diff --git a/src/tape/ensemble.py b/src/tape/ensemble.py
index 406a5881..35eb7ead 100644
--- a/src/tape/ensemble.py
+++ b/src/tape/ensemble.py
@@ -607,41 +607,68 @@ def calc_nobs(self, by_band=False, label="nobs", temporary=True):
         """
 
         if by_band:
-            band_counts = (
-                self._source.groupby([self._id_col])[self._band_col]  # group by each object
-                .value_counts()  # count occurence of each band
-                .to_frame()  # convert series to dataframe
-                .rename(columns={self._band_col: "counts"})  # rename column
-                .reset_index()  # break up the multiindex
-                .categorize(columns=[self._band_col])  # retype the band labels as categories
-                .pivot_table(values=self._band_col, index=self._id_col, columns=self._band_col, aggfunc="sum")
-            )  # the pivot_table call makes each band_count a column of the id_col row
-
             # repartition the result to align with object
             if self._object.known_divisions:
-                band_counts.divisions = self._source.divisions
-                band_counts = band_counts.repartition(divisions=self._object.divisions)
+                # Grab these up front to help out the task graph
+                id_col = self._id_col
+                band_col = self._band_col
+
+                # Get the band metadata
+                unq_bands = np.unique(self._source[band_col])
+                meta = {band: float for band in unq_bands}
+
+                # Map the groupby to each partition
+                band_counts = self._source.map_partitions(
+                    lambda x: x.groupby(id_col)[[band_col]]
+                    .value_counts()
+                    .to_frame()
+                    .reset_index()
+                    .pivot_table(values=band_col, index=id_col, columns=band_col, aggfunc="sum"),
+                    meta=meta,
+                ).repartition(divisions=self._object.divisions)
             else:
+                band_counts = (
+                    self._source.groupby([self._id_col])[self._band_col]  # group by each object
+                    .value_counts()  # count occurence of each band
+                    .to_frame()  # convert series to dataframe
+                    .rename(columns={self._band_col: "counts"})  # rename column
+                    .reset_index()  # break up the multiindex
+                    .categorize(columns=[self._band_col])  # retype the band labels as categories
+                    .pivot_table(
+                        values=self._band_col, index=self._id_col, columns=self._band_col, aggfunc="sum"
+                    )
+                )  # the pivot_table call makes each band_count a column of the id_col row
+
                 band_counts = band_counts.repartition(npartitions=self._object.npartitions)
 
             # short-hand for calculating nobs_total
             band_counts["total"] = band_counts[list(band_counts.columns)].sum(axis=1)
 
             bands = band_counts.columns.values
-            self._object = self._object.assign(**{label + "_" + band: band_counts[band] for band in bands})
+            self._object = self._object.assign(
+                **{label + "_" + str(band): band_counts[band] for band in bands}
+            )
 
             if temporary:
-                self._object_temp.extend(label + "_" + band for band in bands)
+                self._object_temp.extend(label + "_" + str(band) for band in bands)
 
         else:
-            counts = self._source.groupby([self._id_col])[[self._band_col]].aggregate("count")
-
-            # repartition the result to align with object
             if self._object.known_divisions and self._source.known_divisions:
-                counts.divisions = self._source.divisions
-                counts = counts.repartition(divisions=self._object.divisions)
+                # Grab these up front to help out the task graph
+                id_col = self._id_col
+                band_col = self._band_col
+
+                # Map the groupby to each partition
+                counts = self._source.map_partitions(
+                    lambda x: x.groupby([id_col])[[band_col]].aggregate("count")
+                ).repartition(divisions=self._object.divisions)
             else:
-                counts = counts.repartition(npartitions=self._object.npartitions)
+                # Just do a groupby on all source
+                counts = (
+                    self._source.groupby([self._id_col])[[self._band_col]]
+                    .aggregate("count")
+                    .repartition(npartitions=self._object.npartitions)
+                )
 
             self._object = self._object.assign(**{label + "_total": counts[self._band_col]})
 
diff --git a/tests/tape_tests/test_ensemble.py b/tests/tape_tests/test_ensemble.py
index 9fbf1d53..f4bb7df2 100644
--- a/tests/tape_tests/test_ensemble.py
+++ b/tests/tape_tests/test_ensemble.py
@@ -823,10 +823,14 @@ def test_keep_zeros(parquet_ensemble):
     ],
 )
 @pytest.mark.parametrize("by_band", [True, False])
-def test_calc_nobs(data_fixture, request, by_band):
+@pytest.mark.parametrize("multi_partition", [True, False])
+def test_calc_nobs(data_fixture, request, by_band, multi_partition):
     # Get the Ensemble from a fixture
     ens = request.getfixturevalue(data_fixture)
 
+    if multi_partition:
+        ens._source = ens._source.repartition(3)
+
     # Drop the existing nobs columns
     ens._object = ens._object.drop(["nobs_g", "nobs_r", "nobs_total"], axis=1)