FIX-#2482: improved handling non-str 'by'

modin/backends/pandas/query_compiler.py

@@ -29,7 +29,7 @@
 
 from modin.backends.base.query_compiler import BaseQueryCompiler
 from modin.error_message import ErrorMessage
-from modin.utils import try_cast_to_pandas, wrap_udf_function
+from modin.utils import try_cast_to_pandas, wrap_udf_function, hashable
 from modin.data_management.functions import (
     Function,
     FoldFunction,
@@ -2555,7 +2555,7 @@ def is_reduce_fn(fn, deep_level=0):
         else:
             if not isinstance(by, list):
                 by = [by]
-            internal_by = [o for o in by if isinstance(o, str) and o in self.columns]
+            internal_by = [o for o in by if hashable(o) and o in self.columns]
             internal_qc = (
                 [self.getitem_column_array(internal_by)] if len(internal_by) else []
             )

modin/data_management/functions/groupby_function.py

@@ -14,7 +14,7 @@
 import pandas
 
 from .mapreducefunction import MapReduceFunction
-from modin.utils import try_cast_to_pandas
+from modin.utils import try_cast_to_pandas, hashable
 
 
 class GroupbyReduceFunction(MapReduceFunction):
@@ -113,7 +113,9 @@ def caller(
         numeric_only=True,
         **kwargs,
     ):
-        if not isinstance(by, (type(query_compiler), str)):
+        if not (isinstance(by, (type(query_compiler)) or hashable(by))) or isinstance(
+            by, pandas.Grouper
+        ):
             by = try_cast_to_pandas(by, squeeze=True)
             default_func = (
                 (lambda grp: grp.agg(map_func))

modin/pandas/dataframe.py

@@ -365,7 +365,7 @@ def groupby(
 
         if callable(by):
             by = self.index.map(by)
-        elif isinstance(by, str):
+        elif hashable(by) and not isinstance(by, pandas.Grouper):
             drop = by in self.columns
             idx_name = by
             if self._query_compiler.has_multiindex(
@@ -374,7 +374,7 @@ def groupby(
                 # In this case we pass the string value of the name through to the
                 # partitions. This is more efficient than broadcasting the values.
                 pass
-            else:
+            elif level is None:
                 by = self.__getitem__(by)._query_compiler
         elif isinstance(by, Series):
             drop = by._parent is self
@@ -384,7 +384,7 @@ def groupby(
             # fastpath for multi column groupby
             if axis == 0 and all(
                 (
-                    (isinstance(o, str) and (o in self))
+                    (hashable(o) and (o in self))
                     or isinstance(o, Series)
                     or (is_list_like(o) and len(o) == len(self.axes[axis]))
                 )
@@ -395,7 +395,7 @@ def groupby(
                 internal_by, external_by = [], []
 
                 for current_by in by:
-                    if isinstance(current_by, str):
+                    if hashable(current_by):
                         internal_by.append(current_by)
                     elif isinstance(current_by, Series):
                         if current_by._parent is self:
@@ -414,7 +414,7 @@ def groupby(
             else:
                 mismatch = len(by) != len(self.axes[axis])
                 if mismatch and all(
-                    isinstance(obj, str)
+                    hashable(obj)
                     and (
                         obj in self or obj in self._query_compiler.get_index_names(axis)
                     )
@@ -424,7 +424,7 @@ def groupby(
                     # we default to pandas in this case.
                     pass
                 elif mismatch and any(
-                    isinstance(obj, str) and obj not in self.columns for obj in by
+                    hashable(obj) and obj not in self.columns for obj in by
                 ):
                     names = [o.name if isinstance(o, Series) else o for o in by]
                     raise KeyError(next(x for x in names if x not in self))

modin/pandas/groupby.py

@@ -32,7 +32,12 @@
 from collections.abc import Iterable
 
 from modin.error_message import ErrorMessage
-from modin.utils import _inherit_docstrings, try_cast_to_pandas, wrap_udf_function
+from modin.utils import (
+    _inherit_docstrings,
+    try_cast_to_pandas,
+    wrap_udf_function,
+    hashable,
+)
 from modin.backends.base.query_compiler import BaseQueryCompiler
 from modin.config import IsExperimental
 from .series import Series
@@ -79,7 +84,7 @@ def __init__(
                 not isinstance(by, type(self._query_compiler))
                 and axis == 0
                 and all(
-                    (isinstance(obj, str) and obj in self._query_compiler.columns)
+                    (hashable(obj) and obj in self._query_compiler.columns)
                     or isinstance(obj, type(self._query_compiler))
                     or is_list_like(obj)
                     for obj in self._by
@@ -324,7 +329,7 @@ def __getitem__(self, key):
         if (
             self._is_multi_by
             and isinstance(self._by, list)
-            and not all(isinstance(o, str) for o in self._by)
+            and not all(hashable(o) and o in self._df for o in self._by)
         ):
             raise NotImplementedError(
                 "Column lookups on GroupBy with arbitrary Series in by"
@@ -809,7 +814,14 @@ def _index_grouped(self):
                 # aware.
                 ErrorMessage.catch_bugs_and_request_email(self._axis == 1)
                 ErrorMessage.default_to_pandas("Groupby with multiple columns")
-                if isinstance(by, list) and all(isinstance(o, str) for o in by):
+                if isinstance(by, list) and all(
+                    hashable(o)
+                    and (
+                        o in self._df
+                        or o in self._df._query_compiler.get_index_names(self._axis)
+                    )
+                    for o in by
+                ):
                     pandas_df = self._df._query_compiler.getitem_column_array(
                         by
                     ).to_pandas()

modin/pandas/test/test_groupby.py

@@ -1494,3 +1494,34 @@ def test_multi_column_groupby_different_partitions(
         by, as_index=as_index
     )
     eval_general(md_grp, pd_grp, func_to_apply)
+
+
+@pytest.mark.parametrize(
+    "by",
+    [
+        0,
+        1.5,
+        "str",
+        pandas.Timestamp("2020-02-02"),
+        [None],
+        [0, "str"],
+        [None, 0],
+        [pandas.Timestamp("2020-02-02"), 1.5],
+    ],
+)
+@pytest.mark.parametrize("as_index", [True, False])
+def test_not_str_by(by, as_index):
+    data = {f"col{i}": np.arange(5) for i in range(5)}
+    columns = pandas.Index([0, 1.5, "str", pandas.Timestamp("2020-02-02"), None])
+
+    md_df, pd_df = create_test_dfs(data, columns=columns)
+    md_grp, pd_grp = md_df.groupby(by, as_index=as_index), pd_df.groupby(
+        by, as_index=as_index
+    )
+
+    modin_groupby_equals_pandas(md_grp, pd_grp)
+    df_equals(md_grp.sum(), pd_grp.sum())
+    df_equals(md_grp.size(), pd_grp.size())
+    df_equals(md_grp.agg(lambda df: df.mean()), pd_grp.agg(lambda df: df.mean()))
+    df_equals(md_grp.dtypes, pd_grp.dtypes)
+    df_equals(md_grp.first(), pd_grp.first())