# Buggy code
def __init__(self, f: Union[FilePathOrBuffer, List], **kwds):
    """
    Workhorse function for processing nested list into DataFrame
    """
    ParserBase.__init__(self, kwds)

    self.data: Optional[Iterator[str]] = None
    self.buf: List = []
    self.pos = 0
    self.line_pos = 0

    self.skiprows = kwds["skiprows"]

    if callable(self.skiprows):
        self.skipfunc = self.skiprows
    else:
        self.skipfunc = lambda x: x in self.skiprows

    self.skipfooter = _validate_skipfooter_arg(kwds["skipfooter"])
    self.delimiter = kwds["delimiter"]

    self.quotechar = kwds["quotechar"]
    if isinstance(self.quotechar, str):
        self.quotechar = str(self.quotechar)

    self.escapechar = kwds["escapechar"]
    self.doublequote = kwds["doublequote"]
    self.skipinitialspace = kwds["skipinitialspace"]
    self.lineterminator = kwds["lineterminator"]
    self.quoting = kwds["quoting"]
    self.usecols, _ = _validate_usecols_arg(kwds["usecols"])
    self.skip_blank_lines = kwds["skip_blank_lines"]

    self.warn_bad_lines = kwds["warn_bad_lines"]
    self.error_bad_lines = kwds["error_bad_lines"]

    self.names_passed = kwds["names"] or None

    self.has_index_names = False
    if "has_index_names" in kwds:
        self.has_index_names = kwds["has_index_names"]

    self.verbose = kwds["verbose"]
    self.converters = kwds["converters"]

    self.dtype = kwds["dtype"]
    self.thousands = kwds["thousands"]
    self.decimal = kwds["decimal"]

    self.comment = kwds["comment"]

    # Set self.data to something that can read lines.
    if isinstance(f, list):
        # read_excel: f is a list
        self.data = cast(Iterator[str], f)
    else:
        self._open_handles(f, kwds)
        assert self.handles is not None
        assert hasattr(self.handles.handle, "readline")
        self._make_reader(self.handles.handle)

    # Get columns in two steps: infer from data, then
    # infer column indices from self.usecols if it is specified.
    self._col_indices: Optional[List[int]] = None
    try:
        (
            self.columns,
            self.num_original_columns,
            self.unnamed_cols,
        ) = self._infer_columns()
    except (TypeError, ValueError):
        self.close()
        raise

    # Now self.columns has the set of columns that we will process.
    # The original set is stored in self.original_columns.
    if len(self.columns) > 1:
        # we are processing a multi index column
        (
            self.columns,
            self.index_names,
            self.col_names,
            _,
        ) = self._extract_multi_indexer_columns(
            self.columns, self.index_names, self.col_names
        )
        # Update list of original names to include all indices.
        self.num_original_columns = len(self.columns)
    else:
        self.columns = self.columns[0]

    # get popped off for index
    self.orig_names = list(self.columns)

    # needs to be cleaned/refactored
    # multiple date column thing turning into a real spaghetti factory

    if not self._has_complex_date_col:
        (index_names, self.orig_names, self.columns) = self._get_index_name(
            self.columns
        )
        self._name_processed = True
        if self.index_names is None:
            self.index_names = index_names

    if self._col_indices is None:
        self._col_indices = list(range(len(self.columns)))

    self._validate_parse_dates_presence(self.columns)
    if self.parse_dates:
        self._no_thousands_columns = self._set_no_thousands_columns()
    else:
        self._no_thousands_columns = None

    if len(self.decimal) != 1:
        raise ValueError("Only length-1 decimal markers supported")

    decimal = re.escape(self.decimal)
    if self.thousands is None:
        regex = rf"^[\-\+]?[0-9]*({decimal}[0-9]*)?([0-9]?(E|e)\-?[0-9]+)?$"
    else:
        thousands = re.escape(self.thousands)
        regex = (
            rf"^[\-\+]?([0-9]+{thousands}|[0-9])*({decimal}[0-9]*)?"
            rf"([0-9]?(E|e)\-?[0-9]+)?$"
        )
    self.num = re.compile(regex)


# Traceback (UnicodeDecodeError)
Traceback (most recent call last):
File "..\scratch\pandas_file_handle.py", line 19, in <module>
dataframe = pandas.read_csv(csv_file, sep=None)
File "C:\Users\dmf\projects\invest\env\lib\site-packages\pandas\io\parsers.py", line 605, in read_csv
return _read(filepath_or_buffer, kwds)
File "C:\Users\dmf\projects\invest\env\lib\site-packages\pandas\io\parsers.py", line 457, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "C:\Users\dmf\projects\invest\env\lib\site-packages\pandas\io\parsers.py", line 814, in __init__
self._engine = self._make_engine(self.engine)
File "C:\Users\dmf\projects\invest\env\lib\site-packages\pandas\io\parsers.py", line 1045, in _make_engine
return mapping[engine](self.f, **self.options)  # type: ignore[call-arg]
File "C:\Users\dmf\projects\invest\env\lib\site-packages\pandas\io\parsers.py", line 2291, in __init__
self._make_reader(self.handles.handle)
File "C:\Users\dmf\projects\invest\env\lib\site-packages\pandas\io\parsers.py", line 2412, in _make_reader
line = f.readline()
File "C:\Users\dmf\projects\invest\env\lib\codecs.py", line 322, in decode
(result, consumed) = self._buffer_decode(data, self.errors, final)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xce in position 10: invalid continuation byte

# Fixed code
def __init__(self, f: Union[FilePathOrBuffer, List], **kwds):
    """
    Workhorse function for processing nested list into DataFrame
    """
    ParserBase.__init__(self, kwds)

    self.data: Optional[Iterator[str]] = None
    self.buf: List = []
    self.pos = 0
    self.line_pos = 0

    self.skiprows = kwds["skiprows"]

    if callable(self.skiprows):
        self.skipfunc = self.skiprows
    else:
        self.skipfunc = lambda x: x in self.skiprows

    self.skipfooter = _validate_skipfooter_arg(kwds["skipfooter"])
    self.delimiter = kwds["delimiter"]

    self.quotechar = kwds["quotechar"]
    if isinstance(self.quotechar, str):
        self.quotechar = str(self.quotechar)

    self.escapechar = kwds["escapechar"]
    self.doublequote = kwds["doublequote"]
    self.skipinitialspace = kwds["skipinitialspace"]
    self.lineterminator = kwds["lineterminator"]
    self.quoting = kwds["quoting"]
    self.usecols, _ = _validate_usecols_arg(kwds["usecols"])
    self.skip_blank_lines = kwds["skip_blank_lines"]

    self.warn_bad_lines = kwds["warn_bad_lines"]
    self.error_bad_lines = kwds["error_bad_lines"]

    self.names_passed = kwds["names"] or None

    self.has_index_names = False
    if "has_index_names" in kwds:
        self.has_index_names = kwds["has_index_names"]

    self.verbose = kwds["verbose"]
    self.converters = kwds["converters"]

    self.dtype = kwds["dtype"]
    self.thousands = kwds["thousands"]
    self.decimal = kwds["decimal"]

    self.comment = kwds["comment"]

    # Set self.data to something that can read lines.
    if isinstance(f, list):
        # read_excel: f is a list
        self.data = cast(Iterator[str], f)
    else:
        self._open_handles(f, kwds)
        assert self.handles is not None
        assert hasattr(self.handles.handle, "readline")
        try:
            self._make_reader(self.handles.handle)
        except (csv.Error, UnicodeDecodeError):
            self.close()
            raise

    # Get columns in two steps: infer from data, then
    # infer column indices from self.usecols if it is specified.
    self._col_indices: Optional[List[int]] = None
    try:
        (
            self.columns,
            self.num_original_columns,
            self.unnamed_cols,
        ) = self._infer_columns()
    except (TypeError, ValueError):
        self.close()
        raise

    # Now self.columns has the set of columns that we will process.
    # The original set is stored in self.original_columns.
    if len(self.columns) > 1:
        # we are processing a multi index column
        (
            self.columns,
            self.index_names,
            self.col_names,
            _,
        ) = self._extract_multi_indexer_columns(
            self.columns, self.index_names, self.col_names
        )
        # Update list of original names to include all indices.
        self.num_original_columns = len(self.columns)
    else:
        self.columns = self.columns[0]

    # get popped off for index
    self.orig_names = list(self.columns)

    # needs to be cleaned/refactored
    # multiple date column thing turning into a real spaghetti factory

    if not self._has_complex_date_col:
        (index_names, self.orig_names, self.columns) = self._get_index_name(
            self.columns
        )
        self._name_processed = True
        if self.index_names is None:
            self.index_names = index_names

    if self._col_indices is None:
        self._col_indices = list(range(len(self.columns)))

    self._validate_parse_dates_presence(self.columns)
    if self.parse_dates:
        self._no_thousands_columns = self._set_no_thousands_columns()
    else:
        self._no_thousands_columns = None

    if len(self.decimal) != 1:
        raise ValueError("Only length-1 decimal markers supported")

    decimal = re.escape(self.decimal)
    if self.thousands is None:
        regex = rf"^[\-\+]?[0-9]*({decimal}[0-9]*)?([0-9]?(E|e)\-?[0-9]+)?$"
    else:
        thousands = re.escape(self.thousands)
        regex = (
            rf"^[\-\+]?([0-9]+{thousands}|[0-9])*({decimal}[0-9]*)?"
            rf"([0-9]?(E|e)\-?[0-9]+)?$"
        )
    self.num = re.compile(regex)