# Buggy code
def _read(cls, io, **kwargs):
    if kwargs.get("engine", None) is not None and kwargs.get("engine") != "openpyxl":
        warnings.warn(
            "Modin only implements parallel `read_excel` with `openpyxl` engine, "
            'please specify `engine=None` or `engine="openpyxl"` to '
            "use Modin's parallel implementation."
        )
        return cls.single_worker_read(io, **kwargs)
    if sys.version_info < (3, 7):
        warnings.warn("Python 3.7 or higher required for parallel `read_excel`.")
        return cls.single_worker_read(io, **kwargs)

    from zipfile import ZipFile
    from openpyxl.worksheet.worksheet import Worksheet
    from openpyxl.worksheet._reader import WorksheetReader
    from openpyxl.reader.excel import ExcelReader
    from modin.backends.pandas.parsers import PandasExcelParser

    sheet_name = kwargs.get("sheet_name", 0)
    if sheet_name is None or isinstance(sheet_name, list):
        warnings.warn(
            "`read_excel` functionality is only implemented for a single sheet at a "
            "time. Multiple sheet reading coming soon!"
        )
        return cls.single_worker_read(io, **kwargs)

    warnings.warn(
        "Parallel `read_excel` is a new feature! Please email "
        "bug_reports@modin.org if you run into any problems."
    )

    # NOTE: ExcelReader() in read-only mode does not close file handle by itself
    # work around that by passing file object if we received some path
    io_file = open(io, "rb") if isinstance(io, str) else io
    try:
        ex = ExcelReader(io_file, read_only=True)
        ex.read()
        wb = ex.wb

        # Get shared strings
        ex.read_manifest()
        ex.read_strings()
        ws = Worksheet(wb)
    finally:
        if isinstance(io, str):
            # close only if it were us who opened the object
            io_file.close()

    with ZipFile(io) as z:
        from io import BytesIO

        # Convert index to sheet name in file
        if isinstance(sheet_name, int):
            sheet_name = "sheet{}".format(sheet_name + 1)
        else:
            sheet_name = "sheet{}".format(wb.sheetnames.index(sheet_name) + 1)
        if any(sheet_name.lower() in name for name in z.namelist()):
            sheet_name = sheet_name.lower()
        elif any(sheet_name.title() in name for name in z.namelist()):
            sheet_name = sheet_name.title()
        else:
            raise ValueError("Sheet {} not found".format(sheet_name.lower()))
        # Pass this value to the workers
        kwargs["sheet_name"] = sheet_name

        f = z.open("xl/worksheets/{}.xml".format(sheet_name))
        f = BytesIO(f.read())
        total_bytes = cls.file_size(f)

        from modin.pandas import DEFAULT_NPARTITIONS

        num_partitions = DEFAULT_NPARTITIONS
        # Read some bytes from the sheet so we can extract the XML header and first
        # line. We need to make sure we get the first line of the data as well
        # because that is where the column names are. The header information will
        # be extracted and sent to all of the nodes.
        sheet_block = f.read(EXCEL_READ_BLOCK_SIZE)
        end_of_row_tag = b"</row>"
        while end_of_row_tag not in sheet_block:
            sheet_block += f.read(EXCEL_READ_BLOCK_SIZE)
        idx_of_header_end = sheet_block.index(end_of_row_tag) + len(end_of_row_tag)
        sheet_header = sheet_block[:idx_of_header_end]
        # Reset the file pointer to begin at the end of the header information.
        f.seek(idx_of_header_end)
        kwargs["_header"] = sheet_header
        footer = b"</sheetData></worksheet>"
        # Use openpyxml to parse the data
        reader = WorksheetReader(
            ws, BytesIO(sheet_header + footer), ex.shared_strings, False
        )
        # Attach cells to the worksheet
        reader.bind_cells()
        data = PandasExcelParser.get_sheet_data(ws, kwargs.get("convert_float", True))
        # Extract column names from parsed data.
        column_names = pandas.Index(data[0])
        index_col = kwargs.get("index_col", None)
        # Remove column names that are specified as `index_col`
        if index_col is not None:
            column_names = column_names.drop(column_names[index_col])
        # Compute partition metadata upfront so it is uniform for all partitions
        chunk_size = max(1, (total_bytes - f.tell()) // num_partitions)
        num_splits = min(len(column_names), num_partitions)
        kwargs["fname"] = io
        # Skiprows will be used to inform a partition how many rows come before it.
        kwargs["skiprows"] = 0
        row_count = 0
        data_ids = []
        index_ids = []
        dtypes_ids = []

        # Compute column metadata
        column_chunksize = compute_chunksize(
            pandas.DataFrame(columns=column_names), num_splits, axis=1
        )
        if column_chunksize > len(column_names):
            column_widths = [len(column_names)]
            # This prevents us from unnecessarily serializing a bunch of empty
            # objects.
            num_splits = 1
        else:
            column_widths = [
                column_chunksize
                if len(column_names) > (column_chunksize * (i + 1))
                else 0
                if len(column_names) < (column_chunksize * i)
                else len(column_names) - (column_chunksize * i)
                for i in range(num_splits)
            ]
        kwargs["num_splits"] = num_splits

        while f.tell() < total_bytes:
            args = kwargs
            args["skiprows"] = row_count + args["skiprows"]
            args["start"] = f.tell()
            chunk = f.read(chunk_size)
            # This edge case can happen when we have reached the end of the data
            # but not the end of the file.
            if b"<row" not in chunk:
                break
            row_close_tag = b"</row>"
            row_count = re.subn(row_close_tag, b"", chunk)[1]

            # Make sure we are reading at least one row.
            while row_count == 0:
                chunk += f.read(chunk_size)
                row_count += re.subn(row_close_tag, b"", chunk)[1]

            last_index = chunk.rindex(row_close_tag)
            f.seek(-(len(chunk) - last_index) + len(row_close_tag), 1)
            args["end"] = f.tell()

            # If there is no data, exit before triggering computation.
            if b"</row>" not in chunk and b"</sheetData>" in chunk:
                break
            remote_results_list = cls.deploy(cls.parse, num_splits + 2, args)
            data_ids.append(remote_results_list[:-2])
            index_ids.append(remote_results_list[-2])
            dtypes_ids.append(remote_results_list[-1])

            # The end of the spreadsheet
            if b"</sheetData>" in chunk:
                break

    # Compute the index based on a sum of the lengths of each partition (by default)
    # or based on the column(s) that were requested.
    if index_col is None:
        row_lengths = cls.materialize(index_ids)
        new_index = pandas.RangeIndex(sum(row_lengths))
    else:
        index_objs = cls.materialize(index_ids)
        row_lengths = [len(o) for o in index_objs]
        new_index = index_objs[0].append(index_objs[1:])

    # Compute dtypes by getting collecting and combining all of the partitions. The
    # reported dtypes from differing rows can be different based on the inference in
    # the limited data seen by each worker. We use pandas to compute the exact dtype
    # over the whole column for each column. The index is set below.
    dtypes = cls.get_dtypes(dtypes_ids)

    data_ids = cls.build_partition(data_ids, row_lengths, column_widths)
    # Set the index for the dtypes to the column names
    if isinstance(dtypes, pandas.Series):
        dtypes.index = column_names
    else:
        dtypes = pandas.Series(dtypes, index=column_names)
    new_frame = cls.frame_cls(
        data_ids,
        new_index,
        column_names,
        row_lengths,
        column_widths,
        dtypes=dtypes,
    )
    new_query_compiler = cls.query_compiler_cls(new_frame)
    if index_col is None:
        new_query_compiler._modin_frame._apply_index_objs(axis=0)
    return new_query_compiler


# Traceback (Exception)
pd.read_excel('test_emptyline.xlsx')
UserWarning: Parallel `read_excel` is a new feature! Please email bug_reports@modin.org if you run into any problems.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "c:\foo\modin\pandas\dataframe.py", line 183, in __repr__
result = repr(self._build_repr_df(num_rows, num_cols))
File "c:\foo\modin\pandas\base.py", line 168, in _build_repr_df
return self.iloc[indexer]._query_compiler.to_pandas()
File "c:\foo\modin\backends\pandas\query_compiler.py", line 233, in to_pandas
return self._modin_frame.to_pandas()
File "c:\foo\modin\engines\base\frame\data.py", line 2063, in to_pandas
f"Internal and external indices on axis {axis} do not match.",
File "c:\foo\modin\error_message.py", line 63, in catch_bugs_and_request_email
" caused this error.\n{}".format(extra_log)
Exception: Internal Error. Please email bug_reports@modin.org with the traceback and command that caused this error.
Internal and external indices on axis 1 do not match.

# Fixed code
def _read(cls, io, **kwargs):
    if kwargs.get("engine", None) is not None and kwargs.get("engine") != "openpyxl":
        warnings.warn(
            "Modin only implements parallel `read_excel` with `openpyxl` engine, "
            'please specify `engine=None` or `engine="openpyxl"` to '
            "use Modin's parallel implementation."
        )
        return cls.single_worker_read(io, **kwargs)
    if sys.version_info < (3, 7):
        warnings.warn("Python 3.7 or higher required for parallel `read_excel`.")
        return cls.single_worker_read(io, **kwargs)

    from zipfile import ZipFile
    from openpyxl.worksheet.worksheet import Worksheet
    from openpyxl.worksheet._reader import WorksheetReader
    from openpyxl.reader.excel import ExcelReader
    from modin.backends.pandas.parsers import PandasExcelParser

    sheet_name = kwargs.get("sheet_name", 0)
    if sheet_name is None or isinstance(sheet_name, list):
        warnings.warn(
            "`read_excel` functionality is only implemented for a single sheet at a "
            "time. Multiple sheet reading coming soon!"
        )
        return cls.single_worker_read(io, **kwargs)

    warnings.warn(
        "Parallel `read_excel` is a new feature! Please email "
        "bug_reports@modin.org if you run into any problems."
    )

    # NOTE: ExcelReader() in read-only mode does not close file handle by itself
    # work around that by passing file object if we received some path
    io_file = open(io, "rb") if isinstance(io, str) else io
    try:
        ex = ExcelReader(io_file, read_only=True)
        ex.read()
        wb = ex.wb

        # Get shared strings
        ex.read_manifest()
        ex.read_strings()
        ws = Worksheet(wb)
    finally:
        if isinstance(io, str):
            # close only if it were us who opened the object
            io_file.close()

    pandas_kw = dict(kwargs)  # preserve original kwargs
    with ZipFile(io) as z:
        from io import BytesIO

        # Convert index to sheet name in file
        if isinstance(sheet_name, int):
            sheet_name = "sheet{}".format(sheet_name + 1)
        else:
            sheet_name = "sheet{}".format(wb.sheetnames.index(sheet_name) + 1)
        if any(sheet_name.lower() in name for name in z.namelist()):
            sheet_name = sheet_name.lower()
        elif any(sheet_name.title() in name for name in z.namelist()):
            sheet_name = sheet_name.title()
        else:
            raise ValueError("Sheet {} not found".format(sheet_name.lower()))
        # Pass this value to the workers
        kwargs["sheet_name"] = sheet_name

        f = z.open("xl/worksheets/{}.xml".format(sheet_name))
        f = BytesIO(f.read())
        total_bytes = cls.file_size(f)

        from modin.pandas import DEFAULT_NPARTITIONS

        num_partitions = DEFAULT_NPARTITIONS
        # Read some bytes from the sheet so we can extract the XML header and first
        # line. We need to make sure we get the first line of the data as well
        # because that is where the column names are. The header information will
        # be extracted and sent to all of the nodes.
        sheet_block = f.read(EXCEL_READ_BLOCK_SIZE)
        end_of_row_tag = b"</row>"
        while end_of_row_tag not in sheet_block:
            sheet_block += f.read(EXCEL_READ_BLOCK_SIZE)
        idx_of_header_end = sheet_block.index(end_of_row_tag) + len(end_of_row_tag)
        sheet_header = sheet_block[:idx_of_header_end]
        # Reset the file pointer to begin at the end of the header information.
        f.seek(idx_of_header_end)
        kwargs["_header"] = sheet_header
        footer = b"</sheetData></worksheet>"
        # Use openpyxml to parse the data
        reader = WorksheetReader(
            ws, BytesIO(sheet_header + footer), ex.shared_strings, False
        )
        # Attach cells to the worksheet
        reader.bind_cells()
        data = PandasExcelParser.get_sheet_data(ws, kwargs.get("convert_float", True))
        # Extract column names from parsed data.
        column_names = pandas.Index(data[0])
        index_col = kwargs.get("index_col", None)
        # Remove column names that are specified as `index_col`
        if index_col is not None:
            column_names = column_names.drop(column_names[index_col])

        if not all(column_names):
            # some column names are empty, use pandas reader to take the names from it
            pandas_kw["nrows"] = 1
            df = pandas.read_excel(io, **pandas_kw)
            column_names = df.columns

        # Compute partition metadata upfront so it is uniform for all partitions
        chunk_size = max(1, (total_bytes - f.tell()) // num_partitions)
        num_splits = min(len(column_names), num_partitions)
        kwargs["fname"] = io
        # Skiprows will be used to inform a partition how many rows come before it.
        kwargs["skiprows"] = 0
        row_count = 0
        data_ids = []
        index_ids = []
        dtypes_ids = []

        # Compute column metadata
        column_chunksize = compute_chunksize(
            pandas.DataFrame(columns=column_names), num_splits, axis=1
        )
        if column_chunksize > len(column_names):
            column_widths = [len(column_names)]
            # This prevents us from unnecessarily serializing a bunch of empty
            # objects.
            num_splits = 1
        else:
            column_widths = [
                column_chunksize
                if len(column_names) > (column_chunksize * (i + 1))
                else 0
                if len(column_names) < (column_chunksize * i)
                else len(column_names) - (column_chunksize * i)
                for i in range(num_splits)
            ]
        kwargs["num_splits"] = num_splits

        while f.tell() < total_bytes:
            args = kwargs
            args["skiprows"] = row_count + args["skiprows"]
            args["start"] = f.tell()
            chunk = f.read(chunk_size)
            # This edge case can happen when we have reached the end of the data
            # but not the end of the file.
            if b"<row" not in chunk:
                break
            row_close_tag = b"</row>"
            row_count = re.subn(row_close_tag, b"", chunk)[1]

            # Make sure we are reading at least one row.
            while row_count == 0:
                chunk += f.read(chunk_size)
                row_count += re.subn(row_close_tag, b"", chunk)[1]

            last_index = chunk.rindex(row_close_tag)
            f.seek(-(len(chunk) - last_index) + len(row_close_tag), 1)
            args["end"] = f.tell()

            # If there is no data, exit before triggering computation.
            if b"</row>" not in chunk and b"</sheetData>" in chunk:
                break
            remote_results_list = cls.deploy(cls.parse, num_splits + 2, args)
            data_ids.append(remote_results_list[:-2])
            index_ids.append(remote_results_list[-2])
            dtypes_ids.append(remote_results_list[-1])

            # The end of the spreadsheet
            if b"</sheetData>" in chunk:
                break

    # Compute the index based on a sum of the lengths of each partition (by default)
    # or based on the column(s) that were requested.
    if index_col is None:
        row_lengths = cls.materialize(index_ids)
        new_index = pandas.RangeIndex(sum(row_lengths))
    else:
        index_objs = cls.materialize(index_ids)
        row_lengths = [len(o) for o in index_objs]
        new_index = index_objs[0].append(index_objs[1:])

    # Compute dtypes by getting collecting and combining all of the partitions. The
    # reported dtypes from differing rows can be different based on the inference in
    # the limited data seen by each worker. We use pandas to compute the exact dtype
    # over the whole column for each column. The index is set below.
    dtypes = cls.get_dtypes(dtypes_ids)

    data_ids = cls.build_partition(data_ids, row_lengths, column_widths)
    # Set the index for the dtypes to the column names
    if isinstance(dtypes, pandas.Series):
        dtypes.index = column_names
    else:
        dtypes = pandas.Series(dtypes, index=column_names)
    new_frame = cls.frame_cls(
        data_ids,
        new_index,
        column_names,
        row_lengths,
        column_widths,
        dtypes=dtypes,
    )
    new_query_compiler = cls.query_compiler_cls(new_frame)
    if index_col is None:
        new_query_compiler._modin_frame._apply_index_objs(axis=0)
    return new_query_compiler