BUG: fix tzaware dataframe transpose bug

doc/source/whatsnew/v0.25.0.rst

@@ -728,6 +728,7 @@ Reshaping
 - Bug in :func:`DataFrame.sort_index` where an error is thrown when a multi-indexed ``DataFrame`` is sorted on all levels with the initial level sorted last (:issue:`26053`)
 - Bug in :meth:`Series.nlargest` treats ``True`` as smaller than ``False`` (:issue:`26154`)
 - Bug in :func:`DataFrame.pivot_table` with a :class:`IntervalIndex` as pivot index would raise ``TypeError`` (:issue:`25814`)
+- Bug in :meth:`DataFrame.transpose` where transposing a DataFrame with a timezone-aware datetime column would incorrectly raise ``ValueError`` (:issue:`26825`)
 
 Sparse
 ^^^^^^
@@ -743,6 +744,7 @@ Other
 - Removed unused C functions from vendored UltraJSON implementation (:issue:`26198`)
 - Bug in :func:`factorize` when passing an ``ExtensionArray`` with a custom ``na_sentinel`` (:issue:`25696`).
 - Allow :class:`Index` and :class:`RangeIndex` to be passed to numpy ``min`` and ``max`` functions (:issue:`26125`)
+- Bug in :class:`DataFrame` where passing an object array of timezone-aware `datetime` objects would incorrectly raise ``ValueError`` (:issue:`13287`)
 
 .. _whatsnew_0.250.contributors:
 

pandas/core/groupby/generic.py

@@ -24,7 +24,8 @@
 from pandas.core.dtypes.cast import maybe_downcast_to_dtype
 from pandas.core.dtypes.common import (
     ensure_int64, ensure_platform_int, is_bool, is_datetimelike,
-    is_integer_dtype, is_interval_dtype, is_numeric_dtype, is_scalar)
+    is_integer_dtype, is_interval_dtype, is_numeric_dtype, is_object_dtype,
+    is_scalar)
 from pandas.core.dtypes.missing import isna, notna
 
 from pandas._typing import FrameOrSeries
@@ -334,7 +335,6 @@ def _decide_output_index(self, output, labels):
 
     def _wrap_applied_output(self, keys, values, not_indexed_same=False):
         from pandas.core.index import _all_indexes_same
-        from pandas.core.tools.numeric import to_numeric
 
         if len(keys) == 0:
             return DataFrame(index=keys)
@@ -406,7 +406,6 @@ def first_not_none(values):
                     # provide a reduction (Frame -> Series) if groups are
                     # unique
                     if self.squeeze:
-
                         # assign the name to this series
                         if singular_series:
                             values[0].name = keys[0]
@@ -480,15 +479,8 @@ def first_not_none(values):
                 # if we have date/time like in the original, then coerce dates
                 # as we are stacking can easily have object dtypes here
                 so = self._selected_obj
-                if (so.ndim == 2 and so.dtypes.apply(is_datetimelike).any()):
-                    result = result.apply(
-                        lambda x: to_numeric(x, errors='ignore'))
-                    date_cols = self._selected_obj.select_dtypes(
-                        include=['datetime', 'timedelta']).columns
-                    date_cols = date_cols.intersection(result.columns)
-                    result[date_cols] = (result[date_cols]
-                                         ._convert(datetime=True,
-                                                   coerce=True))
+                if so.ndim == 2 and so.dtypes.apply(is_datetimelike).any():
+                    result = _recast_datetimelike_result(result)
                 else:
                     result = result._convert(datetime=True)
 
@@ -1710,3 +1702,43 @@ def _normalize_keyword_aggregation(kwargs):
         order.append((column,
                       com.get_callable_name(aggfunc) or aggfunc))
     return aggspec, columns, order
+
+
+def _recast_datetimelike_result(result: DataFrame) -> DataFrame:
+    """
+    If we have date/time like in the original, then coerce dates
+    as we are stacking can easily have object dtypes here.
+
+    Parameters
+    ----------
+    result : DataFrame
+
+    Returns
+    -------
+    DataFrame
+
+    Notes
+    -----
+    - Assumes Groupby._selected_obj has ndim==2 and at least one
+    datetimelike column
+    """
+    result = result.copy()
+
+    ocols = [idx for idx in range(len(result.columns))
+             if is_object_dtype(result.dtypes[idx])]
+
+    for cidx in ocols:
+        # TODO: get maybe_convert_objects working here
+        cvals = result.iloc[:, cidx]
+
+        # TODO: should we use skipna=True?
+        exdtype = lib.infer_dtype(cvals.values, skipna=False)
+        if exdtype == 'integer':
+            result.iloc[:, cidx] = cvals.astype(np.int64)
+        if exdtype in ['float', 'mixed-integer-float']:
+            result.iloc[:, cidx] = cvals.astype(np.float64)
+        if exdtype == 'datetime':
+            # TODO: what about tz-aware?
+            result.iloc[:, cidx] = cvals.astype('M8[ns]')
+
+    return result

pandas/core/internals/construction.py

@@ -159,7 +159,29 @@ def init_ndarray(values, index, columns, dtype=None, copy=False):
     # on the entire block; this is to convert if we have datetimelike's
     # embedded in an object type
     if dtype is None and is_object_dtype(values):
-        values = maybe_infer_to_datetimelike(values)
+
+        if values.ndim == 2 and values.shape[0] != 1:
+            # kludge to transpose and separate blocks
+            # unnecessary if we ever allow 2D DatetimeArray
+
+            dvals_list = [maybe_infer_to_datetimelike(values[n, :])
+                          for n in range(len(values))]
+            for n in range(len(dvals_list)):
+                if isinstance(dvals_list[n], np.ndarray):
+                    dvals_list[n] = dvals_list[n].reshape(1, -1)
+
+            from pandas.core.internals.blocks import make_block
+
+            # TODO: What about re-joining object columns?
+            bdvals = [make_block(dvals_list[n], placement=[n])
+                      for n in range(len(dvals_list))]
+            return create_block_manager_from_blocks(bdvals,
+                                                    [columns, index])
+
+        else:
+            dvals = maybe_infer_to_datetimelike(values)
+
+        values = dvals
 
     return create_block_manager_from_blocks([values], [columns, index])