Present
+    Donald Trump             Republican     2017 - 2021
+    Barack Obama               Democrat     2009 - 2017
+    George W. Bush           Republican     2001 - 2009
+    Bill Clinton               Democrat     1993 - 2001
+    """
+
+    table = extract_table(
+        """Create a table of the last 5 presidents of the United States,
+        including their party and the years they served."""
+    )
+    assert isinstance(table, Table)
+    assert isinstance(table.data, pd.DataFrame)
+    print(table.title)
+    #> Last 5 Presidents of the United States
+    print(table.data)
+    """
+                         Party  Years Served
+     President
+    Joe Biden        Democratic     2021-2025
+    Donald Trump     Republican     2017-2021
+    Barack Obama     Democratic     2009-2017
+    George W. Bush   Republican     2001-2009
+    Bill Clinton     Democratic     1993-2001
+    """
+```
+
+Notice that you can extract both the raw `MarkdownDataFrame` or a more complex structure like `Table` which includes a title and the data as a DataFrame. You can even request `Iterable[Table]` to get multiple tables in a single response!