# Buggy code
def sanitize_array(
    data,
    index: Optional["Index"],
    dtype: Optional[DtypeObj] = None,
    copy: bool = False,
    raise_cast_failure: bool = False,
) -> ArrayLike:
    """
    Sanitize input data to an ndarray or ExtensionArray, copy if specified,
    coerce to the dtype if specified.
    """

    if isinstance(data, ma.MaskedArray):
        mask = ma.getmaskarray(data)
        if mask.any():
            data, fill_value = maybe_upcast(data, copy=True)
            data.soften_mask()  # set hardmask False if it was True
            data[mask] = fill_value
        else:
            data = data.copy()

    # extract ndarray or ExtensionArray, ensure we have no PandasArray
    data = extract_array(data, extract_numpy=True)

    # GH#846
    if isinstance(data, np.ndarray):
        if dtype is not None and is_float_dtype(data.dtype) and is_integer_dtype(dtype):
            # possibility of nan -> garbage
            try:
                subarr = _try_cast(data, dtype, copy, True)
            except ValueError:
                if copy:
                    subarr = data.copy()
                else:
                    subarr = np.array(data, copy=False)
        else:
            # we will try to copy be-definition here
            subarr = _try_cast(data, dtype, copy, raise_cast_failure)

    elif isinstance(data, ABCExtensionArray):
        # it is already ensured above this is not a PandasArray
        subarr = data

        if dtype is not None:
            subarr = subarr.astype(dtype, copy=copy)
        elif copy:
            subarr = subarr.copy()
        return subarr

    elif isinstance(data, (list, tuple)) and len(data) > 0:
        if dtype is not None:
            subarr = _try_cast(data, dtype, copy, raise_cast_failure)
        else:
            subarr = maybe_convert_platform(data)

        subarr = maybe_cast_to_datetime(subarr, dtype)

    elif isinstance(data, range):
        # GH#16804
        arr = np.arange(data.start, data.stop, data.step, dtype="int64")
        subarr = _try_cast(arr, dtype, copy, raise_cast_failure)
    elif isinstance(data, abc.Set):
        raise TypeError("Set type is unordered")
    else:
        subarr = _try_cast(data, dtype, copy, raise_cast_failure)

    # scalar like, GH
    if getattr(subarr, "ndim", 0) == 0:
        if isinstance(data, list):  # pragma: no cover
            subarr = np.array(data, dtype=object)
        elif index is not None:
            value = data

            # figure out the dtype from the value (upcast if necessary)
            if dtype is None:
                dtype, value = infer_dtype_from_scalar(value)
            else:
                # need to possibly convert the value here
                value = maybe_cast_to_datetime(value, dtype)

            subarr = construct_1d_arraylike_from_scalar(value, len(index), dtype)

        else:
            return subarr.item()

    # the result that we want
    elif subarr.ndim == 1:
        if index is not None:
            # a 1-element ndarray
            if len(subarr) != len(index) and len(subarr) == 1:
                subarr = construct_1d_arraylike_from_scalar(
                    subarr[0], len(index), subarr.dtype
                )

    elif subarr.ndim > 1:
        if isinstance(data, np.ndarray):
            raise Exception("Data must be 1-dimensional")
        else:
            subarr = com.asarray_tuplesafe(data, dtype=dtype)

    if not (is_extension_array_dtype(subarr.dtype) or is_extension_array_dtype(dtype)):
        # This is to prevent mixed-type Series getting all casted to
        # NumPy string type, e.g. NaN --> '-1#IND'.
        if issubclass(subarr.dtype.type, str):
            # GH#16605
            # If not empty convert the data to dtype
            # GH#19853: If data is a scalar, subarr has already the result
            if not lib.is_scalar(data):
                if not np.all(isna(data)):
                    data = np.array(data, dtype=dtype, copy=False)
                subarr = np.array(data, dtype=object, copy=copy)

        if is_object_dtype(subarr.dtype) and not is_object_dtype(dtype):
            inferred = lib.infer_dtype(subarr, skipna=False)
            if inferred in {"interval", "period"}:
                subarr = array(subarr)

    return subarr


# Traceback (TypeError)
In [8]: pd.Series(None, index=[1, 2, 3], dtype='Int64')
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~/scipy/pandas/pandas/core/internals/construction.py in _try_cast(arr, take_fast_path, dtype, copy, raise_cast_failure)
691         if is_integer_dtype(dtype):
--> 692             subarr = maybe_cast_to_integer_array(arr, dtype)
693

~/scipy/pandas/pandas/core/dtypes/cast.py in maybe_cast_to_integer_array(arr, dtype, copy)
1311         if not hasattr(arr, "astype"):
-> 1312             casted = np.array(arr, dtype=dtype, copy=copy)
1313         else:

TypeError: data type not understood

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
<ipython-input-8-9447295feee6> in <module>
----> 1 pd.Series(None, index=[1, 2, 3], dtype='Int64')

~/scipy/pandas/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
202                 data = data._data
203             elif isinstance(data, dict):
--> 204                 data, index = self._init_dict(data, index, dtype)
205                 dtype = None
206                 copy = False

~/scipy/pandas/pandas/core/series.py in _init_dict(self, data, index, dtype)
295
296         # Input is now list-like, so rely on "standard" construction:
--> 297         s = Series(values, index=keys, dtype=dtype)
298
299         # Now we just make sure the order is respected, if any

~/scipy/pandas/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
253             else:
254                 data = sanitize_array(data, index, dtype, copy,
--> 255                                       raise_cast_failure=True)
256
257                 data = SingleBlockManager(data, index, fastpath=True)

~/scipy/pandas/pandas/core/internals/construction.py in sanitize_array(data, index, dtype, copy, raise_cast_failure)
620         subarr = _try_cast(arr, False, dtype, copy, raise_cast_failure)
621     else:
--> 622         subarr = _try_cast(data, False, dtype, copy, raise_cast_failure)
623
624     # scalar like, GH

~/scipy/pandas/pandas/core/internals/construction.py in _try_cast(arr, take_fast_path, dtype, copy, raise_cast_failure)
711             # create an extension array from its dtype
712             array_type = dtype.construct_array_type()._from_sequence
--> 713             subarr = array_type(arr, dtype=dtype, copy=copy)
714         elif dtype is not None and raise_cast_failure:
715             raise

~/scipy/pandas/pandas/core/arrays/integer.py in _from_sequence(cls, scalars, dtype, copy)
305     @classmethod
306     def _from_sequence(cls, scalars, dtype=None, copy=False):
--> 307         return integer_array(scalars, dtype=dtype, copy=copy)
308
309     @classmethod

~/scipy/pandas/pandas/core/arrays/integer.py in integer_array(values, dtype, copy)
110     TypeError if incompatible types
111     """
--> 112     values, mask = coerce_to_array(values, dtype=dtype, copy=copy)
113     return IntegerArray(values, mask)
114

~/scipy/pandas/pandas/core/arrays/integer.py in coerce_to_array(values, dtype, mask, copy)
202
203     if not values.ndim == 1:
--> 204         raise TypeError("values must be a 1D list-like")
205     if not mask.ndim == 1:
206         raise TypeError("mask must be a 1D list-like")

TypeError: values must be a 1D list-like

# Fixed code
def sanitize_array(
    data,
    index: Optional["Index"],
    dtype: Optional[DtypeObj] = None,
    copy: bool = False,
    raise_cast_failure: bool = False,
) -> ArrayLike:
    """
    Sanitize input data to an ndarray or ExtensionArray, copy if specified,
    coerce to the dtype if specified.
    """

    if isinstance(data, ma.MaskedArray):
        mask = ma.getmaskarray(data)
        if mask.any():
            data, fill_value = maybe_upcast(data, copy=True)
            data.soften_mask()  # set hardmask False if it was True
            data[mask] = fill_value
        else:
            data = data.copy()

    # extract ndarray or ExtensionArray, ensure we have no PandasArray
    data = extract_array(data, extract_numpy=True)

    # GH#846
    if isinstance(data, np.ndarray):
        if dtype is not None and is_float_dtype(data.dtype) and is_integer_dtype(dtype):
            # possibility of nan -> garbage
            try:
                subarr = _try_cast(data, dtype, copy, True)
            except ValueError:
                if copy:
                    subarr = data.copy()
                else:
                    subarr = np.array(data, copy=False)
        else:
            # we will try to copy be-definition here
            subarr = _try_cast(data, dtype, copy, raise_cast_failure)

    elif isinstance(data, ABCExtensionArray):
        # it is already ensured above this is not a PandasArray
        subarr = data

        if dtype is not None:
            subarr = subarr.astype(dtype, copy=copy)
        elif copy:
            subarr = subarr.copy()
        return subarr

    elif isinstance(data, (list, tuple)) and len(data) > 0:
        if dtype is not None:
            subarr = _try_cast(data, dtype, copy, raise_cast_failure)
        else:
            subarr = maybe_convert_platform(data)

        subarr = maybe_cast_to_datetime(subarr, dtype)

    elif isinstance(data, range):
        # GH#16804
        arr = np.arange(data.start, data.stop, data.step, dtype="int64")
        subarr = _try_cast(arr, dtype, copy, raise_cast_failure)
    elif isinstance(data, abc.Set):
        raise TypeError("Set type is unordered")
    elif lib.is_scalar(data) and index is not None and dtype is not None:
        data = maybe_cast_to_datetime(data, dtype)
        if not lib.is_scalar(data):
            data = data[0]
        subarr = construct_1d_arraylike_from_scalar(data, len(index), dtype)
    else:
        subarr = _try_cast(data, dtype, copy, raise_cast_failure)

    # scalar like, GH
    if getattr(subarr, "ndim", 0) == 0:
        if isinstance(data, list):  # pragma: no cover
            subarr = np.array(data, dtype=object)
        elif index is not None:
            value = data

            # figure out the dtype from the value (upcast if necessary)
            if dtype is None:
                dtype, value = infer_dtype_from_scalar(value)
            else:
                # need to possibly convert the value here
                value = maybe_cast_to_datetime(value, dtype)

            subarr = construct_1d_arraylike_from_scalar(value, len(index), dtype)

        else:
            return subarr.item()

    # the result that we want
    elif subarr.ndim == 1:
        if index is not None:
            # a 1-element ndarray
            if len(subarr) != len(index) and len(subarr) == 1:
                subarr = construct_1d_arraylike_from_scalar(
                    subarr[0], len(index), subarr.dtype
                )

    elif subarr.ndim > 1:
        if isinstance(data, np.ndarray):
            raise Exception("Data must be 1-dimensional")
        else:
            subarr = com.asarray_tuplesafe(data, dtype=dtype)

    if not (is_extension_array_dtype(subarr.dtype) or is_extension_array_dtype(dtype)):
        # This is to prevent mixed-type Series getting all casted to
        # NumPy string type, e.g. NaN --> '-1#IND'.
        if issubclass(subarr.dtype.type, str):
            # GH#16605
            # If not empty convert the data to dtype
            # GH#19853: If data is a scalar, subarr has already the result
            if not lib.is_scalar(data):
                if not np.all(isna(data)):
                    data = np.array(data, dtype=dtype, copy=False)
                subarr = np.array(data, dtype=object, copy=copy)

        if is_object_dtype(subarr.dtype) and not is_object_dtype(dtype):
            inferred = lib.infer_dtype(subarr, skipna=False)
            if inferred in {"interval", "period"}:
                subarr = array(subarr)

    return subarr