cleanup

daft/dataframe/dataframe.py

@@ -268,7 +268,7 @@ def iter_rows(
         entire partitions of data, see: :meth:`df.iter_partitions() <daft.DataFrame.iter_partitions>`.
 
         By default, Daft will convert the columns to Python lists for easy consumption. However, for nested data such as List or Struct arrays, this can be expensive.
-        You may wish to set `column_format` to "arrow" such that the nested data is returned as an Arrow array.
+        You may wish to set `column_format` to "arrow" such that the nested data is returned as Arrow scalars.
 
         .. NOTE::
             A quick note on configuring asynchronous/parallel execution using `results_buffer_size`.
@@ -296,7 +296,7 @@ def iter_rows(
         Args:
             results_buffer_size: how many partitions to allow in the results buffer (defaults to the total number of CPUs
                 available on the machine).
-            column_format: the format of the columns to iterate over. One of "python", "arrow", or "numpy". Defaults to "python".
+            column_format: the format of the columns to iterate over. One of "python" or "arrow". Defaults to "python".
 
         .. seealso::
             :meth:`df.iter_partitions() <daft.DataFrame.iter_partitions>`: iterator over entire partitions instead of single rows

tests/dataframe/test_iter.py

@@ -101,18 +101,11 @@ def compare_values(v1, v2):
         assert compare_values(actual_row, expected_row)
 
 
-@pytest.mark.parametrize(
-    "format",
-    [
-        "arrow",
-        "numpy",
-    ],
-)
-def test_iter_rows_column_format_not_compatible(format):
+def test_iter_rows_arrow_column_format_not_compatible():
     df = daft.from_pydict({"a": [object()]})  # Object type is not supported by arrow or numpy
 
     with pytest.raises(ValueError):
-        list(df.iter_rows(column_format=format))
+        list(df.iter_rows(column_format="arrow"))
 
 
 @pytest.mark.parametrize("materialized", [False, True])