DEPR: coercing bools to numeric on concat with numeric dtypes

doc/source/whatsnew/v1.4.0.rst

@@ -606,6 +606,7 @@ Other Deprecations
 - Deprecated :meth:`DateOffset.apply`, use ``offset + other`` instead (:issue:`44522`)
 - Deprecated parameter ``names`` in :meth:`Index.copy` (:issue:`44916`)
 - A deprecation warning is now shown for :meth:`DataFrame.to_latex` indicating the arguments signature may change and emulate more the arguments to :meth:`.Styler.to_latex` in future versions (:issue:`44411`)
+- Deprecated behavior of :func:`concat` between objects with bool-dtype and numeric-dtypes; in a future version these will cast to object dtype instead of coercing bools to numeric values (:issue:`39817`)
 - Deprecated :meth:`Categorical.replace`, use :meth:`Series.replace` instead (:issue:`44929`)
 - Deprecated :meth:`Index.__getitem__` with a bool key; use ``index.values[key]`` to get the old behavior (:issue:`44051`)
 - Deprecated downcasting column-by-column in :meth:`DataFrame.where` with integer-dtypes (:issue:`44597`)

pandas/core/dtypes/concat.py

@@ -5,13 +5,15 @@
     TYPE_CHECKING,
     cast,
 )
+import warnings
 
 import numpy as np
 
 from pandas._typing import (
     ArrayLike,
     DtypeObj,
 )
+from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.cast import (
     astype_array,
@@ -144,8 +146,20 @@ def is_nonempty(x) -> bool:
             else:
                 # coerce to object
                 to_concat = [x.astype("object") for x in to_concat]
-
-    return np.concatenate(to_concat, axis=axis)
+                kinds = {"o"}
+
+    result = np.concatenate(to_concat, axis=axis)
+    if "b" in kinds and result.dtype.kind in ["i", "u", "f"]:
+        # GH#39817
+        warnings.warn(
+            "Behavior when concatenating bool-dtype and numeric-dtype arrays is "
+            "deprecated; in a future version these will cast to object dtype "
+            "(instead of coercing bools to numeric values). To retain the old "
+            "behavior, explicitly cast bool-dtype arrays to numeric dtype.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+    return result
 
 
 def union_categoricals(

pandas/tests/reshape/concat/test_append_common.py

@@ -204,13 +204,16 @@ def test_concatlike_dtypes_coercion(self, item, item2):
             #  instead of a list; we have separate dedicated tests for categorical
             return
 
+        warn = None
         # specify expected dtype
         if typ1 == "bool" and typ2 in ("int64", "float64"):
             # series coerces to numeric based on numpy rule
             # index doesn't because bool is object dtype
             exp_series_dtype = typ2
+            warn = FutureWarning
         elif typ2 == "bool" and typ1 in ("int64", "float64"):
             exp_series_dtype = typ1
+            warn = FutureWarning
         elif (
             typ1 == "datetime64[ns, US/Eastern]"
             or typ2 == "datetime64[ns, US/Eastern]"
@@ -238,23 +241,33 @@ def test_concatlike_dtypes_coercion(self, item, item2):
         # ----- Series ----- #
 
         # series._append
-        res = Series(vals1)._append(Series(vals2), ignore_index=True)
+        with tm.assert_produces_warning(warn, match="concatenating bool-dtype"):
+            # GH#39817
+            res = Series(vals1)._append(Series(vals2), ignore_index=True)
         exp = Series(exp_data, dtype=exp_series_dtype)
         tm.assert_series_equal(res, exp, check_index_type=True)
 
         # concat
-        res = pd.concat([Series(vals1), Series(vals2)], ignore_index=True)
+        with tm.assert_produces_warning(warn, match="concatenating bool-dtype"):
+            # GH#39817
+            res = pd.concat([Series(vals1), Series(vals2)], ignore_index=True)
         tm.assert_series_equal(res, exp, check_index_type=True)
 
         # 3 elements
-        res = Series(vals1)._append([Series(vals2), Series(vals3)], ignore_index=True)
+        with tm.assert_produces_warning(warn, match="concatenating bool-dtype"):
+            # GH#39817
+            res = Series(vals1)._append(
+                [Series(vals2), Series(vals3)], ignore_index=True
+            )
         exp = Series(exp_data3, dtype=exp_series_dtype)
         tm.assert_series_equal(res, exp)
 
-        res = pd.concat(
-            [Series(vals1), Series(vals2), Series(vals3)],
-            ignore_index=True,
-        )
+        with tm.assert_produces_warning(warn, match="concatenating bool-dtype"):
+            # GH#39817
+            res = pd.concat(
+                [Series(vals1), Series(vals2), Series(vals3)],
+                ignore_index=True,
+            )
         tm.assert_series_equal(res, exp)
 
     def test_concatlike_common_coerce_to_pandas_object(self):

pandas/tests/reshape/concat/test_empty.py

@@ -109,7 +109,12 @@ def test_concat_empty_series_timelike(self, tz, values):
         ],
     )
     def test_concat_empty_series_dtypes(self, left, right, expected):
-        result = concat([Series(dtype=left), Series(dtype=right)])
+        warn = None
+        if (left is np.bool_ or right is np.bool_) and expected is not np.object_:
+            warn = FutureWarning
+        with tm.assert_produces_warning(warn, match="concatenating bool-dtype"):
+            # GH#39817
+            result = concat([Series(dtype=left), Series(dtype=right)])
         assert result.dtype == expected
 
     @pytest.mark.parametrize(