ENH: Add return_dict parameter to DataFrame.info() method to return info as a dictionary #59387

doc/source/whatsnew/v3.0.0.rst

@@ -48,6 +48,7 @@ Other enhancements
 - :meth:`DataFrame.ewm` now allows ``adjust=False`` when ``times`` is provided (:issue:`54328`)
 - :meth:`DataFrame.fillna` and :meth:`Series.fillna` can now accept ``value=None``; for non-object dtype the corresponding NA value will be used (:issue:`57723`)
 - :meth:`DataFrame.pivot_table` and :func:`pivot_table` now allow the passing of keyword arguments to ``aggfunc`` through ``**kwargs`` (:issue:`57884`)
+- :meth:`DataFrame.info` now have a ``return_dict`` parameter (:issue:`#59387`)
 - :meth:`Series.cummin` and :meth:`Series.cummax` now supports :class:`CategoricalDtype` (:issue:`52335`)
 - :meth:`Series.plot` now correctly handle the ``ylabel`` parameter for pie charts, allowing for explicit control over the y-axis label (:issue:`58239`)
 - Multiplying two :class:`DateOffset` objects will now raise a ``TypeError`` instead of a ``RecursionError`` (:issue:`59442`)

pandas/core/frame.py

@@ -3529,17 +3529,21 @@ def info(
         max_cols: int | None = None,
         memory_usage: bool | str | None = None,
         show_counts: bool | None = None,
+        return_dict: bool | None = None,
     ) -> None:
         info = DataFrameInfo(
             data=self,
             memory_usage=memory_usage,
         )
-        info.render(
+        info_return = info.render(
             buf=buf,
             max_cols=max_cols,
             verbose=verbose,
             show_counts=show_counts,
+            return_dict=return_dict,
         )
+        if return_dict:
+            return info_return
 
     def memory_usage(self, index: bool = True, deep: bool = False) -> Series:
         """

pandas/io/formats/info.py

@@ -54,6 +54,15 @@
 )
 
 
+return_dict_sub = dedent(
+    """\
+    return_dict : bool, optional
+        Whether to return the summary as a dictionary. If True, the method
+        returns a dictionary containing information about the DataFrame.
+        If False, the summary is printed and None is returned."""
+)
+
+
 frame_examples_sub = dedent(
     """\
     >>> int_values = [1, 2, 3, 4, 5]
@@ -136,7 +145,12 @@
      1   column_2  1000000 non-null  object
      2   column_3  1000000 non-null  object
     dtypes: object(3)
-    memory usage: 165.9 MB"""
+    memory usage: 165.9 MB
+
+    >>> info_dict = df.info(return_dict=True)
+    >>> print(info_dict)
+    {'Column_summary': '...', 'Memory_usage': 24000128,
+    'Index_type': 'RangeIndex', 'Index_entries': 1000000}"""
 )
 
 
@@ -153,6 +167,7 @@
     "type_sub": " and columns",
     "max_cols_sub": frame_max_cols_sub,
     "show_counts_sub": show_counts_sub,
+    "return_dict_sub": return_dict_sub,
     "examples_sub": frame_examples_sub,
     "see_also_sub": frame_see_also_sub,
     "version_added_sub": "",
@@ -233,6 +248,7 @@
     "type_sub": "",
     "max_cols_sub": "",
     "show_counts_sub": show_counts_sub,
+    "return_dict_sub": return_dict_sub,
     "examples_sub": series_examples_sub,
     "see_also_sub": series_see_also_sub,
     "version_added_sub": "\n.. versionadded:: 1.4.0\n",
@@ -273,11 +289,13 @@
         :ref:`Frequently Asked Questions <df-memory-usage>` for more
         details.
     {show_counts_sub}
+    {return_dict_sub}
 
     Returns
     -------
-    None
-        This method prints a summary of a {klass} and returns None.
+    dict or None
+        If return_dict is True, returns a dictionary summarizing the {klass}.
+        Otherwise, returns None.
 
     See Also
     --------
@@ -435,7 +453,7 @@ def render(
         max_cols: int | None,
         verbose: bool | None,
         show_counts: bool | None,
-    ) -> None:
+    ) -> None | dict:
         pass
 
 
@@ -495,21 +513,46 @@ def memory_usage_bytes(self) -> int:
         deep = self.memory_usage == "deep"
         return self.data.memory_usage(index=True, deep=deep).sum()
 
+    def to_dict(self) -> dict:
+        """Return DataFrame info as a dictionary."""
+        return {
+            "Column_summary": self._get_column_summary(),
+            "Memory_usage": self.memory_usage_bytes,
+            "Index_type": type(self.data.index).__name__,
+            "Index_entries": len(self.data.index),
+        }
+
+    def _get_column_summary(self) -> list[dict]:
+        """Return a DataFrame summarizing columns."""
+        return [
+            {
+                "#": i,
+                "Column": col,
+                "Non-Null-Count": self.data[col].notna().sum(),
+                "Dtype": self.data[col].dtype,
+            }
+            for i, col in enumerate(self.ids)
+        ]
+
     def render(
         self,
         *,
         buf: WriteBuffer[str] | None,
         max_cols: int | None,
         verbose: bool | None,
         show_counts: bool | None,
-    ) -> None:
-        printer = _DataFrameInfoPrinter(
-            info=self,
-            max_cols=max_cols,
-            verbose=verbose,
-            show_counts=show_counts,
-        )
-        printer.to_buffer(buf)
+        return_dict: bool | None,
+    ) -> None | dict:
+        if return_dict:
+            return self.to_dict()
+        else:
+            printer = _DataFrameInfoPrinter(
+                info=self,
+                max_cols=max_cols,
+                verbose=verbose,
+                show_counts=show_counts,
+            )
+            printer.to_buffer(buf)
 
 
 class SeriesInfo(_BaseInfo):

pandas/tests/frame/methods/test_info.py

@@ -569,3 +569,27 @@ def test_info_show_counts(row, columns, show_counts, result):
         with StringIO() as buf:
             df.info(buf=buf, show_counts=show_counts)
             assert ("non-null" in buf.getvalue()) is result
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}),
+        DataFrame({}),
+    ],
+)
+def test_info_return_dict(df):
+    result = df.info(return_dict=True)
+    expected_keys = {"Column_summary", "Memory_usage", "Index_type", "Index_entries"}
+    assert isinstance(result, dict)
+    assert expected_keys.issubset(result.keys())
+
+    assert "Column_summary" in result
+    assert "Memory_usage" in result
+    assert "Index_type" in result
+    assert "Index_entries" in result
+
+    assert isinstance(result["Column_summary"], list)
+    assert isinstance(result["Memory_usage"], np.int64)
+    assert isinstance(result["Index_type"], str)
+    assert isinstance(result["Index_entries"], int)