BUG: Fix DataFrame.from_dict empty row drop

doc/source/whatsnew/v3.0.0.rst

@@ -1179,6 +1179,7 @@ Reshaping
 - Bug in :func:`qcut` where values at the quantile boundaries could be incorrectly assigned (:issue:`59355`)
 - Bug in :meth:`DataFrame.combine_first` not preserving the column order (:issue:`60427`)
 - Bug in :meth:`DataFrame.explode` producing incorrect result for :class:`pyarrow.large_list` type (:issue:`61091`)
+- Bug in :meth:`DataFrame.from_dict` where rows corresponding to an empty :class:`Series` or ``dict`` would be dropped when ``orient='index'`` (:issue:`62775`)
 - Bug in :meth:`DataFrame.join` inconsistently setting result index name (:issue:`55815`)
 - Bug in :meth:`DataFrame.join` when a :class:`DataFrame` with a :class:`MultiIndex` would raise an ``AssertionError`` when :attr:`MultiIndex.names` contained ``None``. (:issue:`58721`)
 - Bug in :meth:`DataFrame.merge` where merging on a column containing only ``NaN`` values resulted in an out-of-bounds array access (:issue:`59421`)

pandas/core/frame.py

@@ -1914,11 +1914,13 @@ def from_dict(
  orient = orient.lower() # type: ignore[assignment]
  if orient == "index":
  if len(data) > 0:
+ index = list(data.keys())
  # TODO speed up Series case
  if isinstance(next(iter(data.values())), (Series, dict)):
  data = _from_nested_dict(data)
+ if not data and columns is None:
+ columns = []
  else:
- index = list(data.keys())
  # error: Incompatible types in assignment (expression has type
  # "List[Any]", variable has type "Dict[Any, Any]")
  data = list(data.values()) # type: ignore[assignment]
@@ -14413,9 +14415,22 @@ def _from_nested_dict(
  new_data: collections.defaultdict[HashableT2, dict[HashableT, T]] = (
  collections.defaultdict(dict)
  )
+ all_cols_dict = {}
+ for s in data.values():
+ if isinstance(s, (dict, ABCSeries)):
+ all_cols_dict.update(dict.fromkeys(s.keys()))
+ all_cols_list = list(all_cols_dict.keys())
+ if not all_cols_list:
+ return new_data
  for index, s in data.items():
- for col, v in s.items():
- new_data[col][index] = v
+ if isinstance(s, (dict, ABCSeries)):
+ for col in all_cols_list:
+ new_data[col][index] = s.get(col, None)
+ elif s is None or is_scalar(s):
+ for col in all_cols_list:
+ new_data[col][index] = s
+ else:
+ raise TypeError(f"Value at index {index} is not a dict/Series/scalar/None")
  return new_data
 
 

pandas/tests/frame/test_constructors.py

@@ -2649,6 +2649,48 @@ def test_error_from_2darray(self, col_a, col_b):
  with pytest.raises(ValueError, match=msg):
  DataFrame({"a": col_a, "b": col_b})
 
+ @pytest.mark.parametrize(
+ "data, expected",
+ [
+ (
+ {
+ "good": Series({"a": 1, "b": 2}),
+ "blank": Series(dtype="float64"),
+ },
+ DataFrame(
+ {"a": [1.0, np.nan], "b": [2.0, np.nan]}, index=["good", "blank"]
+ ),
+ ),
+ (
+ {
+ "blank": Series(dtype="float64"),
+ "good": Series({"a": 1, "b": 2}),
+ },
+ DataFrame(
+ {"a": [np.nan, 1.0], "b": [np.nan, 2.0]}, index=["blank", "good"]
+ ),
+ ),
+ (
+ {"blank": Series(dtype="float64")},
+ DataFrame(index=["blank"], columns=[]),
+ ),
+ (
+ {
+ "good": Series({"a": 1, "b": 2}),
+ "blank_dict": {},
+ },
+ DataFrame(
+ {"a": [1.0, np.nan], "b": [2.0, np.nan]},
+ index=["good", "blank_dict"],
+ ),
+ ),
+ ],
+ )
+ def test_from_dict_orient_index_empty_series_or_dict(self, data, expected):
+ # GH-62775
+ result = DataFrame.from_dict(data, orient="index")
+ tm.assert_frame_equal(result, expected)
+
  def test_from_dict_with_missing_copy_false(self):
  # GH#45369 filled columns should not be views of one another
  df = DataFrame(index=[1, 2, 3], columns=["a", "b", "c"], copy=False)