gurudesh/pytracebugs huggingface dataset:
# Buggy code
def add_prefix(self: FrameOrSeries, prefix: str) -> FrameOrSeries:
    """
    Prefix labels with string `prefix`.

    For Series, the row labels are prefixed.
    For DataFrame, the column labels are prefixed.

    Parameters
    ----------
    prefix : str
        The string to add before each label.

    Returns
    -------
    Series or DataFrame
        New Series or DataFrame with updated labels.

    See Also
    --------
    Series.add_suffix: Suffix row labels with string `suffix`.
    DataFrame.add_suffix: Suffix column labels with string `suffix`.

    Examples
    --------
    >>> s = pd.Series([1, 2, 3, 4])
    >>> s
    0    1
    1    2
    2    3
    3    4
    dtype: int64

    >>> s.add_prefix('item_')
    item_0    1
    item_1    2
    item_2    3
    item_3    4
    dtype: int64

    >>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
    >>> df
       A  B
    0  1  3
    1  2  4
    2  3  5
    3  4  6

    >>> df.add_prefix('col_')
         col_A  col_B
    0       1       3
    1       2       4
    2       3       5
    3       4       6
    """
    f = functools.partial("{prefix}{}".format, prefix=prefix)

    mapper = {self._info_axis_name: f}
    return self.rename(**mapper)


# Traceback (TypeError)
df = pd.DataFrame([[1]])
df.rename({0: 1}, columns={0: 2}, axis=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/williamayd/clones/pandas/pandas/util/_decorators.py", line 235, in wrapper
return func(*args, **kwargs)
File "/Users/williamayd/clones/pandas/pandas/core/frame.py", line 4143, in rename
axes = validate_axis_style_args(self, args, kwargs, "mapper", "rename")
File "/Users/williamayd/clones/pandas/pandas/util/_validators.py", line 287, in validate_axis_style_args
raise TypeError(msg)
TypeError: Cannot specify both 'axis' and any of 'index' or 'columns'.

# Fixed code
def add_prefix(self: FrameOrSeries, prefix: str) -> FrameOrSeries:
    """
    Prefix labels with string `prefix`.

    For Series, the row labels are prefixed.
    For DataFrame, the column labels are prefixed.

    Parameters
    ----------
    prefix : str
        The string to add before each label.

    Returns
    -------
    Series or DataFrame
        New Series or DataFrame with updated labels.

    See Also
    --------
    Series.add_suffix: Suffix row labels with string `suffix`.
    DataFrame.add_suffix: Suffix column labels with string `suffix`.

    Examples
    --------
    >>> s = pd.Series([1, 2, 3, 4])
    >>> s
    0    1
    1    2
    2    3
    3    4
    dtype: int64

    >>> s.add_prefix('item_')
    item_0    1
    item_1    2
    item_2    3
    item_3    4
    dtype: int64

    >>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
    >>> df
       A  B
    0  1  3
    1  2  4
    2  3  5
    3  4  6

    >>> df.add_prefix('col_')
         col_A  col_B
    0       1       3
    1       2       4
    2       3       5
    3       4       6
    """
    f = functools.partial("{prefix}{}".format, prefix=prefix)

    mapper = {self._info_axis_name: f}
    return self.rename(**mapper)  # type: ignore
# Buggy code
def add_suffix(self: FrameOrSeries, suffix: str) -> FrameOrSeries:
    """
    Suffix labels with string `suffix`.

    For Series, the row labels are suffixed.
    For DataFrame, the column labels are suffixed.

    Parameters
    ----------
    suffix : str
        The string to add after each label.

    Returns
    -------
    Series or DataFrame
        New Series or DataFrame with updated labels.

    See Also
    --------
    Series.add_prefix: Prefix row labels with string `prefix`.
    DataFrame.add_prefix: Prefix column labels with string `prefix`.

    Examples
    --------
    >>> s = pd.Series([1, 2, 3, 4])
    >>> s
    0    1
    1    2
    2    3
    3    4
    dtype: int64

    >>> s.add_suffix('_item')
    0_item    1
    1_item    2
    2_item    3
    3_item    4
    dtype: int64

    >>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
    >>> df
       A  B
    0  1  3
    1  2  4
    2  3  5
    3  4  6

    >>> df.add_suffix('_col')
         A_col  B_col
    0       1       3
    1       2       4
    2       3       5
    3       4       6
    """
    f = functools.partial("{}{suffix}".format, suffix=suffix)

    mapper = {self._info_axis_name: f}
    return self.rename(**mapper)


# Traceback (TypeError)
df = pd.DataFrame([[1]])
df.rename({0: 1}, columns={0: 2}, axis=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/williamayd/clones/pandas/pandas/util/_decorators.py", line 235, in wrapper
return func(*args, **kwargs)
File "/Users/williamayd/clones/pandas/pandas/core/frame.py", line 4143, in rename
axes = validate_axis_style_args(self, args, kwargs, "mapper", "rename")
File "/Users/williamayd/clones/pandas/pandas/util/_validators.py", line 287, in validate_axis_style_args
raise TypeError(msg)
TypeError: Cannot specify both 'axis' and any of 'index' or 'columns'.

# Fixed code
def add_suffix(self: FrameOrSeries, suffix: str) -> FrameOrSeries:
    """
    Suffix labels with string `suffix`.

    For Series, the row labels are suffixed.
    For DataFrame, the column labels are suffixed.

    Parameters
    ----------
    suffix : str
        The string to add after each label.

    Returns
    -------
    Series or DataFrame
        New Series or DataFrame with updated labels.

    See Also
    --------
    Series.add_prefix: Prefix row labels with string `prefix`.
    DataFrame.add_prefix: Prefix column labels with string `prefix`.

    Examples
    --------
    >>> s = pd.Series([1, 2, 3, 4])
    >>> s
    0    1
    1    2
    2    3
    3    4
    dtype: int64

    >>> s.add_suffix('_item')
    0_item    1
    1_item    2
    2_item    3
    3_item    4
    dtype: int64

    >>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
    >>> df
       A  B
    0  1  3
    1  2  4
    2  3  5
    3  4  6

    >>> df.add_suffix('_col')
         A_col  B_col
    0       1       3
    1       2       4
    2       3       5
    3       4       6
    """
    f = functools.partial("{}{suffix}".format, suffix=suffix)

    mapper = {self._info_axis_name: f}
    return self.rename(**mapper)  # type: ignore
# Buggy code
def argsort(self, axis=0, kind="quicksort", order=None):
    """
    Override ndarray.argsort. Argsorts the value, omitting NA/null values,
    and places the result in the same locations as the non-NA values.

    Parameters
    ----------
    axis : int
        Has no effect but is accepted for compatibility with numpy.
    kind : {'mergesort', 'quicksort', 'heapsort'}, default 'quicksort'
        Choice of sorting algorithm. See np.sort for more
        information. 'mergesort' is the only stable algorithm.
    order : None
        Has no effect but is accepted for compatibility with numpy.

    Returns
    -------
    Series
        Positions of values within the sort order with -1 indicating
        nan values.

    See Also
    --------
    numpy.ndarray.argsort
    """
    values = self._values
    mask = isna(values)

    if mask.any():
        result = Series(-1, index=self.index, name=self.name, dtype="int64")
        notmask = ~mask
        result[notmask] = np.argsort(values[notmask], kind=kind)
        return self._constructor(result, index=self.index).__finalize__(self)
    else:
        return self._constructor(
            np.argsort(values, kind=kind), index=self.index, dtype="int64"
        ).__finalize__(self)


# Traceback (TypeError)
df = pd.DataFrame([[1]])
df.rename({0: 1}, columns={0: 2}, axis=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/williamayd/clones/pandas/pandas/util/_decorators.py", line 235, in wrapper
return func(*args, **kwargs)
File "/Users/williamayd/clones/pandas/pandas/core/frame.py", line 4143, in rename
axes = validate_axis_style_args(self, args, kwargs, "mapper", "rename")
File "/Users/williamayd/clones/pandas/pandas/util/_validators.py", line 287, in validate_axis_style_args
raise TypeError(msg)
TypeError: Cannot specify both 'axis' and any of 'index' or 'columns'.

# Fixed code
def argsort(self, axis=0, kind="quicksort", order=None):
    """
    Override ndarray.argsort. Argsorts the value, omitting NA/null values,
    and places the result in the same locations as the non-NA values.

    Parameters
    ----------
    axis : {0 or "index"}
        Has no effect but is accepted for compatibility with numpy.
    kind : {'mergesort', 'quicksort', 'heapsort'}, default 'quicksort'
        Choice of sorting algorithm. See np.sort for more
        information. 'mergesort' is the only stable algorithm.
    order : None
        Has no effect but is accepted for compatibility with numpy.

    Returns
    -------
    Series
        Positions of values within the sort order with -1 indicating
        nan values.

    See Also
    --------
    numpy.ndarray.argsort
    """
    values = self._values
    mask = isna(values)

    if mask.any():
        result = Series(-1, index=self.index, name=self.name, dtype="int64")
        notmask = ~mask
        result[notmask] = np.argsort(values[notmask], kind=kind)
        return self._constructor(result, index=self.index).__finalize__(self)
    else:
        return self._constructor(
            np.argsort(values, kind=kind), index=self.index, dtype="int64"
        ).__finalize__(self)
# Buggy code
def __init__(self, f: BinaryIO, encoding: str):
    self.reader = codecs.getreader(encoding)(f)


# Traceback (UnicodeDecodeError)
In [4]: with tempfile.TemporaryFile(mode='w+', encoding='utf16') as outfile:
...:     outfile.write('foo')
...:     outfile.seek(0)
...:     pd.read_csv(outfile, encoding='utf16')
UnicodeDecodeError: 'utf-16-le' codec can't decode byte 0x6f in position 2: truncated data

In [4]: with tempfile.TemporaryFile(mode='w+', encoding='utf32') as outfile:
...:     outfile.write('foo')
...:     outfile.seek(0)
...:     pd.read_csv(outfile, encoding='utf32')
UnicodeDecodeError: 'utf-32-le' codec can't decode bytes in position 0-2: truncated data

# Fixed code
def __init__(self, f: IO):
    self.mmap = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)
# Buggy code
def __next__(self) -> bytes:
    return next(self.reader).encode("utf-8")


# Traceback (UnicodeDecodeError)
In [4]: with tempfile.TemporaryFile(mode='w+', encoding='utf16') as outfile:
...:     outfile.write('foo')
...:     outfile.seek(0)
...:     pd.read_csv(outfile, encoding='utf16')
UnicodeDecodeError: 'utf-16-le' codec can't decode byte 0x6f in position 2: truncated data

In [4]: with tempfile.TemporaryFile(mode='w+', encoding='utf32') as outfile:
...:     outfile.write('foo')
...:     outfile.seek(0)
...:     pd.read_csv(outfile, encoding='utf32')
UnicodeDecodeError: 'utf-32-le' codec can't decode bytes in position 0-2: truncated data

# Fixed code
def __next__(self) -> str:
    newbytes = self.mmap.readline()

    # readline returns bytes, not str, but Python's CSV reader
    # expects str, so convert the output to str before continuing
    newline = newbytes.decode("utf-8")

    # mmap doesn't raise if reading past the allocated
    # data but instead returns an empty string, so raise
    # if that is returned
    if newline == "":
        raise StopIteration
    return newline
# Buggy code
def pct_change(self, periods=1, fill_method="pad", limit=None, freq=None, **kwargs):
    # TODO: Not sure if above is correct - need someone to confirm.
    axis = self._get_axis_number(kwargs.pop("axis", self._stat_axis_name))
    if fill_method is None:
        data = self
    else:
        data = self.fillna(method=fill_method, limit=limit, axis=axis)

    rs = data.div(data.shift(periods=periods, freq=freq, axis=axis, **kwargs)) - 1
    rs = rs.loc[~rs.index.duplicated()]
    rs = rs.reindex_like(data)
    if freq is None:
        mask = isna(com.values_from_object(data))
        np.putmask(rs.values, mask, np.nan)
    return rs


# Traceback (TypeError)
data.reset_index().groupby('index')[0].pct_change(1, fill_method=None, limit=1)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-18-60898304743e> in <module>
----> 1 data.reset_index().groupby('index')[0].pct_change(1, fill_method=None, limit=1)

~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/generic.py in pct_change(self, periods, fill_method, limit, freq)
1344                 )
1345             )
-> 1346         filled = getattr(self, fill_method)(limit=limit)
1347         fill_grp = filled.groupby(self.grouper.labels)
1348         shifted = fill_grp.shift(periods=periods, freq=freq)

TypeError: getattr(): attribute name must be string

# Fixed code
def pct_change(self, periods=1, fill_method="pad", limit=None, freq=None, **kwargs):
    # TODO: Not sure if above is correct - need someone to confirm.
    axis = self._get_axis_number(kwargs.pop("axis", self._stat_axis_name))
    if fill_method is None:
        data = self
    else:
        data = self.fillna(method=fill_method, limit=limit, axis=axis)

    rs = data.div(data.shift(periods=periods, freq=freq, axis=axis, **kwargs)) - 1
    if freq is not None:
        # Shift method is implemented differently when freq is not None
        # We want to restore the original index
        rs = rs.loc[~rs.index.duplicated()]
        rs = rs.reindex_like(data)
    return rs
# Buggy code
def _simple_new(cls, values, name=None, dtype=None):
    """
    We require that we have a dtype compat for the values. If we are passed
    a non-dtype compat, then coerce using the constructor.

    Must be careful not to recurse.
    """
    if isinstance(values, (ABCSeries, ABCIndexClass)):
        # Index._data must always be an ndarray.
        # This is no-copy for when _values is an ndarray,
        # which should be always at this point.
        values = np.asarray(values._values)

    result = object.__new__(cls)
    result._data = values
    # _index_data is a (temporary?) fix to ensure that the direct data
    # manipulation we do in `_libs/reduction.pyx` continues to work.
    # We need access to the actual ndarray, since we're messing with
    # data buffers and strides. We don't re-use `_ndarray_values`, since
    # we actually set this value too.
    result._index_data = values
    result.name = name

    return result._reset_identity()


# Traceback (TypeError)
In [5]: pd.Series([], name=[])
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-5-2c73ddde103e> in <module>
----> 1 pd.Series([], name=[])

~/sandbox/pandas/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
326         generic.NDFrame.__init__(self, data, fastpath=True)
327
--> 328         self.name = name
329         self._set_axis(0, index, fastpath=True)
330

~/sandbox/pandas/pandas/core/generic.py in __setattr__(self, name, value)
5257             object.__setattr__(self, name, value)
5258         elif name in self._metadata:
-> 5259             object.__setattr__(self, name, value)
5260         else:
5261             try:

~/sandbox/pandas/pandas/core/series.py in name(self, value)
468     def name(self, value):
469         if value is not None and not is_hashable(value):
--> 470             raise TypeError("Series.name must be a hashable type")
471         object.__setattr__(self, "_name", value)
472

TypeError: Series.name must be a hashable type

# Fixed code
def _simple_new(cls, values, name=None, dtype=None):
    """
    We require that we have a dtype compat for the values. If we are passed
    a non-dtype compat, then coerce using the constructor.

    Must be careful not to recurse.
    """
    if isinstance(values, (ABCSeries, ABCIndexClass)):
        # Index._data must always be an ndarray.
        # This is no-copy for when _values is an ndarray,
        # which should be always at this point.
        values = np.asarray(values._values)

    result = object.__new__(cls)
    result._data = values
    # _index_data is a (temporary?) fix to ensure that the direct data
    # manipulation we do in `_libs/reduction.pyx` continues to work.
    # We need access to the actual ndarray, since we're messing with
    # data buffers and strides. We don't re-use `_ndarray_values`, since
    # we actually set this value too.
    result._index_data = values
    result._name = name

    return result._reset_identity()
# Buggy code
def _set_names(self, values, level=None):
    """
    Set new names on index. Each name has to be a hashable type.

    Parameters
    ----------
    values : str or sequence
        name(s) to set
    level : int, level name, or sequence of int/level names (default None)
        If the index is a MultiIndex (hierarchical), level(s) to set (None
        for all levels).  Otherwise level must be None

    Raises
    ------
    TypeError if each name is not hashable.
    """
    if not is_list_like(values):
        raise ValueError("Names must be a list-like")
    if len(values) != 1:
        raise ValueError(f"Length of new names must be 1, got {len(values)}")

    # GH 20527
    # All items in 'name' need to be hashable:
    for name in values:
        if not is_hashable(name):
            raise TypeError(f"{type(self).__name__}.name must be a hashable type")
    self.name = values[0]


# Traceback (TypeError)
In [5]: pd.Series([], name=[])
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-5-2c73ddde103e> in <module>
----> 1 pd.Series([], name=[])

~/sandbox/pandas/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
326         generic.NDFrame.__init__(self, data, fastpath=True)
327
--> 328         self.name = name
329         self._set_axis(0, index, fastpath=True)
330

~/sandbox/pandas/pandas/core/generic.py in __setattr__(self, name, value)
5257             object.__setattr__(self, name, value)
5258         elif name in self._metadata:
-> 5259             object.__setattr__(self, name, value)
5260         else:
5261             try:

~/sandbox/pandas/pandas/core/series.py in name(self, value)
468     def name(self, value):
469         if value is not None and not is_hashable(value):
--> 470             raise TypeError("Series.name must be a hashable type")
471         object.__setattr__(self, "_name", value)
472

TypeError: Series.name must be a hashable type

# Fixed code
def _set_names(self, values, level=None):
    """
    Set new names on index. Each name has to be a hashable type.

    Parameters
    ----------
    values : str or sequence
        name(s) to set
    level : int, level name, or sequence of int/level names (default None)
        If the index is a MultiIndex (hierarchical), level(s) to set (None
        for all levels).  Otherwise level must be None

    Raises
    ------
    TypeError if each name is not hashable.
    """
    if not is_list_like(values):
        raise ValueError("Names must be a list-like")
    if len(values) != 1:
        raise ValueError(f"Length of new names must be 1, got {len(values)}")

    # GH 20527
    # All items in 'name' need to be hashable:
    for name in values:
        if not is_hashable(name):
            raise TypeError(f"{type(self).__name__}.name must be a hashable type")
    self._name = values[0]
# Buggy code
def __setstate__(self, state):
    """
    Necessary for making this object picklable.
    """

    if isinstance(state, dict):
        self._data = state.pop("data")
        for k, v in state.items():
            setattr(self, k, v)

    elif isinstance(state, tuple):
        if len(state) == 2:
            nd_state, own_state = state
            data = np.empty(nd_state[1], dtype=nd_state[2])
            np.ndarray.__setstate__(data, nd_state)
            self.name = own_state[0]

        else:  # pragma: no cover
            data = np.empty(state)
            np.ndarray.__setstate__(data, state)

        self._data = data
        self._reset_identity()
    else:
        raise Exception("invalid pickle state")


# Traceback (TypeError)
In [5]: pd.Series([], name=[])
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-5-2c73ddde103e> in <module>
----> 1 pd.Series([], name=[])

~/sandbox/pandas/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
326         generic.NDFrame.__init__(self, data, fastpath=True)
327
--> 328         self.name = name
329         self._set_axis(0, index, fastpath=True)
330

~/sandbox/pandas/pandas/core/generic.py in __setattr__(self, name, value)
5257             object.__setattr__(self, name, value)
5258         elif name in self._metadata:
-> 5259             object.__setattr__(self, name, value)
5260         else:
5261             try:

~/sandbox/pandas/pandas/core/series.py in name(self, value)
468     def name(self, value):
469         if value is not None and not is_hashable(value):
--> 470             raise TypeError("Series.name must be a hashable type")
471         object.__setattr__(self, "_name", value)
472

TypeError: Series.name must be a hashable type

# Fixed code
def __setstate__(self, state):
    """
    Necessary for making this object picklable.
    """

    if isinstance(state, dict):
        self._data = state.pop("data")
        for k, v in state.items():
            setattr(self, k, v)

    elif isinstance(state, tuple):
        if len(state) == 2:
            nd_state, own_state = state
            data = np.empty(nd_state[1], dtype=nd_state[2])
            np.ndarray.__setstate__(data, nd_state)
            self._name = own_state[0]

        else:  # pragma: no cover
            data = np.empty(state)
            np.ndarray.__setstate__(data, state)

        self._data = data
        self._reset_identity()
    else:
        raise Exception("invalid pickle state")
# Buggy code
def __new__(
    cls,
    data=None,
    categories=None,
    ordered=None,
    dtype=None,
    copy=False,
    name=None,
):
    dtype = CategoricalDtype._from_values_or_dtype(data, categories, ordered, dtype)

    if name is None and hasattr(data, "name"):
        name = data.name

    if not is_categorical_dtype(data):
        # don't allow scalars
        # if data is None, then categories must be provided
        if is_scalar(data):
            if data is not None or categories is None:
                raise cls._scalar_data_error(data)
            data = []

    data = cls._create_categorical(data, dtype=dtype)

    data = data.copy() if copy else data

    return cls._simple_new(data, name=name)


# Traceback (TypeError)
In [5]: pd.Series([], name=[])
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-5-2c73ddde103e> in <module>
----> 1 pd.Series([], name=[])

~/sandbox/pandas/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
326         generic.NDFrame.__init__(self, data, fastpath=True)
327
--> 328         self.name = name
329         self._set_axis(0, index, fastpath=True)
330

~/sandbox/pandas/pandas/core/generic.py in __setattr__(self, name, value)
5257             object.__setattr__(self, name, value)
5258         elif name in self._metadata:
-> 5259             object.__setattr__(self, name, value)
5260         else:
5261             try:

~/sandbox/pandas/pandas/core/series.py in name(self, value)
468     def name(self, value):
469         if value is not None and not is_hashable(value):
--> 470             raise TypeError("Series.name must be a hashable type")
471         object.__setattr__(self, "_name", value)
472

TypeError: Series.name must be a hashable type

# Fixed code
def __new__(
    cls,
    data=None,
    categories=None,
    ordered=None,
    dtype=None,
    copy=False,
    name=None,
):
    dtype = CategoricalDtype._from_values_or_dtype(data, categories, ordered, dtype)

    name = maybe_extract_name(name, data, cls)

    if not is_categorical_dtype(data):
        # don't allow scalars
        # if data is None, then categories must be provided
        if is_scalar(data):
            if data is not None or categories is None:
                raise cls._scalar_data_error(data)
            data = []

    data = cls._create_categorical(data, dtype=dtype)

    data = data.copy() if copy else data

    return cls._simple_new(data, name=name)
# Buggy code
def __new__(
    cls,
    data=None,
    freq=None,
    tz=None,
    normalize=False,
    closed=None,
    ambiguous="raise",
    dayfirst=False,
    yearfirst=False,
    dtype=None,
    copy=False,
    name=None,
):
    if is_scalar(data):
        raise TypeError(
            f"{cls.__name__}() must be called with a "
            f"collection of some kind, {repr(data)} was passed"
        )

    # - Cases checked above all return/raise before reaching here - #

    if name is None and hasattr(data, "name"):
        name = data.name

    dtarr = DatetimeArray._from_sequence(
        data,
        dtype=dtype,
        copy=copy,
        tz=tz,
        freq=freq,
        dayfirst=dayfirst,
        yearfirst=yearfirst,
        ambiguous=ambiguous,
    )

    subarr = cls._simple_new(dtarr, name=name, freq=dtarr.freq, tz=dtarr.tz)
    return subarr


# Traceback (TypeError)
In [5]: pd.Series([], name=[])
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-5-2c73ddde103e> in <module>
----> 1 pd.Series([], name=[])

~/sandbox/pandas/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
326         generic.NDFrame.__init__(self, data, fastpath=True)
327
--> 328         self.name = name
329         self._set_axis(0, index, fastpath=True)
330

~/sandbox/pandas/pandas/core/generic.py in __setattr__(self, name, value)
5257             object.__setattr__(self, name, value)
5258         elif name in self._metadata:
-> 5259             object.__setattr__(self, name, value)
5260         else:
5261             try:

~/sandbox/pandas/pandas/core/series.py in name(self, value)
468     def name(self, value):
469         if value is not None and not is_hashable(value):
--> 470             raise TypeError("Series.name must be a hashable type")
471         object.__setattr__(self, "_name", value)
472

TypeError: Series.name must be a hashable type

# Fixed code
def __new__(
    cls,
    data=None,
    freq=None,
    tz=None,
    normalize=False,
    closed=None,
    ambiguous="raise",
    dayfirst=False,
    yearfirst=False,
    dtype=None,
    copy=False,
    name=None,
):
    if is_scalar(data):
        raise TypeError(
            f"{cls.__name__}() must be called with a "
            f"collection of some kind, {repr(data)} was passed"
        )

    # - Cases checked above all return/raise before reaching here - #

    name = maybe_extract_name(name, data, cls)

    dtarr = DatetimeArray._from_sequence(
        data,
        dtype=dtype,
        copy=copy,
        tz=tz,
        freq=freq,
        dayfirst=dayfirst,
        yearfirst=yearfirst,
        ambiguous=ambiguous,
    )

    subarr = cls._simple_new(dtarr, name=name, freq=dtarr.freq, tz=dtarr.tz)
    return subarr
# Buggy code
def __new__(
    cls,
    data,
    closed=None,
    dtype=None,
    copy: bool = False,
    name=None,
    verify_integrity: bool = True,
):
    if name is None and hasattr(data, "name"):
        name = data.name

    with rewrite_exception("IntervalArray", cls.__name__):
        array = IntervalArray(
            data,
            closed=closed,
            copy=copy,
            dtype=dtype,
            verify_integrity=verify_integrity,
        )

    return cls._simple_new(array, name)


# Traceback (TypeError)
In [5]: pd.Series([], name=[])
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-5-2c73ddde103e> in <module>
----> 1 pd.Series([], name=[])

~/sandbox/pandas/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
326         generic.NDFrame.__init__(self, data, fastpath=True)
327
--> 328         self.name = name
329         self._set_axis(0, index, fastpath=True)
330

~/sandbox/pandas/pandas/core/generic.py in __setattr__(self, name, value)
5257             object.__setattr__(self, name, value)
5258         elif name in self._metadata:
-> 5259             object.__setattr__(self, name, value)
5260         else:
5261             try:

~/sandbox/pandas/pandas/core/series.py in name(self, value)
468     def name(self, value):
469         if value is not None and not is_hashable(value):
--> 470             raise TypeError("Series.name must be a hashable type")
471         object.__setattr__(self, "_name", value)
472

TypeError: Series.name must be a hashable type

# Fixed code
def __new__(
    cls,
    data,
    closed=None,
    dtype=None,
    copy: bool = False,
    name=None,
    verify_integrity: bool = True,
):
    name = maybe_extract_name(name, data, cls)

    with rewrite_exception("IntervalArray", cls.__name__):
        array = IntervalArray(
            data,
            closed=closed,
            copy=copy,
            dtype=dtype,
            verify_integrity=verify_integrity,
        )

    return cls._simple_new(array, name)
# Buggy code
def __new__(cls, data=None, dtype=None, copy=False, name=None):
    cls._validate_dtype(dtype)

    # Coerce to ndarray if not already ndarray or Index
    if not isinstance(data, (np.ndarray, Index)):
        if is_scalar(data):
            raise cls._scalar_data_error(data)

        # other iterable of some kind
        if not isinstance(data, (ABCSeries, list, tuple)):
            data = list(data)

        data = np.asarray(data, dtype=dtype)

    if issubclass(data.dtype.type, str):
        cls._string_data_error(data)

    if copy or not is_dtype_equal(data.dtype, cls._default_dtype):
        subarr = np.array(data, dtype=cls._default_dtype, copy=copy)
        cls._assert_safe_casting(data, subarr)
    else:
        subarr = data

    if name is None and hasattr(data, "name"):
        name = data.name
    return cls._simple_new(subarr, name=name)


# Traceback (TypeError)
In [5]: pd.Series([], name=[])
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-5-2c73ddde103e> in <module>
----> 1 pd.Series([], name=[])

~/sandbox/pandas/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
326         generic.NDFrame.__init__(self, data, fastpath=True)
327
--> 328         self.name = name
329         self._set_axis(0, index, fastpath=True)
330

~/sandbox/pandas/pandas/core/generic.py in __setattr__(self, name, value)
5257             object.__setattr__(self, name, value)
5258         elif name in self._metadata:
-> 5259             object.__setattr__(self, name, value)
5260         else:
5261             try:

~/sandbox/pandas/pandas/core/series.py in name(self, value)
468     def name(self, value):
469         if value is not None and not is_hashable(value):
--> 470             raise TypeError("Series.name must be a hashable type")
471         object.__setattr__(self, "_name", value)
472

TypeError: Series.name must be a hashable type

# Fixed code
def __new__(cls, data=None, dtype=None, copy=False, name=None):
    cls._validate_dtype(dtype)

    # Coerce to ndarray if not already ndarray or Index
    if not isinstance(data, (np.ndarray, Index)):
        if is_scalar(data):
            raise cls._scalar_data_error(data)

        # other iterable of some kind
        if not isinstance(data, (ABCSeries, list, tuple)):
            data = list(data)

        data = np.asarray(data, dtype=dtype)

    if issubclass(data.dtype.type, str):
        cls._string_data_error(data)

    if copy or not is_dtype_equal(data.dtype, cls._default_dtype):
        subarr = np.array(data, dtype=cls._default_dtype, copy=copy)
        cls._assert_safe_casting(data, subarr)
    else:
        subarr = data

    name = maybe_extract_name(name, data, cls)
    return cls._simple_new(subarr, name=name)
# Buggy code
def __new__(
    cls,
    start=None,
    stop=None,
    step=None,
    dtype=None,
    copy=False,
    name=None,
):
    cls._validate_dtype(dtype)

    # RangeIndex
    if isinstance(start, RangeIndex):
        name = start.name if name is None else name
        start = start._range
        return cls._simple_new(start, dtype=dtype, name=name)

    # validate the arguments
    if com.all_none(start, stop, step):
        raise TypeError("RangeIndex(...) must be called with integers")

    start = ensure_python_int(start) if start is not None else 0

    if stop is None:
        start, stop = 0, start
    else:
        stop = ensure_python_int(stop)

    step = ensure_python_int(step) if step is not None else 1
    if step == 0:
        raise ValueError("Step must not be zero")

    rng = range(start, stop, step)
    return cls._simple_new(rng, dtype=dtype, name=name)


# Traceback (TypeError)
In [5]: pd.Series([], name=[])
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-5-2c73ddde103e> in <module>
----> 1 pd.Series([], name=[])

~/sandbox/pandas/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
326         generic.NDFrame.__init__(self, data, fastpath=True)
327
--> 328         self.name = name
329         self._set_axis(0, index, fastpath=True)
330

~/sandbox/pandas/pandas/core/generic.py in __setattr__(self, name, value)
5257             object.__setattr__(self, name, value)
5258         elif name in self._metadata:
-> 5259             object.__setattr__(self, name, value)
5260         else:
5261             try:

~/sandbox/pandas/pandas/core/series.py in name(self, value)
468     def name(self, value):
469         if value is not None and not is_hashable(value):
--> 470             raise TypeError("Series.name must be a hashable type")
471         object.__setattr__(self, "_name", value)
472

TypeError: Series.name must be a hashable type

# Fixed code
def __new__(
    cls,
    start=None,
    stop=None,
    step=None,
    dtype=None,
    copy=False,
    name=None,
):
    cls._validate_dtype(dtype)
    name = maybe_extract_name(name, start, cls)

    # RangeIndex
    if isinstance(start, RangeIndex):
        start = start._range
        return cls._simple_new(start, dtype=dtype, name=name)

    # validate the arguments
    if com.all_none(start, stop, step):
        raise TypeError("RangeIndex(...) must be called with integers")

    start = ensure_python_int(start) if start is not None else 0

    if stop is None:
        start, stop = 0, start
    else:
        stop = ensure_python_int(stop)

    step = ensure_python_int(step) if step is not None else 1
    if step == 0:
        raise ValueError("Step must not be zero")

    rng = range(start, stop, step)
    return cls._simple_new(rng, dtype=dtype, name=name)
# Buggy code
def _simple_new(cls, values, name=None, freq=None, dtype=_TD_DTYPE):
    # `dtype` is passed by _shallow_copy in corner cases, should always
    #  be timedelta64[ns] if present
    if not isinstance(values, TimedeltaArray):
        values = TimedeltaArray._simple_new(values, dtype=dtype, freq=freq)
    else:
        if freq is None:
            freq = values.freq
    assert isinstance(values, TimedeltaArray), type(values)
    assert dtype == _TD_DTYPE, dtype
    assert values.dtype == "m8[ns]", values.dtype

    tdarr = TimedeltaArray._simple_new(values._data, freq=freq)
    result = object.__new__(cls)
    result._data = tdarr
    result.name = name
    # For groupby perf. See note in indexes/base about _index_data
    result._index_data = tdarr._data

    result._reset_identity()
    return result


# Traceback (TypeError)
In [5]: pd.Series([], name=[])
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-5-2c73ddde103e> in <module>
----> 1 pd.Series([], name=[])

~/sandbox/pandas/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
326         generic.NDFrame.__init__(self, data, fastpath=True)
327
--> 328         self.name = name
329         self._set_axis(0, index, fastpath=True)
330

~/sandbox/pandas/pandas/core/generic.py in __setattr__(self, name, value)
5257             object.__setattr__(self, name, value)
5258         elif name in self._metadata:
-> 5259             object.__setattr__(self, name, value)
5260         else:
5261             try:

~/sandbox/pandas/pandas/core/series.py in name(self, value)
468     def name(self, value):
469         if value is not None and not is_hashable(value):
--> 470             raise TypeError("Series.name must be a hashable type")
471         object.__setattr__(self, "_name", value)
472

TypeError: Series.name must be a hashable type

# Fixed code
def _simple_new(cls, values, name=None, freq=None, dtype=_TD_DTYPE):
    # `dtype` is passed by _shallow_copy in corner cases, should always
    #  be timedelta64[ns] if present
    if not isinstance(values, TimedeltaArray):
        values = TimedeltaArray._simple_new(values, dtype=dtype, freq=freq)
    else:
        if freq is None:
            freq = values.freq
    assert isinstance(values, TimedeltaArray), type(values)
    assert dtype == _TD_DTYPE, dtype
    assert values.dtype == "m8[ns]", values.dtype

    tdarr = TimedeltaArray._simple_new(values._data, freq=freq)
    result = object.__new__(cls)
    result._data = tdarr
    result._name = name
    # For groupby perf. See note in indexes/base about _index_data
    result._index_data = tdarr._data

    result._reset_identity()
    return result
# Buggy code
def recons_codes(self):
    comp_ids, obs_ids, _ = self.group_info
    codes = (ping.codes for ping in self.groupings)
    return decons_obs_group_ids(comp_ids, obs_ids, self.shape, codes, xnull=True)


# Traceback (ValueError)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-543-5efc1c882109> in <module>
14 [print(g) for g in dfg]
15 print('This will cause crash:')
---> 16 display(dfg['Food'].value_counts())

~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/generic.py in value_counts(self, normalize, sort, ascending, bins, dropna)
1137
1138         # multi-index components
-> 1139         labels = list(map(rep, self.grouper.recons_labels)) + [llab(lab, inc)]
1140         levels = [ping.group_index for ping in self.grouper.groupings] + [lev]
1141         names = self.grouper.names + [self._selection_name]

~/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py in repeat(a, repeats, axis)
469
470     """
--> 471     return _wrapfunc(a, 'repeat', repeats, axis=axis)
472
473

~/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py in _wrapfunc(obj, method, *args, **kwds)
54 def _wrapfunc(obj, method, *args, **kwds):
55     try:
---> 56         return getattr(obj, method)(*args, **kwds)
57
58     # An AttributeError occurs if the object does not have

ValueError: operands could not be broadcast together with shape (5,) (4,)

# Fixed code
def recons_codes(self):
    # get unique result indices, and prepend 0 as groupby starts from the first
    return [np.r_[0, np.flatnonzero(self.bins[1:] != self.bins[:-1]) + 1]]
# Buggy code
def _get_colors():
    #  num_colors=3 is required as method maybe_color_bp takes the colors
    #  in positions 0 and 2.
    return _get_standard_colors(color=kwds.get("color"), num_colors=3)


# Traceback (TypeError)
Traceback (most recent call last):
File "/Users/BNL28/Code/DataPerformance/bug_report.py", line 33, in <module>
comparative_results()
File "/Users/BNL28/Code/DataPerformance/bug_report.py", line 26, in comparative_results
ax = draw_plot(ax, df1, 'k')
File "/Users/BNL28/Code/DataPerformance/bug_report.py", line 22, in draw_plot
ax = data.boxplot(column=['x'], by=['z'], showfliers=False, ax=ax, color=colors)
File "/Users/BNL28/anaconda3/lib/python3.6/site-packages/pandas/plotting/_core.py", line 2254, in boxplot_frame
return_type=return_type, **kwds)
File "/Users/BNL28/anaconda3/lib/python3.6/site-packages/pandas/plotting/_core.py", line 2223, in boxplot
return_type=return_type)
File "/Users/BNL28/anaconda3/lib/python3.6/site-packages/pandas/plotting/_core.py", line 2683, in _grouped_plot_by_column
re_plotf = plotf(keys, values, ax, **kwargs)
File "/Users/BNL28/anaconda3/lib/python3.6/site-packages/pandas/plotting/_core.py", line 2191, in plot_group
bp = ax.boxplot(values, **kwds)
File "/Users/BNL28/anaconda3/lib/python3.6/site-packages/matplotlib/__init__.py", line 1810, in inner
return func(ax, *args, **kwargs)
TypeError: boxplot() got an unexpected keyword argument 'color'

Process finished with exit code 1

# Fixed code
def _get_colors():
    #  num_colors=3 is required as method maybe_color_bp takes the colors
    #  in positions 0 and 2.
    #  if colors not provided, use same defaults as DataFrame.plot.box
    result = _get_standard_colors(num_colors=3)
    result = np.take(result, [0, 0, 2])
    result = np.append(result, "k")

    colors = kwds.pop("color", None)
    if colors:
        if is_dict_like(colors):
            # replace colors in result array with user-specified colors
            # taken from the colors dict parameter
            # "boxes" value placed in position 0, "whiskers" in 1, etc.
            valid_keys = ["boxes", "whiskers", "medians", "caps"]
            key_to_index = dict(zip(valid_keys, range(4)))
            for key, value in colors.items():
                if key in valid_keys:
                    result[key_to_index[key]] = value
                else:
                    raise ValueError(
                        "color dict contains invalid "
                        "key '{0}' "
                        "The key must be either {1}".format(key, valid_keys)
                    )
        else:
            result.fill(colors)

    return result
# Buggy code
def maybe_color_bp(bp):
    if "color" not in kwds:
        setp(bp["boxes"], color=colors[0], alpha=1)
        setp(bp["whiskers"], color=colors[0], alpha=1)
        setp(bp["medians"], color=colors[2], alpha=1)


# Traceback (TypeError)
Traceback (most recent call last):
File "/Users/BNL28/Code/DataPerformance/bug_report.py", line 33, in <module>
comparative_results()
File "/Users/BNL28/Code/DataPerformance/bug_report.py", line 26, in comparative_results
ax = draw_plot(ax, df1, 'k')
File "/Users/BNL28/Code/DataPerformance/bug_report.py", line 22, in draw_plot
ax = data.boxplot(column=['x'], by=['z'], showfliers=False, ax=ax, color=colors)
File "/Users/BNL28/anaconda3/lib/python3.6/site-packages/pandas/plotting/_core.py", line 2254, in boxplot_frame
return_type=return_type, **kwds)
File "/Users/BNL28/anaconda3/lib/python3.6/site-packages/pandas/plotting/_core.py", line 2223, in boxplot
return_type=return_type)
File "/Users/BNL28/anaconda3/lib/python3.6/site-packages/pandas/plotting/_core.py", line 2683, in _grouped_plot_by_column
re_plotf = plotf(keys, values, ax, **kwargs)
File "/Users/BNL28/anaconda3/lib/python3.6/site-packages/pandas/plotting/_core.py", line 2191, in plot_group
bp = ax.boxplot(values, **kwds)
File "/Users/BNL28/anaconda3/lib/python3.6/site-packages/matplotlib/__init__.py", line 1810, in inner
return func(ax, *args, **kwargs)
TypeError: boxplot() got an unexpected keyword argument 'color'

Process finished with exit code 1

# Fixed code
def maybe_color_bp(bp):
    setp(bp["boxes"], color=colors[0], alpha=1)
    setp(bp["whiskers"], color=colors[1], alpha=1)
    setp(bp["medians"], color=colors[2], alpha=1)
    setp(bp["caps"], color=colors[3], alpha=1)
# Buggy code
def __setitem__(self, key, value):
    value = extract_array(value, extract_numpy=True)

    if not lib.is_scalar(key) and is_list_like(key):
        key = np.asarray(key)

    if not lib.is_scalar(value):
        value = np.asarray(value)

    values = self._ndarray
    t = np.result_type(value, values)
    if t != self._ndarray.dtype:
        values = values.astype(t, casting="safe")
        values[key] = value
        self._dtype = PandasDtype(t)
        self._ndarray = values
    else:
        self._ndarray[key] = value


# Traceback (TypeError)
In [3]: t = pd.array(['a', 'b', 'c'])

In [4]: t[0] = 't'
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-4-7d1c8d6d2e6a> in <module>
----> 1 t[0] = 't'

~/sandbox/pandas/pandas/core/arrays/numpy_.py in __setitem__(self, key, value)
237
238         values = self._ndarray
--> 239         t = np.result_type(value, values)
240         if t != self._ndarray.dtype:
241             values = values.astype(t, casting="safe")

<__array_function__ internals> in result_type(*args, **kwargs)

TypeError: data type "t" not understood

# Fixed code
def __setitem__(self, key, value):
    value = extract_array(value, extract_numpy=True)

    if not lib.is_scalar(key) and is_list_like(key):
        key = np.asarray(key)

    if not lib.is_scalar(value):
        value = np.asarray(value)

    value = np.asarray(value, dtype=self._ndarray.dtype)
    self._ndarray[key] = value
# Buggy code
def length_of_indexer(indexer, target=None) -> int:
    """
    return the length of a single non-tuple indexer which could be a slice
    """
    if target is not None and isinstance(indexer, slice):
        target_len = len(target)
        start = indexer.start
        stop = indexer.stop
        step = indexer.step
        if start is None:
            start = 0
        elif start < 0:
            start += target_len
        if stop is None or stop > target_len:
            stop = target_len
        elif stop < 0:
            stop += target_len
        if step is None:
            step = 1
        elif step < 0:
            step = -step
        return (stop - start + step - 1) // step
    elif isinstance(indexer, (ABCSeries, ABCIndexClass, np.ndarray, list)):
        return len(indexer)
    elif not is_list_like_indexer(indexer):
        return 1
    raise AssertionError("cannot find the length of the indexer")


# Traceback (ValueError)
import pandas as pd
s = pd.Series(index=range(2010, 2020))
s.loc[2015:2010:-1] = [6, 5, 4, 3, 2, 1]
Traceback (most recent call last):
[...]
ValueError: cannot set using a slice indexer with a different length than the value

# Fixed code
def length_of_indexer(indexer, target=None) -> int:
    """
    return the length of a single non-tuple indexer which could be a slice
    """
    if target is not None and isinstance(indexer, slice):
        target_len = len(target)
        start = indexer.start
        stop = indexer.stop
        step = indexer.step
        if start is None:
            start = 0
        elif start < 0:
            start += target_len
        if stop is None or stop > target_len:
            stop = target_len
        elif stop < 0:
            stop += target_len
        if step is None:
            step = 1
        elif step < 0:
            start, stop = stop + 1, start + 1
            step = -step
        return (stop - start + step - 1) // step
    elif isinstance(indexer, (ABCSeries, ABCIndexClass, np.ndarray, list)):
        return len(indexer)
    elif not is_list_like_indexer(indexer):
        return 1
    raise AssertionError("cannot find the length of the indexer")