simplify read_as_pandas

apis/python/src/tiledbsoma/soma_dataframe.py

@@ -12,7 +12,7 @@
 
 # from .query_condition import QueryCondition
 from . import query_condition as qcmodule
-from . import util, util_arrow, util_tiledb
+from . import util, util_arrow
 from .logging import log_io
 from .soma_collection import SOMACollectionBase
 from .tiledb_array import TileDBArray
@@ -216,6 +216,52 @@ def read_all(
             )
         )
 
+    def read_as_pandas(
+        self,
+        *,
+        ids: Optional[Ids] = None,
+        value_filter: Optional[str] = None,
+        column_names: Optional[Sequence[str]] = None,
+        result_order: Optional[SOMAResultOrder] = None,
+        # to rename index to 'obs_id' or 'var_id', if desired, for anndata
+        id_column_name: Optional[str] = None,
+    ) -> Iterator[pd.DataFrame]:
+        """
+        Reads from SOMA storage into memory.  For ``to_anndata``, as well as for any interactive use where the user wants a Pandas dataframe.  Returns a generator over dataframes for batched read. See also ``read_as_pandas_all`` for a convenience wrapper.
+
+        TODO: params-list
+        """
+        for tbl in self.read(
+            ids=ids,
+            value_filter=value_filter,
+            column_names=column_names,
+            result_order=result_order,
+        ):
+            yield tbl.to_pandas()
+
+    def read_as_pandas_all(
+        self,
+        *,
+        ids: Optional[Ids] = None,
+        value_filter: Optional[str] = None,
+        column_names: Optional[Sequence[str]] = None,
+        result_order: Optional[SOMAResultOrder] = None,
+        # to rename index to 'obs_id' or 'var_id', if desired, for anndata
+        id_column_name: Optional[str] = None,
+    ) -> pd.DataFrame:
+        """
+        This is a convenience method around ``read``. It concatenates all partial read results into a single DataFrame. Its nominal use is to simplify unit-test cases.
+        """
+        return pd.concat(
+            self.read_as_pandas(
+                ids=ids,
+                value_filter=value_filter,
+                column_names=column_names,
+                result_order=result_order,
+                id_column_name=id_column_name,
+            )
+        )
+
     def _get_is_sparse(self) -> bool:
         if self._cached_is_sparse is None: