# Buggy code
def __init__(
    self,
    data=None,
    index: Optional[Axes] = None,
    columns: Optional[Axes] = None,
    dtype: Optional[Dtype] = None,
    copy: bool = False,
):
    if data is None:
        data = {}
    if dtype is not None:
        dtype = self._validate_dtype(dtype)

    if isinstance(data, DataFrame):
        data = data._mgr

    if isinstance(data, BlockManager):
        if index is None and columns is None and dtype is None and copy is False:
            # GH#33357 fastpath
            NDFrame.__init__(self, data)
            return

        mgr = self._init_mgr(
            data, axes={"index": index, "columns": columns}, dtype=dtype, copy=copy
        )

    elif isinstance(data, dict):
        mgr = init_dict(data, index, columns, dtype=dtype)
    elif isinstance(data, ma.MaskedArray):
        import numpy.ma.mrecords as mrecords

        # masked recarray
        if isinstance(data, mrecords.MaskedRecords):
            mgr = masked_rec_array_to_mgr(data, index, columns, dtype, copy)

        # a masked array
        else:
            data = sanitize_masked_array(data)
            mgr = init_ndarray(data, index, columns, dtype=dtype, copy=copy)

    elif isinstance(data, (np.ndarray, Series, Index)):
        if data.dtype.names:
            data_columns = list(data.dtype.names)
            data = {k: data[k] for k in data_columns}
            if columns is None:
                columns = data_columns
            mgr = init_dict(data, index, columns, dtype=dtype)
        elif getattr(data, "name", None) is not None:
            mgr = init_dict({data.name: data}, index, columns, dtype=dtype)
        else:
            mgr = init_ndarray(data, index, columns, dtype=dtype, copy=copy)

    # For data is list-like, or Iterable (will consume into list)
    elif isinstance(data, abc.Iterable) and not isinstance(data, (str, bytes)):
        if not isinstance(data, (abc.Sequence, ExtensionArray)):
            data = list(data)
        if len(data) > 0:
            if is_dataclass(data[0]):
                data = dataclasses_to_dicts(data)
            if is_list_like(data[0]) and getattr(data[0], "ndim", 1) == 1:
                if is_named_tuple(data[0]) and columns is None:
                    columns = data[0]._fields
                arrays, columns = to_arrays(data, columns, dtype=dtype)
                columns = ensure_index(columns)

                # set the index
                if index is None:
                    if isinstance(data[0], Series):
                        index = get_names_from_index(data)
                    elif isinstance(data[0], Categorical):
                        index = ibase.default_index(len(data[0]))
                    else:
                        index = ibase.default_index(len(data))

                mgr = arrays_to_mgr(arrays, columns, index, columns, dtype=dtype)
            else:
                mgr = init_ndarray(data, index, columns, dtype=dtype, copy=copy)
        else:
            mgr = init_dict({}, index, columns, dtype=dtype)
    # For data is scalar
    else:
        if index is None or columns is None:
            raise ValueError("DataFrame constructor not properly called!")

        if not dtype:
            dtype, _ = infer_dtype_from_scalar(data, pandas_dtype=True)

        # For data is a scalar extension dtype
        if is_extension_array_dtype(dtype):
            values = [
                construct_1d_arraylike_from_scalar(data, len(index), dtype)
                for _ in range(len(columns))
            ]
            mgr = arrays_to_mgr(values, columns, index, columns, dtype=None)
        else:
            # Attempt to coerce to a numpy array
            try:
                arr = np.array(data, dtype=dtype, copy=copy)
            except (ValueError, TypeError) as err:
                exc = TypeError(
                    "DataFrame constructor called with "
                    f"incompatible data and dtype: {err}"
                )
                raise exc from err

            if arr.ndim != 0:
                raise ValueError("DataFrame constructor not properly called!")

            shape = (len(index), len(columns))
            values = np.full(shape, arr)

            mgr = init_ndarray(values, index, columns, dtype=values.dtype, copy=False)

    NDFrame.__init__(self, mgr)


# Traceback (AssertionError)
In [31]: import pandas as pd
...: import pandas._testing as tm
...:
...: td = pd.Timedelta(nanoseconds=500)
...: ser = pd.Series({"a": td})
...: expected = pd.Series(td, index=["a"], dtype="timedelta64[ns]")
...:
...: tm.assert_series_equal(ser, expected)
---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-31-a9c6a6312101> in <module>
6 expected = pd.Series(td, index=["a"], dtype="timedelta64[ns]")
7
----> 8 tm.assert_series_equal(ser, expected)

[... skipping hidden 1 frame]

~/repos/pandas/pandas/_testing.py in assert_extension_array_equal(left, right, check_dtype, index_values, check_less_precise, check_exact, rtol, atol)
1243         # Avoid slow object-dtype comparisons
1244         # np.asarray for case where we have a np.MaskedArray
-> 1245         assert_numpy_array_equal(
1246             np.asarray(left.asi8), np.asarray(right.asi8), index_values=index_values
1247         )

[... skipping hidden 1 frame]

~/repos/pandas/pandas/_testing.py in _raise(left, right, err_msg)
1155             diff = diff * 100.0 / left.size
1156             msg = f"{obj} values are different ({np.round(diff, 5)} %)"
-> 1157             raise_assert_detail(obj, msg, left, right, index_values=index_values)
1158
1159         raise AssertionError(err_msg)

~/repos/pandas/pandas/_testing.py in raise_assert_detail(obj, message, left, right, diff, index_values)
1085         msg += f"\n[diff]: {diff}"
1086
-> 1087     raise AssertionError(msg)
1088
1089

AssertionError: numpy array are different

numpy array values are different (100.0 %)
[index]: [a]
[left]:  [500]
[right]: [0]

# Fixed code
def __init__(
    self,
    data=None,
    index: Optional[Axes] = None,
    columns: Optional[Axes] = None,
    dtype: Optional[Dtype] = None,
    copy: bool = False,
):
    if data is None:
        data = {}
    if dtype is not None:
        dtype = self._validate_dtype(dtype)

    if isinstance(data, DataFrame):
        data = data._mgr

    if isinstance(data, BlockManager):
        if index is None and columns is None and dtype is None and copy is False:
            # GH#33357 fastpath
            NDFrame.__init__(self, data)
            return

        mgr = self._init_mgr(
            data, axes={"index": index, "columns": columns}, dtype=dtype, copy=copy
        )

    elif isinstance(data, dict):
        mgr = init_dict(data, index, columns, dtype=dtype)
    elif isinstance(data, ma.MaskedArray):
        import numpy.ma.mrecords as mrecords

        # masked recarray
        if isinstance(data, mrecords.MaskedRecords):
            mgr = masked_rec_array_to_mgr(data, index, columns, dtype, copy)

        # a masked array
        else:
            data = sanitize_masked_array(data)
            mgr = init_ndarray(data, index, columns, dtype=dtype, copy=copy)

    elif isinstance(data, (np.ndarray, Series, Index)):
        if data.dtype.names:
            data_columns = list(data.dtype.names)
            data = {k: data[k] for k in data_columns}
            if columns is None:
                columns = data_columns
            mgr = init_dict(data, index, columns, dtype=dtype)
        elif getattr(data, "name", None) is not None:
            mgr = init_dict({data.name: data}, index, columns, dtype=dtype)
        else:
            mgr = init_ndarray(data, index, columns, dtype=dtype, copy=copy)

    # For data is list-like, or Iterable (will consume into list)
    elif isinstance(data, abc.Iterable) and not isinstance(data, (str, bytes)):
        if not isinstance(data, (abc.Sequence, ExtensionArray)):
            data = list(data)
        if len(data) > 0:
            if is_dataclass(data[0]):
                data = dataclasses_to_dicts(data)
            if is_list_like(data[0]) and getattr(data[0], "ndim", 1) == 1:
                if is_named_tuple(data[0]) and columns is None:
                    columns = data[0]._fields
                arrays, columns = to_arrays(data, columns, dtype=dtype)
                columns = ensure_index(columns)

                # set the index
                if index is None:
                    if isinstance(data[0], Series):
                        index = get_names_from_index(data)
                    elif isinstance(data[0], Categorical):
                        index = ibase.default_index(len(data[0]))
                    else:
                        index = ibase.default_index(len(data))

                mgr = arrays_to_mgr(arrays, columns, index, columns, dtype=dtype)
            else:
                mgr = init_ndarray(data, index, columns, dtype=dtype, copy=copy)
        else:
            mgr = init_dict({}, index, columns, dtype=dtype)
    # For data is scalar
    else:
        if index is None or columns is None:
            raise ValueError("DataFrame constructor not properly called!")

        if not dtype:
            dtype, _ = infer_dtype_from_scalar(data, pandas_dtype=True)

        # For data is a scalar extension dtype
        if is_extension_array_dtype(dtype):
            values = [
                construct_1d_arraylike_from_scalar(data, len(index), dtype)
                for _ in range(len(columns))
            ]
            mgr = arrays_to_mgr(values, columns, index, columns, dtype=None)
        else:
            if dtype.kind in ["m", "M"]:
                data = maybe_unbox_datetimelike(data, dtype)

            # Attempt to coerce to a numpy array
            try:
                arr = np.array(data, dtype=dtype, copy=copy)
            except (ValueError, TypeError) as err:
                exc = TypeError(
                    "DataFrame constructor called with "
                    f"incompatible data and dtype: {err}"
                )
                raise exc from err

            if arr.ndim != 0:
                raise ValueError("DataFrame constructor not properly called!")

            shape = (len(index), len(columns))
            values = np.full(shape, arr)

            mgr = init_ndarray(values, index, columns, dtype=values.dtype, copy=False)

    NDFrame.__init__(self, mgr)