diff --git a/src/nested_pandas/nestedframe/core.py b/src/nested_pandas/nestedframe/core.py
index b3be03e..f0169b5 100644
--- a/src/nested_pandas/nestedframe/core.py
+++ b/src/nested_pandas/nestedframe/core.py
@@ -362,7 +362,8 @@ def reduce(self, func, *args, **kwargs) -> NestedFrame:  # type: ignore[override
         ----------
         func : callable
             Function to apply to each nested dataframe. The first arguments to `func` should be which
-            columns to apply the function to.
+            columns to apply the function to. See the Notes for recommendations
+            on writing func outputs.
         args : positional arguments
             Positional arguments to pass to the function, the first *args should be the names of the
             columns to apply the function to.
@@ -376,22 +377,16 @@ def reduce(self, func, *args, **kwargs) -> NestedFrame:  # type: ignore[override
 
         Notes
         -----
-        The recommend return value of func should be a `pd.Series` where the indices are the names of the
-        output columns in the dataframe returned by `reduce`. Note however that in cases where func
-        returns a single value there may be a performance benefit to returning the scalar value
-        rather than a `pd.Series`.
+        By default, `reduce` will produce a `NestedFrame` with enumerated
+        column names for each returned value of the function. For more useful
+        naming, it's recommended to have `func` return a dictionary where each
+        key is an output column of the dataframe returned by `reduce`.
 
         Example User Function:
-        ```
-        import pandas as pd
-
-        def my_sum(col1, col2):
-            return pd.Series(
-                [sum(col1), sum(col2)],
-                index=["sum_col1", "sum_col2"],
-            )
 
-        ```
+        >>> def my_sum(col1, col2):
+        >>>    '''reduce will return a NestedFrame with two columns'''
+        >>>    return {"sum_col1": sum(col1), "sum_col2": sum(col2)}
 
         """
         # Parse through the initial args to determine the columns to apply the function to