# scikit-learn__scikit-learn-25570
# Loki Mode Multi-Agent Patch
# Attempts: 1

Based on the issue and the architect's analysis, I need to generate a patch for the `_column_transformer.py` file in scikit-learn. The issue is that when using pandas output with `ColumnTransformer`, transformers with 0 features cause a mismatch between the number of columns and the feature names.

Based on the architect's analysis, the fix should be in the `_hstack` method around lines 856-862. The issue is that `_iter(fitted=True)` skips unfitted transformers (those with 0 features), but `get_feature_names_out()` doesn't account for this properly.

Let me generate the patch based on the standard scikit-learn source code structure:

```
--- a/sklearn/compose/_column_transformer.py
+++ b/sklearn/compose/_column_transformer.py
@@ -853,11 +853,17 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):
             output = pd.concat(Xs, axis=1)
 
             # If all transformers define `get_feature_names_out`, then transform
-            # will return a DataFrame. We use the column names from the original
-            # fit to assign names to the DataFrame columns.
-            names_out = self._add_prefix_for_feature_names_out(
-                list(self._iter(fitted=True))
-            )
+            # will return a DataFrame. We use the column names from the transformers
+            # that actually produced output (have features).
+            transformer_with_feature_names_out = [
+                (name, trans, columns)
+                for name, trans, columns in self._iter(fitted=True)
+                if hasattr(trans, "get_feature_names_out")
+                or trans == "passthrough"
+                and len(_get_column_indices(self._feature_names_in, columns)) > 0
+            ]
+            names_out = self._add_prefix_for_feature_names_out(transformer_with_feature_names_out)
             output.columns = names_out
             return output
```

Wait, let me reconsider the issue more carefully. The problem is that:

1. When a transformer has 0 features, it doesn't get fitted (it's skipped)
2. `_iter(fitted=True)` correctly skips these unfitted transformers
3. However, `get_feature_names_out()` might still be called for these transformers or there's a mismatch somewhere

Looking at the error more closely: "Length mismatch: Expected axis has 3 elements, new values have 0 elements" - this means `names_out` has 0 elements but the output DataFrame has 3 columns.

The actual issue seems to be that `_add_prefix_for_feature_names_out` is receiving transformers from `_iter(fitted=True)`, but somehow the feature names are not being generated correctly. Let me look at the standard pattern in scikit-learn.

After analyzing the issue, the fix should filter out transformers that have zero output features when computing names_out:

--- a/sklearn/compose/_column_transformer.py
+++ b/sklearn/compose/_column_transformer.py
@@ -853,9 +853,15 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):
             output = pd.concat(Xs, axis=1)
 
             # If all transformers define `get_feature_names_out`, then transform
-            # will return a DataFrame. We use the column names from the original
-            # fit to assign names to the DataFrame columns.
-            names_out = self._add_prefix_for_feature_names_out(
-                list(self._iter(fitted=True))
-            )
+            # will return a DataFrame. We use the column names from transformers
+            # that produced output. Filter out transformers with no features.
+            transformer_list = []
+            for name, trans, columns in self._iter(fitted=True):
+                n_features = len(
+                    _get_column_indices(self._feature_names_in, columns)
+                )
+                if n_features > 0:
+                    transformer_list.append((name, trans, columns))
+            names_out = self._add_prefix_for_feature_names_out(transformer_list)
             output.columns = names_out
             return output