Repository: modin-project/modin

Issue:
BUG: dictionary aggregation fails if multiple aggregations specified for a single column partition
### Modin version checks

- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the latest released version of Modin.

- [X] I have confirmed this bug exists on the main branch of Modin. (In order to do this you can follow [this guide](https://modin.readthedocs.io/en/stable/getting_started/installation.html#installing-from-the-github-master-branch).)


### Reproducible Example

```python
import modin.pandas as pd

df = pd.DataFrame({"id3": [1, 1, 2, 2], "v1": [1, 2, 3, 4], "v3": [3, 2, 1, 1]})
df.groupby(["id3"]).agg({"v1": "sum", "v3": "mean"})  # fails
# KeyError: "Column(s) [array(['1', '__ID_LEVEL_NAME__', 'v'], dtype=object)] do not exist"
```


### Issue Description

This happens because we don't check if `key` is a string here and spread it into a list of characters unconditionally:

https://github.com/modin-project/modin/blob/672077fa743df4c0bc7a636365a33b556ffd1a3c/modin/core/dataframe/algebra/groupby.py#L520-L524

We only test aggregations performed across multiple partitions, that's why the case wasn't caught by our tests

### Expected Behavior

To not fail...

### Error Logs

<details>

```python-traceback

KeyError: "Column(s) [array(['1', '__ID_LEVEL_NAME__', 'v'], dtype=object)] do not exist"

```

</details>


### Installed Versions

<details>

Replace this line with the output of pd.show_versions()

</details>



Discussion:

Resolving patch:
diff --git a/modin/core/dataframe/algebra/groupby.py b/modin/core/dataframe/algebra/groupby.py
--- a/modin/core/dataframe/algebra/groupby.py
+++ b/modin/core/dataframe/algebra/groupby.py
@@ -518,7 +518,10 @@ def aggregate_on_dict(grp_obj, *args, **kwargs):
             elif grp_has_id_level:
                 # Adding the 'id' level to the aggregation keys so they match `grp_obj` columns
                 native_aggs_modified = {
-                    (cls.ID_LEVEL_NAME, *key): value
+                    (
+                        cls.ID_LEVEL_NAME,
+                        *(key if isinstance(key, tuple) else (key,)),
+                    ): value
                     for key, value in native_aggs.items()
                 }
                 native_agg_res = grp_obj.agg(native_aggs_modified)


Test patch:
diff --git a/modin/pandas/test/test_groupby.py b/modin/pandas/test/test_groupby.py
--- a/modin/pandas/test/test_groupby.py
+++ b/modin/pandas/test/test_groupby.py
@@ -1783,26 +1783,65 @@ def get_columns(df):
                 list(test_data_values[0].keys())[-2]: np.sum,
             }
         ),
-        lambda grp: grp.agg(
-            {
-                list(test_data_values[0].keys())[1]: [
-                    ("new_sum", "sum"),
-                    ("new_mean", "mean"),
-                ],
-                list(test_data_values[0].keys())[-2]: "skew",
-            }
+        pytest.param(
+            lambda grp: grp.agg(
+                {
+                    list(test_data_values[0].keys())[1]: [
+                        ("new_sum", "sum"),
+                        ("new_mean", "mean"),
+                    ],
+                    list(test_data_values[0].keys())[-2]: "skew",
+                }
+            ),
+            id="renaming_aggs_at_different_partitions",
         ),
-        lambda grp: grp.agg(
-            {
-                list(test_data_values[0].keys())[1]: "mean",
-                list(test_data_values[0].keys())[-2]: "skew",
-            }
+        pytest.param(
+            lambda grp: grp.agg(
+                {
+                    list(test_data_values[0].keys())[1]: [
+                        ("new_sum", "sum"),
+                        ("new_mean", "mean"),
+                    ],
+                    list(test_data_values[0].keys())[2]: "skew",
+                }
+            ),
+            id="renaming_aggs_at_same_partition",
         ),
-        lambda grp: grp.agg(
-            {
-                list(test_data_values[0].keys())[1]: "mean",
-                list(test_data_values[0].keys())[-2]: "sum",
-            }
+        pytest.param(
+            lambda grp: grp.agg(
+                {
+                    list(test_data_values[0].keys())[1]: "mean",
+                    list(test_data_values[0].keys())[-2]: "skew",
+                }
+            ),
+            id="custom_aggs_at_different_partitions",
+        ),
+        pytest.param(
+            lambda grp: grp.agg(
+                {
+                    list(test_data_values[0].keys())[1]: "mean",
+                    list(test_data_values[0].keys())[2]: "skew",
+                }
+            ),
+            id="custom_aggs_at_same_partition",
+        ),
+        pytest.param(
+            lambda grp: grp.agg(
+                {
+                    list(test_data_values[0].keys())[1]: "mean",
+                    list(test_data_values[0].keys())[-2]: "sum",
+                }
+            ),
+            id="native_and_custom_aggs_at_different_partitions",
+        ),
+        pytest.param(
+            lambda grp: grp.agg(
+                {
+                    list(test_data_values[0].keys())[1]: "mean",
+                    list(test_data_values[0].keys())[2]: "sum",
+                }
+            ),
+            id="native_and_custom_aggs_at_same_partition",
         ),
         pytest.param(
             lambda grp: grp.agg(