gurudesh/pytracebugs huggingface dataset:
# Buggy code
def _create_blocks(self, obj: FrameOrSeriesUnion):
    """
    Split data into blocks & return conformed data.
    """
    # Ensure the object we're rolling over is monotonically sorted relative
    # to the groups
    groupby_order = np.concatenate(list(self._groupby.grouper.indices.values())).astype(
        np.int64
    )
    obj = obj.take(groupby_order)
    return super()._create_blocks(obj)


# Traceback (ValueError)
pd.DataFrame({"s1": []}).groupby("s1").rolling(window=1).sum()'
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "site-packages/pandas/core/window/rolling.py", line 2072, in sum
return super().sum(*args, **kwargs)
File "site-packages/pandas/core/window/rolling.py", line 1424, in sum
window_func, center=self.center, floor=0, name="sum", **kwargs
File "site-packages/pandas/core/window/rolling.py", line 2194, in _apply
**kwargs,
File "site-packages/pandas/core/window/rolling.py", line 528, in _apply
blocks, obj = self._create_blocks(self._selected_obj)
File "site-packages/pandas/core/window/rolling.py", line 2230, in _create_blocks
list(self._groupby.grouper.indices.values())
File "<__array_function__ internals>", line 6, in concatenate
ValueError: need at least one array to concatenate

# Fixed code
def _create_blocks(self, obj: FrameOrSeriesUnion):
    """
    Split data into blocks & return conformed data.
    """
    # Ensure the object we're rolling over is monotonically sorted relative
    # to the groups
    # GH 36197
    if not obj.empty:
        groupby_order = np.concatenate(
            list(self._groupby.grouper.indices.values())
        ).astype(np.int64)
        obj = obj.take(groupby_order)
    return super()._create_blocks(obj)
# Buggy code
def _extract_bool_array(mask: ArrayLike) -> np.ndarray:
    """
    If we have a SparseArray or BooleanArray, convert it to ndarray[bool].
    """
    if isinstance(mask, ExtensionArray):
        # We could have BooleanArray, Sparse[bool], ...
        mask = np.asarray(mask, dtype=np.bool_)

    assert isinstance(mask, np.ndarray), type(mask)
    assert mask.dtype == bool, mask.dtype
    return mask


# Traceback (TypeError)
Traceback (most recent call last):
File "/home/developer/.config/JetBrains/PyCharmCE2020.1/scratches/scratch_1.py", line 7, in <module>
df = df.replace({"replace_string": "test"})
File "/home/developer/PycharmProjects/pandas/pandas/core/frame.py", line 4277, in replace
return super().replace(
File "/home/developer/PycharmProjects/pandas/pandas/core/generic.py", line 6598, in replace
return self.replace(
File "/home/developer/PycharmProjects/pandas/pandas/core/frame.py", line 4277, in replace
return super().replace(
File "/home/developer/PycharmProjects/pandas/pandas/core/generic.py", line 6641, in replace
new_data = self._mgr.replace_list(
File "/home/developer/PycharmProjects/pandas/pandas/core/internals/managers.py", line 616, in replace_list
masks = [comp(s, regex) for s in src_list]
File "/home/developer/PycharmProjects/pandas/pandas/core/internals/managers.py", line 616, in <listcomp>
masks = [comp(s, regex) for s in src_list]
File "/home/developer/PycharmProjects/pandas/pandas/core/internals/managers.py", line 614, in comp
return _compare_or_regex_search(values, s, regex)
File "/home/developer/PycharmProjects/pandas/pandas/core/internals/managers.py", line 1946, in _compare_or_regex_search
_check_comparison_types(False, a, b)
File "/home/developer/PycharmProjects/pandas/pandas/core/internals/managers.py", line 1925, in _check_comparison_types
raise TypeError(
TypeError: Cannot compare types 'ndarray(dtype=int64)' and 'str'

Process finished with exit code 1

# Fixed code
def _extract_bool_array(mask: ArrayLike) -> np.ndarray:
    """
    If we have a SparseArray or BooleanArray, convert it to ndarray[bool].
    """
    if isinstance(mask, ExtensionArray):
        # We could have BooleanArray, Sparse[bool], ...
        #  Except for BooleanArray, this is equivalent to just
        #  np.asarray(mask, dtype=bool)
        mask = mask.to_numpy(dtype=bool, na_value=False)

    assert isinstance(mask, np.ndarray), type(mask)
    assert mask.dtype == bool, mask.dtype
    return mask
# Buggy code
def replace_list(
    self: T,
    src_list: List[Any],
    dest_list: List[Any],
    inplace: bool = False,
    regex: bool = False,
) -> T:
    """do a list replace"""
    inplace = validate_bool_kwarg(inplace, "inplace")

    # figure out our mask apriori to avoid repeated replacements
    values = self.as_array()

    def comp(s: Scalar, mask: np.ndarray, regex: bool = False):
        """
        Generate a bool array by perform an equality check, or perform
        an element-wise regular expression matching
        """
        if isna(s):
            return ~mask

        s = com.maybe_box_datetimelike(s)
        return _compare_or_regex_search(values, s, regex, mask)

    # Calculate the mask once, prior to the call of comp
    # in order to avoid repeating the same computations
    mask = ~isna(values)

    masks = [comp(s, mask, regex) for s in src_list]

    bm = self.apply(
        "_replace_list",
        src_list=src_list,
        dest_list=dest_list,
        masks=masks,
        inplace=inplace,
        regex=regex,
    )
    bm._consolidate_inplace()
    return bm


# Traceback (TypeError)
Traceback (most recent call last):
File "/home/developer/.config/JetBrains/PyCharmCE2020.1/scratches/scratch_1.py", line 7, in <module>
df = df.replace({"replace_string": "test"})
File "/home/developer/PycharmProjects/pandas/pandas/core/frame.py", line 4277, in replace
return super().replace(
File "/home/developer/PycharmProjects/pandas/pandas/core/generic.py", line 6598, in replace
return self.replace(
File "/home/developer/PycharmProjects/pandas/pandas/core/frame.py", line 4277, in replace
return super().replace(
File "/home/developer/PycharmProjects/pandas/pandas/core/generic.py", line 6641, in replace
new_data = self._mgr.replace_list(
File "/home/developer/PycharmProjects/pandas/pandas/core/internals/managers.py", line 616, in replace_list
masks = [comp(s, regex) for s in src_list]
File "/home/developer/PycharmProjects/pandas/pandas/core/internals/managers.py", line 616, in <listcomp>
masks = [comp(s, regex) for s in src_list]
File "/home/developer/PycharmProjects/pandas/pandas/core/internals/managers.py", line 614, in comp
return _compare_or_regex_search(values, s, regex)
File "/home/developer/PycharmProjects/pandas/pandas/core/internals/managers.py", line 1946, in _compare_or_regex_search
_check_comparison_types(False, a, b)
File "/home/developer/PycharmProjects/pandas/pandas/core/internals/managers.py", line 1925, in _check_comparison_types
raise TypeError(
TypeError: Cannot compare types 'ndarray(dtype=int64)' and 'str'

Process finished with exit code 1

# Fixed code
def replace_list(
    self: T,
    src_list: List[Any],
    dest_list: List[Any],
    inplace: bool = False,
    regex: bool = False,
) -> T:
    """do a list replace"""
    inplace = validate_bool_kwarg(inplace, "inplace")

    bm = self.apply(
        "_replace_list",
        src_list=src_list,
        dest_list=dest_list,
        inplace=inplace,
        regex=regex,
    )
    bm._consolidate_inplace()
    return bm
# Buggy code
def is_in_obj(gpr) -> bool:
    if not hasattr(gpr, "name"):
        return False
    try:
        return gpr is obj[gpr.name]
    except (KeyError, IndexError, ValueError):
        # TODO: ValueError: Given date string not likely a datetime.
        # should be KeyError?
        return False


# Traceback (ValueError)
In [11]: s = pd.Series([1, 2, 3], index=pd.period_range('2000', periods=3, name='A'))

In [12]: s.index.get_loc('A')
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-12-3a472f211c46> in <module>
----> 1 s.index.get_loc('A')

~/sandbox/pandas/pandas/core/indexes/period.py in get_loc(self, key, method, tolerance)
501
502             try:
--> 503                 asdt, reso = parse_time_string(key, self.freq)
504             except DateParseError as err:
505                 # A string with invalid format

~/sandbox/pandas/pandas/_libs/tslibs/parsing.pyx in pandas._libs.tslibs.parsing.parse_time_string()

~/sandbox/pandas/pandas/_libs/tslibs/parsing.pyx in pandas._libs.tslibs.parsing.parse_datetime_string_with_reso()

ValueError: Given date string not likely a datetime.

# Fixed code
def is_in_obj(gpr) -> bool:
    if not hasattr(gpr, "name"):
        return False
    try:
        return gpr is obj[gpr.name]
    except (KeyError, IndexError):
        # IndexError reached in e.g. test_skip_group_keys when we pass
        #  lambda here
        return False
# Buggy code
def _disallow_scalar_only_bool_ops(self):
    if (
        (self.lhs.is_scalar or self.rhs.is_scalar)
        and self.op in _bool_ops_dict
        and (
            not (
                issubclass(self.rhs.return_type, (bool, np.bool_))
                and issubclass(self.lhs.return_type, (bool, np.bool_))
            )
        )
    ):
        raise NotImplementedError("cannot evaluate scalar only bool ops")


# Traceback (TypeError)
# Your code here
Scenario 1:

df2=pd.DataFrame({'a1':[10,20]})

df2.eval("c=((a1>10) &amp; True )")
Out[8]:
a1      c
0  10  False
1  20   True
Scenario 2:
df2=pd.DataFrame({'a1':['Y','N']})
df2.eval("c=((a1 == 'Y') &amp; True )")
Traceback (most recent call last):

TypeError: issubclass() arg 1 must be a class

# Fixed code
def _disallow_scalar_only_bool_ops(self):
    rhs = self.rhs
    lhs = self.lhs

    # GH#24883 unwrap dtype if necessary to ensure we have a type object
    rhs_rt = rhs.return_type
    rhs_rt = getattr(rhs_rt, "type", rhs_rt)
    lhs_rt = lhs.return_type
    lhs_rt = getattr(lhs_rt, "type", lhs_rt)
    if (
        (lhs.is_scalar or rhs.is_scalar)
        and self.op in _bool_ops_dict
        and (
            not (
                issubclass(rhs_rt, (bool, np.bool_))
                and issubclass(lhs_rt, (bool, np.bool_))
            )
        )
    ):
        raise NotImplementedError("cannot evaluate scalar only bool ops")
# Buggy code
def _get_data_from_filepath(self, filepath_or_buffer):
    """
    The function read_json accepts three input types:
        1. filepath (string-like)
        2. file-like object (e.g. open file object, StringIO)
        3. JSON string

    This method turns (1) into (2) to simplify the rest of the processing.
    It returns input types (2) and (3) unchanged.
    """
    data = filepath_or_buffer

    exists = False
    if isinstance(data, str):
        try:
            exists = os.path.exists(filepath_or_buffer)
        # gh-5874: if the filepath is too long will raise here
        except (TypeError, ValueError):
            pass

    if exists or self.compression is not None:
        data, _ = get_handle(
            filepath_or_buffer,
            "r",
            encoding=self.encoding,
            compression=self.compression,
        )
        self.should_close = True
        self.open_stream = data

    return data


# Traceback (TypeError)
# output when using fileurl:
TypeError                                 Traceback (most recent call last)
<ipython-input-82-605ab8a466fd> in <module>
----> 1 for chunk in reader:
2     print(chunk)
3

~/anaconda3/envs/test_latest_pandas_json/lib/python3.7/site-packages/pandas/io/json/json.py in __next__(self)
579         lines = list(islice(self.data, self.chunksize))
580         if lines:
--> 581             lines_json = self._combine_lines(lines)
582             obj = self._get_object_parser(lines_json)
583

~/anaconda3/envs/test_latest_pandas_json/lib/python3.7/site-packages/pandas/io/json/json.py in _combine_lines(self, lines)
520         """
521         lines = filter(None, map(lambda x: x.strip(), lines))
--> 522         return '[' + ','.join(lines) + ']'
523
524     def read(self):

TypeError: sequence item 0: expected str instance, bytes found

# Fixed code
def _get_data_from_filepath(self, filepath_or_buffer):
    """
    The function read_json accepts three input types:
        1. filepath (string-like)
        2. file-like object (e.g. open file object, StringIO)
        3. JSON string

    This method turns (1) into (2) to simplify the rest of the processing.
    It returns input types (2) and (3) unchanged.
    """
    data = filepath_or_buffer

    exists = False
    if isinstance(data, str):
        try:
            exists = os.path.exists(filepath_or_buffer)
        # gh-5874: if the filepath is too long will raise here
        except (TypeError, ValueError):
            pass

    if exists or self.compression is not None:
        data, _ = get_handle(
            filepath_or_buffer,
            "r",
            encoding=self.encoding,
            compression=self.compression,
        )
        self.should_close = True
        self.open_stream = data

    if isinstance(data, BytesIO):
        data = data.getvalue().decode()

    return data
# Buggy code
def setup(self, orient, frame):
    N = 10**5
    ncols = 5
    index = date_range("20000101", periods=N, freq="H")
    timedeltas = timedelta_range(start=1, periods=N, freq="s")
    datetimes = date_range(start=1, periods=N, freq="s")
    ints = np.random.randint(100000000, size=N)
    floats = np.random.randn(N)
    strings = tm.makeStringIndex(N)
    self.df = DataFrame(np.random.randn(N, ncols), index=np.arange(N))
    self.df_date_idx = DataFrame(np.random.randn(N, ncols), index=index)
    self.df_td_int_ts = DataFrame(
        {
            "td_1": timedeltas,
            "td_2": timedeltas,
            "int_1": ints,
            "int_2": ints,
            "ts_1": datetimes,
            "ts_2": datetimes,
        },
        index=index,
    )
    self.df_int_floats = DataFrame(
        {
            "int_1": ints,
            "int_2": ints,
            "int_3": ints,
            "float_1": floats,
            "float_2": floats,
            "float_3": floats,
        },
        index=index,
    )
    self.df_int_float_str = DataFrame(
        {
            "int_1": ints,
            "int_2": ints,
            "float_1": floats,
            "float_2": floats,
            "str_1": strings,
            "str_2": strings,
        },
        index=index,
    )


# Traceback (OverflowError)
dumps(sys.maxsize)
'9223372036854775807'
dumps(sys.maxsize + 1, default_handler=str)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
OverflowError: int too big to convert

# Fixed code
def setup(self, orient, frame):
    N = 10**5
    ncols = 5
    index = date_range("20000101", periods=N, freq="H")
    timedeltas = timedelta_range(start=1, periods=N, freq="s")
    datetimes = date_range(start=1, periods=N, freq="s")
    ints = np.random.randint(100000000, size=N)
    longints = sys.maxsize * np.random.randint(100000000, size=N)
    floats = np.random.randn(N)
    strings = tm.makeStringIndex(N)
    self.df = DataFrame(np.random.randn(N, ncols), index=np.arange(N))
    self.df_date_idx = DataFrame(np.random.randn(N, ncols), index=index)
    self.df_td_int_ts = DataFrame(
        {
            "td_1": timedeltas,
            "td_2": timedeltas,
            "int_1": ints,
            "int_2": ints,
            "ts_1": datetimes,
            "ts_2": datetimes,
        },
        index=index,
    )
    self.df_int_floats = DataFrame(
        {
            "int_1": ints,
            "int_2": ints,
            "int_3": ints,
            "float_1": floats,
            "float_2": floats,
            "float_3": floats,
        },
        index=index,
    )
    self.df_int_float_str = DataFrame(
        {
            "int_1": ints,
            "int_2": ints,
            "float_1": floats,
            "float_2": floats,
            "str_1": strings,
            "str_2": strings,
        },
        index=index,
    )

    self.df_longint_float_str = DataFrame(
        {
            "longint_1": longints,
            "longint_2": longints,
            "float_1": floats,
            "float_2": floats,
            "str_1": strings,
            "str_2": strings,
        },
        index=index,
    )
# Buggy code
def setup(self):
    N = 10**5
    ncols = 5
    index = date_range("20000101", periods=N, freq="H")
    timedeltas = timedelta_range(start=1, periods=N, freq="s")
    datetimes = date_range(start=1, periods=N, freq="s")
    ints = np.random.randint(100000000, size=N)
    floats = np.random.randn(N)
    strings = tm.makeStringIndex(N)
    self.df = DataFrame(np.random.randn(N, ncols), index=np.arange(N))
    self.df_date_idx = DataFrame(np.random.randn(N, ncols), index=index)
    self.df_td_int_ts = DataFrame(
        {
            "td_1": timedeltas,
            "td_2": timedeltas,
            "int_1": ints,
            "int_2": ints,
            "ts_1": datetimes,
            "ts_2": datetimes,
        },
        index=index,
    )
    self.df_int_floats = DataFrame(
        {
            "int_1": ints,
            "int_2": ints,
            "int_3": ints,
            "float_1": floats,
            "float_2": floats,
            "float_3": floats,
        },
        index=index,
    )
    self.df_int_float_str = DataFrame(
        {
            "int_1": ints,
            "int_2": ints,
            "float_1": floats,
            "float_2": floats,
            "str_1": strings,
            "str_2": strings,
        },
        index=index,
    )


# Traceback (OverflowError)
dumps(sys.maxsize)
'9223372036854775807'
dumps(sys.maxsize + 1, default_handler=str)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
OverflowError: int too big to convert

# Fixed code
def setup(self):
    N = 10**5
    ncols = 5
    index = date_range("20000101", periods=N, freq="H")
    timedeltas = timedelta_range(start=1, periods=N, freq="s")
    datetimes = date_range(start=1, periods=N, freq="s")
    ints = np.random.randint(100000000, size=N)
    longints = sys.maxsize * np.random.randint(100000000, size=N)
    floats = np.random.randn(N)
    strings = tm.makeStringIndex(N)
    self.df = DataFrame(np.random.randn(N, ncols), index=np.arange(N))
    self.df_date_idx = DataFrame(np.random.randn(N, ncols), index=index)
    self.df_td_int_ts = DataFrame(
        {
            "td_1": timedeltas,
            "td_2": timedeltas,
            "int_1": ints,
            "int_2": ints,
            "ts_1": datetimes,
            "ts_2": datetimes,
        },
        index=index,
    )
    self.df_int_floats = DataFrame(
        {
            "int_1": ints,
            "int_2": ints,
            "int_3": ints,
            "float_1": floats,
            "float_2": floats,
            "float_3": floats,
        },
        index=index,
    )
    self.df_int_float_str = DataFrame(
        {
            "int_1": ints,
            "int_2": ints,
            "float_1": floats,
            "float_2": floats,
            "str_1": strings,
            "str_2": strings,
        },
        index=index,
    )
    self.df_longint_float_str = DataFrame(
        {
            "longint_1": longints,
            "longint_2": longints,
            "float_1": floats,
            "float_2": floats,
            "str_1": strings,
            "str_2": strings,
        },
        index=index,
    )
# Buggy code
def _is_potential_multi_index(columns):
    """
    Check whether or not the `columns` parameter
    could be converted into a MultiIndex.

    Parameters
    ----------
    columns : array-like
        Object which may or may not be convertible into a MultiIndex

    Returns
    -------
    boolean : Whether or not columns could become a MultiIndex
    """
    return (
        len(columns)
        and not isinstance(columns, MultiIndex)
        and all(isinstance(c, tuple) for c in columns)
    )


# Traceback (ValueError)
$ ~/miniconda3/bin/python read.py
Traceback (most recent call last):
File "read.py", line 4, in <module>
df = pd.read_excel('df_empty.xlsx', engine='openpyxl', sheet_name='Sheet1', index_col=0, header=[0, 1])
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 334, in read_excel
**kwds,
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 888, in parse
**kwds,
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 521, in parse
].columns.set_names(header_names)
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 1325, in set_names
idx._set_names(names, level=level)
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 1239, in _set_names
raise ValueError(f"Length of new names must be 1, got {len(values)}")
ValueError: Length of new names must be 1, got 2

# Fixed code
def _is_potential_multi_index(
    columns, index_col: Optional[Union[bool, Sequence[int]]] = None
):
    """
    Check whether or not the `columns` parameter
    could be converted into a MultiIndex.

    Parameters
    ----------
    columns : array-like
        Object which may or may not be convertible into a MultiIndex
    index_col : None, bool or list, optional
        Column or columns to use as the (possibly hierarchical) index

    Returns
    -------
    boolean : Whether or not columns could become a MultiIndex
    """
    if index_col is None or isinstance(index_col, bool):
        index_col = []

    return (
        len(columns)
        and not isinstance(columns, MultiIndex)
        and all(isinstance(c, tuple) for c in columns if c not in list(index_col))
    )
# Buggy code
def _maybe_dedup_names(self, names):
    # see gh-7160 and gh-9424: this helps to provide
    # immediate alleviation of the duplicate names
    # issue and appears to be satisfactory to users,
    # but ultimately, not needing to butcher the names
    # would be nice!
    if self.mangle_dupe_cols:
        names = list(names)  # so we can index
        counts = defaultdict(int)
        is_potential_mi = _is_potential_multi_index(names)

        for i, col in enumerate(names):
            cur_count = counts[col]

            while cur_count > 0:
                counts[col] = cur_count + 1

                if is_potential_mi:
                    col = col[:-1] + (f"{col[-1]}.{cur_count}",)
                else:
                    col = f"{col}.{cur_count}"
                cur_count = counts[col]

            names[i] = col
            counts[col] = cur_count + 1

    return names


# Traceback (ValueError)
$ ~/miniconda3/bin/python read.py
Traceback (most recent call last):
File "read.py", line 4, in <module>
df = pd.read_excel('df_empty.xlsx', engine='openpyxl', sheet_name='Sheet1', index_col=0, header=[0, 1])
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 334, in read_excel
**kwds,
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 888, in parse
**kwds,
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 521, in parse
].columns.set_names(header_names)
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 1325, in set_names
idx._set_names(names, level=level)
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 1239, in _set_names
raise ValueError(f"Length of new names must be 1, got {len(values)}")
ValueError: Length of new names must be 1, got 2

# Fixed code
def _maybe_dedup_names(self, names):
    # see gh-7160 and gh-9424: this helps to provide
    # immediate alleviation of the duplicate names
    # issue and appears to be satisfactory to users,
    # but ultimately, not needing to butcher the names
    # would be nice!
    if self.mangle_dupe_cols:
        names = list(names)  # so we can index
        counts = defaultdict(int)
        is_potential_mi = _is_potential_multi_index(names, self.index_col)

        for i, col in enumerate(names):
            cur_count = counts[col]

            while cur_count > 0:
                counts[col] = cur_count + 1

                if is_potential_mi:
                    col = col[:-1] + (f"{col[-1]}.{cur_count}",)
                else:
                    col = f"{col}.{cur_count}"
                cur_count = counts[col]

            names[i] = col
            counts[col] = cur_count + 1

    return names
# Buggy code
def to_timestamp(self, freq=None, how="start", copy=True) -> "Series":
    """
    Cast to DatetimeIndex of Timestamps, at *beginning* of period.

    Parameters
    ----------
    freq : str, default frequency of PeriodIndex
        Desired frequency.
    how : {'s', 'e', 'start', 'end'}
        Convention for converting period to timestamp; start of period
        vs. end.
    copy : bool, default True
        Whether or not to return a copy.

    Returns
    -------
    Series with DatetimeIndex
    """
    new_values = self._values
    if copy:
        new_values = new_values.copy()

    assert isinstance(self.index, PeriodIndex)
    new_index = self.index.to_timestamp(freq=freq, how=how)  # type: ignore
    return self._constructor(new_values, index=new_index).__finalize__(
        self, method="to_timestamp"
    )


# Traceback (AssertionError)
In [5]: import pandas as pd

In [6]: pd.Series([0]).to_timestamp()
---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-6-1a9d08d7daba> in <module>
----> 1 pd.Series([0]).to_timestamp()

~/sandbox/pandas/pandas/core/series.py in to_timestamp(self, freq, how, copy)
4565             new_values = new_values.copy()
4566
-> 4567         assert isinstance(self.index, (ABCDatetimeIndex, ABCPeriodIndex))
4568         new_index = self.index.to_timestamp(freq=freq, how=how)
4569         return self._constructor(new_values, index=new_index).__finalize__(

AssertionError:

# Fixed code
def to_timestamp(self, freq=None, how="start", copy=True) -> "Series":
    """
    Cast to DatetimeIndex of Timestamps, at *beginning* of period.

    Parameters
    ----------
    freq : str, default frequency of PeriodIndex
        Desired frequency.
    how : {'s', 'e', 'start', 'end'}
        Convention for converting period to timestamp; start of period
        vs. end.
    copy : bool, default True
        Whether or not to return a copy.

    Returns
    -------
    Series with DatetimeIndex
    """
    new_values = self._values
    if copy:
        new_values = new_values.copy()

    if not isinstance(self.index, PeriodIndex):
        raise TypeError(f"unsupported Type {type(self.index).__name__}")
    new_index = self.index.to_timestamp(freq=freq, how=how)  # type: ignore
    return self._constructor(new_values, index=new_index).__finalize__(
        self, method="to_timestamp"
    )
# Buggy code
def to_period(self, freq=None, copy=True) -> "Series":
    """
    Convert Series from DatetimeIndex to PeriodIndex with desired
    frequency (inferred from index if not passed).

    Parameters
    ----------
    freq : str, default None
        Frequency associated with the PeriodIndex.
    copy : bool, default True
        Whether or not to return a copy.

    Returns
    -------
    Series
        Series with index converted to PeriodIndex.
    """
    new_values = self._values
    if copy:
        new_values = new_values.copy()

    assert isinstance(self.index, DatetimeIndex)
    new_index = self.index.to_period(freq=freq)  # type: ignore
    return self._constructor(new_values, index=new_index).__finalize__(
        self, method="to_period"
    )


# Traceback (AssertionError)
In [5]: import pandas as pd

In [6]: pd.Series([0]).to_timestamp()
---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-6-1a9d08d7daba> in <module>
----> 1 pd.Series([0]).to_timestamp()

~/sandbox/pandas/pandas/core/series.py in to_timestamp(self, freq, how, copy)
4565             new_values = new_values.copy()
4566
-> 4567         assert isinstance(self.index, (ABCDatetimeIndex, ABCPeriodIndex))
4568         new_index = self.index.to_timestamp(freq=freq, how=how)
4569         return self._constructor(new_values, index=new_index).__finalize__(

AssertionError:

# Fixed code
def to_period(self, freq=None, copy=True) -> "Series":
    """
    Convert Series from DatetimeIndex to PeriodIndex with desired
    frequency (inferred from index if not passed).

    Parameters
    ----------
    freq : str, default None
        Frequency associated with the PeriodIndex.
    copy : bool, default True
        Whether or not to return a copy.

    Returns
    -------
    Series
        Series with index converted to PeriodIndex.
    """
    new_values = self._values
    if copy:
        new_values = new_values.copy()

    if not isinstance(self.index, DatetimeIndex):
        raise TypeError(f"unsupported Type {type(self.index).__name__}")
    new_index = self.index.to_period(freq=freq)  # type: ignore
    return self._constructor(new_values, index=new_index).__finalize__(
        self, method="to_period"
    )
# Buggy code
def _na_for_min_count(
    values: np.ndarray, axis: Optional[int]
) -> Union[Scalar, np.ndarray]:
    """
    Return the missing value for `values`.

    Parameters
    ----------
    values : ndarray
    axis : int or None
        axis for the reduction, required if values.ndim > 1.

    Returns
    -------
    result : scalar or ndarray
        For 1-D values, returns a scalar of the correct missing type.
        For 2-D values, returns a 1-D array where each element is missing.
    """
    # we either return np.nan or pd.NaT
    if is_numeric_dtype(values):
        values = values.astype("float64")
    fill_value = na_value_for_dtype(values.dtype)

    if values.ndim == 1:
        return fill_value
    else:
        assert axis is not None  # assertion to make mypy happy
        result_shape = values.shape[:axis] + values.shape[axis + 1 :]
        result = np.empty(result_shape, dtype=values.dtype)
        result.fill(fill_value)
        return result


# Traceback (ValueError)
Traceback (most recent call last):
File "<ipython-input-17-be9940feb663>", line 1, in <module>
df.max()
File "pandas/core/generic.py", line 11215, in stat_func
f, name, axis=axis, skipna=skipna, numeric_only=numeric_only
File "pandas/core/frame.py", line 7907, in _reduce
result = f(values)
File "pandas/core/frame.py", line 7865, in f
return op(x, axis=axis, skipna=skipna, **kwds)
File "pandas/core/nanops.py", line 109, in f
return _na_for_min_count(values, axis)
File "pandas/core/nanops.py", line 392, in _na_for_min_count
result.fill(fill_value)
ValueError: cannot convert float NaN to integer

# Fixed code
def _na_for_min_count(
    values: np.ndarray, axis: Optional[int]
) -> Union[Scalar, np.ndarray]:
    """
    Return the missing value for `values`.

    Parameters
    ----------
    values : ndarray
    axis : int or None
        axis for the reduction, required if values.ndim > 1.

    Returns
    -------
    result : scalar or ndarray
        For 1-D values, returns a scalar of the correct missing type.
        For 2-D values, returns a 1-D array where each element is missing.
    """
    # we either return np.nan or pd.NaT
    if is_numeric_dtype(values):
        values = values.astype("float64")
    fill_value = na_value_for_dtype(values.dtype)

    if values.ndim == 1:
        return fill_value
    else:
        assert axis is not None  # assertion to make mypy happy
        result_shape = values.shape[:axis] + values.shape[axis + 1 :]
        # calling np.full with dtype parameter throws an ValueError when called
        # with dtype=np.datetime64 and and fill_value=pd.NaT
        try:
            result = np.full(result_shape, fill_value, dtype=values.dtype)
        except ValueError:
            result = np.full(result_shape, fill_value)
        return result
# Buggy code
def putmask(
    self,
    mask,
    new,
    inplace: bool = False,
    axis: int = 0,
    transpose: bool = False,
) -> List["Block"]:
    """
    See Block.putmask.__doc__
    """
    inplace = validate_bool_kwarg(inplace, "inplace")

    mask = _extract_bool_array(mask)

    new_values = self.values if inplace else self.values.copy()

    if isinstance(new, np.ndarray) and len(new) == len(mask):
        new = new[mask]

    mask = _safe_reshape(mask, new_values.shape)

    new_values[mask] = new
    return [self.make_block(values=new_values)]


# Traceback (ValueError)
Traceback (most recent call last):

File "<ipython-input-15-b9da8f25067a>", line 1, in <module>
a.update(b)

File "C:\tools\anaconda3\envs\Simple\lib\site-packages\pandas\core\series.py", line 2810, in update
self._data = self._data.putmask(mask=mask, new=other, inplace=True)

File "C:\tools\anaconda3\envs\Simple\lib\site-packages\pandas\core\internals\managers.py", line 564, in putmask
return self.apply("putmask", **kwargs)

File "C:\tools\anaconda3\envs\Simple\lib\site-packages\pandas\core\internals\managers.py", line 442, in apply
applied = getattr(b, f)(**kwargs)

File "C:\tools\anaconda3\envs\Simple\lib\site-packages\pandas\core\internals\blocks.py", line 1676, in putmask
new_values[mask] = new

File "C:\tools\anaconda3\envs\Simple\lib\site-packages\pandas\core\arrays\string_.py", line 248, in __setitem__
super().__setitem__(key, value)

File "C:\tools\anaconda3\envs\Simple\lib\site-packages\pandas\core\arrays\numpy_.py", line 252, in __setitem__
self._ndarray[key] = value

ValueError: NumPy boolean array indexing assignment cannot assign 3 input values to the 1 output values where the mask is true

# Fixed code
def putmask(
    self,
    mask,
    new,
    inplace: bool = False,
    axis: int = 0,
    transpose: bool = False,
) -> List["Block"]:
    """
    See Block.putmask.__doc__
    """
    inplace = validate_bool_kwarg(inplace, "inplace")

    mask = _extract_bool_array(mask)

    new_values = self.values if inplace else self.values.copy()

    if isinstance(new, (np.ndarray, ExtensionArray)) and len(new) == len(mask):
        new = new[mask]

    mask = _safe_reshape(mask, new_values.shape)

    new_values[mask] = new
    return [self.make_block(values=new_values)]
# Buggy code
def __getitem__(self, key):
    if self.ndim != 1 or not is_scalar(key):
        # FIXME: is_scalar check is a kludge
        return super().__getitem__(key)

    # Like Index.get_value, but we do not allow positional fallback
    obj = self.obj
    loc = obj.index.get_loc(key)
    return obj.index._get_values_for_loc(obj, loc, key)


# Traceback (AttributeError)
arr = np.random.randn(6).reshape(3, 2)
df = pd.DataFrame(arr, columns=["A", "A"])

df.at[0, "A"]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/core/indexing.py", line 2069, in __getitem__
return super().__getitem__(key)
File "pandas/core/indexing.py", line 2034, in __getitem__
return self.obj._get_value(*key, takeable=self._takeable)
File "pandas/core/frame.py", line 2706, in _get_value
series = self._get_item_cache(col)
File "pandas/core/generic.py", line 3550, in _get_item_cache
res = self._box_item_values(item, values)
File "pandas/core/frame.py", line 2880, in _box_item_values
return self._constructor(values.T, columns=items, index=self.index)
AttributeError: 'BlockManager' object has no attribute 'T'

# Fixed code
def __getitem__(self, key):
    if self.ndim == 2 and not self._axes_are_unique:
        # GH#33041 fall back to .loc
        if not isinstance(key, tuple) or not all(is_scalar(x) for x in key):
            raise ValueError("Invalid call for scalar access (getting)!")
        return self.obj.loc[key]

    return super().__getitem__(key)
# Buggy code
def __setitem__(self, key, value):
    if isinstance(key, tuple):
        key = tuple(com.apply_if_callable(x, self.obj) for x in key)
    else:
        # scalar callable may return tuple
        key = com.apply_if_callable(key, self.obj)

    if not isinstance(key, tuple):
        key = _tuplify(self.ndim, key)
    if len(key) != self.ndim:
        raise ValueError("Not enough indexers for scalar access (setting)!")
    key = list(self._convert_key(key, is_setter=True))
    self.obj._set_value(*key, value=value, takeable=self._takeable)


# Traceback (AttributeError)
arr = np.random.randn(6).reshape(3, 2)
df = pd.DataFrame(arr, columns=["A", "A"])

df.at[0, "A"]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/core/indexing.py", line 2069, in __getitem__
return super().__getitem__(key)
File "pandas/core/indexing.py", line 2034, in __getitem__
return self.obj._get_value(*key, takeable=self._takeable)
File "pandas/core/frame.py", line 2706, in _get_value
series = self._get_item_cache(col)
File "pandas/core/generic.py", line 3550, in _get_item_cache
res = self._box_item_values(item, values)
File "pandas/core/frame.py", line 2880, in _box_item_values
return self._constructor(values.T, columns=items, index=self.index)
AttributeError: 'BlockManager' object has no attribute 'T'

# Fixed code
def __setitem__(self, key, value):
    if self.ndim == 2 and not self._axes_are_unique:
        # GH#33041 fall back to .loc
        if not isinstance(key, tuple) or not all(is_scalar(x) for x in key):
            raise ValueError("Invalid call for scalar access (setting)!")

        self.obj.loc[key] = value
        return

    return super().__setitem__(key, value)
# Buggy code
def pre_processor(vals: np.ndarray) -> Tuple[np.ndarray, Optional[Type]]:
    if is_object_dtype(vals):
        raise TypeError("'quantile' cannot be performed against 'object' dtypes!")

    inference = None
    if is_integer_dtype(vals):
        inference = np.int64
    elif is_datetime64_dtype(vals):
        inference = "datetime64[ns]"
        vals = np.asarray(vals).astype(np.float)

    return vals, inference


# Traceback (TypeError)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-1-ef27f953b945> in <module>
4     {"a": ["x", "x", "y", "y"], "b": pd.array([1, 2, 3, 4], dtype="Int64")}
5 )
----> 6 df.groupby("a")["b"].quantile(0.5)

~/opt/miniconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in quantile(self, q, interpolation)
1911                 post_processing=post_processor,
1912                 q=q,
-> 1913                 interpolation=interpolation,
1914             )
1915         else:

~/opt/miniconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in _get_cythonized_result(self, how, cython_dtype, aggregate, needs_values, needs_mask, needs_ngroups, result_is_index, pre_processing, post_processing, **kwargs)
2289                 func = partial(func, ngroups)
2290
-> 2291             func(**kwargs)  # Call func to modify indexer values in place
2292
2293             if result_is_index:

pandas/_libs/groupby.pyx in pandas._libs.groupby.__pyx_fused_cpdef()

TypeError: No matching signature found

# Fixed code
def pre_processor(vals: np.ndarray) -> Tuple[np.ndarray, Optional[Type]]:
    if is_object_dtype(vals):
        raise TypeError("'quantile' cannot be performed against 'object' dtypes!")

    inference = None
    if is_integer_dtype(vals.dtype):
        if is_extension_array_dtype(vals.dtype):
            vals = vals.to_numpy(dtype=float, na_value=np.nan)
        inference = np.int64
    elif is_bool_dtype(vals.dtype) and is_extension_array_dtype(vals.dtype):
        vals = vals.to_numpy(dtype=float, na_value=np.nan)
    elif is_datetime64_dtype(vals.dtype):
        inference = "datetime64[ns]"
        vals = np.asarray(vals).astype(np.float)

    return vals, inference
# Buggy code
def __init__(
    self, filepath_or_buffer, index=None, encoding="ISO-8859-1", chunksize=None
):
    self._encoding = encoding
    self._lines_read = 0
    self._index = index
    self._chunksize = chunksize

    if isinstance(filepath_or_buffer, str):
        (
            filepath_or_buffer,
            encoding,
            compression,
            should_close,
        ) = get_filepath_or_buffer(filepath_or_buffer, encoding=encoding)

    if isinstance(filepath_or_buffer, (str, bytes)):
        self.filepath_or_buffer = open(filepath_or_buffer, "rb")
    else:
        # Copy to BytesIO, and ensure no encoding
        contents = filepath_or_buffer.read()
        try:
            contents = contents.encode(self._encoding)
        except UnicodeEncodeError:
            pass
        self.filepath_or_buffer = BytesIO(contents)

    self._read_header()


# Traceback (AttributeError)
Traceback (most recent call last):
File "after.py", line 15, in <module>
df = pd.read_sas(f, format="xport")
File "/Users/swast/miniconda3/envs/scratch/lib/python3.7/site-packages/pandas/io/sas/sasreader.py", line 70, in read_sas
filepath_or_buffer, index=index, encoding=encoding, chunksize=chunksize
File "/Users/swast/miniconda3/envs/scratch/lib/python3.7/site-packages/pandas/io/sas/sas_xport.py", line 280, in __init__
contents = contents.encode(self._encoding)
AttributeError: 'bytes' object has no attribute 'encode'
(scratch)

# Fixed code
def __init__(
    self, filepath_or_buffer, index=None, encoding="ISO-8859-1", chunksize=None
):
    self._encoding = encoding
    self._lines_read = 0
    self._index = index
    self._chunksize = chunksize

    if isinstance(filepath_or_buffer, str):
        (
            filepath_or_buffer,
            encoding,
            compression,
            should_close,
        ) = get_filepath_or_buffer(filepath_or_buffer, encoding=encoding)

    if isinstance(filepath_or_buffer, (str, bytes)):
        self.filepath_or_buffer = open(filepath_or_buffer, "rb")
    else:
        # Since xport files include non-text byte sequences, xport files
        # should already be opened in binary mode in Python 3.
        self.filepath_or_buffer = filepath_or_buffer

    self._read_header()
# Buggy code
def __setitem__(self, key, value) -> None:
    value = extract_array(value, extract_numpy=True)

    key = check_array_indexer(self, key)
    scalar_key = lib.is_scalar(key)
    scalar_value = lib.is_scalar(value)

    if not scalar_key and scalar_value:
        key = np.asarray(key)

    if not scalar_value:
        value = np.asarray(value, dtype=self._ndarray.dtype)

    self._ndarray[key] = value


# Traceback (IndexError)
In [33]: df = pd.DataFrame({'A': ['a', 'b', 'c']}, dtype='string')

In [34]: df.loc[0:, 'A'] = "test"
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-34-bc65087be9d7> in <module>
----> 1 df.loc[0:, 'A'] = "test"

~/scipy/pandas/pandas/core/indexing.py in __setitem__(self, key, value)
629             key = com.apply_if_callable(key, self.obj)
630         indexer = self._get_setitem_indexer(key)
--> 631         self._setitem_with_indexer(indexer, value)
632
633     def _validate_key(self, key, axis: int):

~/scipy/pandas/pandas/core/indexing.py in _setitem_with_indexer(self, indexer, value)
1014             # actually do the set
1015             self.obj._consolidate_inplace()
-> 1016             self.obj._data = self.obj._data.setitem(indexer=indexer, value=value)
1017             self.obj._maybe_update_cacher(clear=True)
1018

~/scipy/pandas/pandas/core/internals/managers.py in setitem(self, **kwargs)
538
539     def setitem(self, **kwargs):
--> 540         return self.apply("setitem", **kwargs)
541
542     def putmask(self, **kwargs):

~/scipy/pandas/pandas/core/internals/managers.py in apply(self, f, filter, **kwargs)
417                 applied = b.apply(f, **kwargs)
418             else:
--> 419                 applied = getattr(b, f)(**kwargs)
420             result_blocks = _extend_blocks(applied, result_blocks)
421

~/scipy/pandas/pandas/core/internals/blocks.py in setitem(self, indexer, value)
1801
1802         check_setitem_lengths(indexer, value, self.values)
-> 1803         self.values[indexer] = value
1804         return self
1805

~/scipy/pandas/pandas/core/arrays/string_.py in __setitem__(self, key, value)
260                 raise ValueError("Must provide strings.")
261
--> 262         super().__setitem__(key, value)
263
264     def fillna(self, value=None, method=None, limit=None):

~/scipy/pandas/pandas/core/arrays/numpy_.py in __setitem__(self, key, value)
273             value = np.asarray(value, dtype=self._ndarray.dtype)
274
--> 275         self._ndarray[key] = value
276
277     def __len__(self) -> int:

IndexError: arrays used as indices must be of integer (or boolean) type

# Fixed code
def __setitem__(self, key, value) -> None:
    value = extract_array(value, extract_numpy=True)

    key = check_array_indexer(self, key)
    scalar_value = lib.is_scalar(value)

    if not scalar_value:
        value = np.asarray(value, dtype=self._ndarray.dtype)

    self._ndarray[key] = value
# Buggy code
def _execute_insert(self, conn, keys, data_iter):
    data_list = list(data_iter)
    conn.executemany(self.insert_statement(), data_list)


# Traceback (TypeError)
INFO  - ==========================================================================
INFO  - Inserting data one per row.
INFO  - Data inserted successfully!
INFO  - ==========================================================================
INFO  - Inserting data with multirow method.
ERROR - Error while inserting data with multirow method:
Traceback (most recent call last):
File "bug_multirow_sqlite.py", line 52, in store_df_data
df.to_sql("financial_data", con=conn, if_exists="append", index=False, method="multi")
File "C:\Users\jconstanzo\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\generic.py", line 2531, in to_sql
dtype=dtype, method=method)
File "C:\Users\jconstanzo\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\io\sql.py", line 460, in to_sql
chunksize=chunksize, dtype=dtype, method=method)
File "C:\Users\jconstanzo\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\io\sql.py", line 1547, in to_sql
table.insert(chunksize, method)
File "C:\Users\jconstanzo\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\io\sql.py", line 686, in insert
exec_insert(conn, keys, chunk_iter)
File "C:\Users\jconstanzo\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\io\sql.py", line 609, in _execute_insert_multi
conn.execute(self.table.insert(data))
TypeError: insert() takes exactly 2 arguments (1 given)
INFO  - ==========================================================================
INFO  - Inserting data with multirow method and chunksize.
ERROR - Error while inserting data with multirow method and chunksize:
Traceback (most recent call last):
File "bug_multirow_sqlite.py", line 64, in store_df_data
df.to_sql("financial_data", con=conn, if_exists="append", index=False, method="multi", chunksize=10)
File "C:\Users\jconstanzo\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\generic.py", line 2531, in to_sql
dtype=dtype, method=method)
File "C:\Users\jconstanzo\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\io\sql.py", line 460, in to_sql
chunksize=chunksize, dtype=dtype, method=method)
File "C:\Users\jconstanzo\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\io\sql.py", line 1547, in to_sql
table.insert(chunksize, method)
File "C:\Users\jconstanzo\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\io\sql.py", line 686, in insert
exec_insert(conn, keys, chunk_iter)
File "C:\Users\jconstanzo\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\io\sql.py", line 609, in _execute_insert_multi
conn.execute(self.table.insert(data))
TypeError: insert() takes exactly 2 arguments (1 given)

# Fixed code
def _execute_insert(self, conn, keys, data_iter):
    data_list = list(data_iter)
    conn.executemany(self.insert_statement(num_rows=1), data_list)