DF.__setitem__ creates extension column when given extension scalar

doc/source/whatsnew/v1.0.0.rst

@@ -1259,6 +1259,7 @@ ExtensionArray
 - Bug in :class:`arrays.PandasArray` when setting a scalar string (:issue:`28118`, :issue:`28150`).
 - Bug where nullable integers could not be compared to strings (:issue:`28930`)
 - Bug where :class:`DataFrame` constructor raised ``ValueError`` with list-like data and ``dtype`` specified (:issue:`30280`)
+- Bug where :class:`Series` set to scalar extension type was considered an object type rather than the extension type (:issue:`34832`)
 
 Other
 ^^^^^

pandas/core/dtypes/cast.py

@@ -1505,10 +1505,20 @@ def cast_scalar_to_array(shape, value, dtype: Optional[DtypeObj] = None) -> np.n
 
  """
  if dtype is None:
- dtype, fill_value = infer_dtype_from_scalar(value)
+ dtype, fill_value = infer_dtype_from_scalar(value, pandas_dtype=True)
  else:
  fill_value = value
 
+ # TODO: Update this function to add support for 3rd party extension types
+ # Issue #34959
+ if is_extension_array_dtype(dtype):
+ if isinstance(shape, int):
+ shape = (shape, 1)
+ return [
+ construct_1d_arraylike_from_scalar(value, shape[0], dtype)
+ for _ in range(shape[1])
+ ]
+
  values = np.empty(shape, dtype=dtype)
  values.fill(fill_value)
 

pandas/core/frame.py

@@ -76,6 +76,7 @@
  cast_scalar_to_array,
  coerce_to_dtypes,
  find_common_type,
+ infer_dtype_from_array,
  infer_dtype_from_scalar,
  invalidate_string_dtypes,
  maybe_cast_to_datetime,
@@ -528,9 +529,15 @@ def __init__(
  values = cast_scalar_to_array(
  (len(index), len(columns)), data, dtype=dtype
  )
- mgr = init_ndarray(
- values, index, columns, dtype=values.dtype, copy=False
- )
+ if isinstance(values, list):
+ # Case 1: values is a list of extension arrays
+ dtype, _ = infer_dtype_from_array(values[0], pandas_dtype=True)
+ mgr = arrays_to_mgr(values, columns, index, columns, dtype=dtype)
+ else:
+ # Case 2: values is a numpy array
+ mgr = init_ndarray(
+ values, index, columns, dtype=values.dtype, copy=False
+ )
  else:
  raise ValueError("DataFrame constructor not properly called!")
 
@@ -3731,6 +3738,11 @@ def reindexer(value):
 
  # upcast
  value = cast_scalar_to_array(len(self.index), value)
+
+ # if extension dtype, value will be a list of length 1
+ if isinstance(value, list):
+ value = value[0]
+
  value = maybe_cast_to_datetime(value, infer_dtype)
 
  # return internal types directly

pandas/tests/dtypes/cast/test_infer_dtype.py

@@ -9,6 +9,7 @@
  infer_dtype_from_scalar,
 )
 from pandas.core.dtypes.common import is_dtype_equal
+from pandas.core.dtypes.dtypes import DatetimeTZDtype, IntervalDtype, PeriodDtype
 
 from pandas import (
  Categorical,
@@ -187,14 +188,37 @@ def test_infer_dtype_from_array(arr, expected, pandas_dtype):
  (1.1, np.float64),
  (Timestamp("2011-01-01"), "datetime64[ns]"),
  (Timestamp("2011-01-01", tz="US/Eastern"), object),
- (Period("2011-01-01", freq="D"), object),
  ],
 )
-def test_cast_scalar_to_array(obj, dtype):
+def test_cast_scalar_to_numpy_array(obj, dtype):
  shape = (3, 2)
 
  exp = np.empty(shape, dtype=dtype)
  exp.fill(obj)
 
  arr = cast_scalar_to_array(shape, obj, dtype=dtype)
  tm.assert_numpy_array_equal(arr, exp)
+
+
+@pytest.mark.parametrize(
+ "obj,dtype",
+ [
+ (Period("2011-01-01", freq="D"), PeriodDtype("D")),
+ (Interval(left=0, right=5), IntervalDtype("int64")),
+ (
+ Timestamp("2011-01-01", tz="US/Eastern"),
+ DatetimeTZDtype(unit="ns", tz="US/Eastern"),
+ ),
+ ],
+)
+def test_cast_scalar_to_extension_array(obj, dtype):
+ # GH: 34832
+ shape = 3
+
+ exp = dtype.construct_array_type()._from_sequence([obj] * shape)
+
+ arr = cast_scalar_to_array(shape, obj, dtype=dtype)
+ tm.assert_extension_array_equal(arr[0], exp)
+
+ arr = cast_scalar_to_array(shape, obj, dtype=None)
+ tm.assert_extension_array_equal(arr[0], exp)

pandas/tests/frame/indexing/test_setitem.py

@@ -1,7 +1,18 @@
 import numpy as np
 import pytest
 
-from pandas import Categorical, DataFrame, Index, Series, Timestamp, date_range
+from pandas.core.dtypes.dtypes import IntervalDtype, PeriodDtype
+
+from pandas import (
+ Categorical,
+ DataFrame,
+ Index,
+ Interval,
+ Period,
+ Series,
+ Timestamp,
+ date_range,
+)
 import pandas._testing as tm
 from pandas.core.arrays import SparseArray
 
@@ -150,3 +161,22 @@ def test_setitem_dict_preserves_dtypes(self):
  "c": float(b),
  }
  tm.assert_frame_equal(df, expected)
+
+ def test_setitem_extension_types(self):
+ # GH: 34832
+ period_val = Period("2020-01")
+ interval_val = Interval(left=0, right=5)
+
+ expected = DataFrame(
+ {
+ "idx": [1, 2, 3],
+ "period": Series([period_val] * 3, dtype=PeriodDtype("M")),
+ "interval": Series([interval_val] * 3, dtype=IntervalDtype("int64")),
+ }
+ )
+
+ df = DataFrame({"idx": [1, 2, 3]})
+ df["period"] = period_val
+ df["interval"] = interval_val
+
+ tm.assert_frame_equal(df, expected)

pandas/tests/frame/methods/test_combine_first.py

@@ -199,12 +199,14 @@ def test_combine_first_timezone(self):
  columns=["UTCdatetime", "abc"],
  data=data1,
  index=pd.date_range("20140627", periods=1),
+ dtype="object",
  )
  data2 = pd.to_datetime("20121212 12:12").tz_localize("UTC")
  df2 = pd.DataFrame(
  columns=["UTCdatetime", "xyz"],
  data=data2,
  index=pd.date_range("20140628", periods=1),
+ dtype="object",
  )
  res = df2[["UTCdatetime"]].combine_first(df1)
  exp = pd.DataFrame(
@@ -217,10 +219,13 @@ def test_combine_first_timezone(self):
  },
  columns=["UTCdatetime", "abc"],
  index=pd.date_range("20140627", periods=2, freq="D"),
+ dtype="object",
  )
- tm.assert_frame_equal(res, exp)
  assert res["UTCdatetime"].dtype == "datetime64[ns, UTC]"
  assert res["abc"].dtype == "datetime64[ns, UTC]"
+ # GH Issue 7509
+ res = res.astype("object")
+ tm.assert_frame_equal(res, exp)
 
  # see gh-10567
  dts1 = pd.date_range("2015-01-01", "2015-01-05", tz="UTC")