# Buggy code
def read_csv(
    cls,
    filepath_or_buffer,
    sep=",",
    delimiter=None,
    header="infer",
    names=None,
    index_col=None,
    usecols=None,
    squeeze=False,
    prefix=None,
    mangle_dupe_cols=True,
    dtype=None,
    engine=None,
    converters=None,
    true_values=None,
    false_values=None,
    skipinitialspace=False,
    skiprows=None,
    nrows=None,
    na_values=None,
    keep_default_na=True,
    na_filter=True,
    verbose=False,
    skip_blank_lines=True,
    parse_dates=False,
    infer_datetime_format=False,
    keep_date_col=False,
    date_parser=None,
    dayfirst=False,
    cache_dates=True,
    iterator=False,
    chunksize=None,
    compression="infer",
    thousands=None,
    decimal=b".",
    lineterminator=None,
    quotechar='"',
    quoting=0,
    escapechar=None,
    comment=None,
    encoding=None,
    dialect=None,
    error_bad_lines=True,
    warn_bad_lines=True,
    skipfooter=0,
    doublequote=True,
    delim_whitespace=False,
    low_memory=True,
    memory_map=False,
    float_precision=None,
    storage_options=None,
):
    items = locals().copy()
    mykwargs = {k: items[k] for k in items if k in cls.arg_keys}
    eng = str(engine).lower().strip()
    try:
        if eng in ["pandas", "c"]:
            return cls._read(**mykwargs)

        if isinstance(dtype, dict):
            column_types = {c: cls._dtype_to_arrow(t) for c, t in dtype.items()}
        else:
            column_types = cls._dtype_to_arrow(dtype)

        if (type(parse_dates) is list) and type(column_types) is dict:
            for c in parse_dates:
                column_types[c] = pa.timestamp("s")

        if names:
            if header == 0:
                skiprows = skiprows + 1 if skiprows is not None else 1
            elif header is None or header == "infer":
                pass
            else:
                raise NotImplementedError(
                    "read_csv with 'arrow' engine and provided 'names' parameter supports only 0, None and 'infer' header values"
                )
        else:
            if header == 0 or header == "infer":
                pass
            else:
                raise NotImplementedError(
                    "read_csv with 'arrow' engine without 'names' parameter provided supports only 0 and 'infer' header values"
                )

        if delimiter is None:
            delimiter = sep

        if delim_whitespace and delimiter != ",":
            raise ValueError(
                "Specified a delimiter and delim_whitespace=True; you can only specify one."
            )

        po = ParseOptions(
            delimiter="\\s+" if delim_whitespace else delimiter,
            quote_char=quotechar,
            double_quote=doublequote,
            escape_char=escapechar,
            newlines_in_values=False,
            ignore_empty_lines=skip_blank_lines,
        )
        co = ConvertOptions(
            check_utf8=None,
            column_types=column_types,
            null_values=None,
            true_values=None,
            false_values=None,
            strings_can_be_null=None,
            include_columns=None,
            include_missing_columns=None,
            auto_dict_encode=None,
            auto_dict_max_cardinality=None,
        )
        ro = ReadOptions(
            use_threads=True,
            block_size=None,
            skip_rows=skiprows,
            column_names=names,
            autogenerate_column_names=None,
        )

        at = read_csv(
            filepath_or_buffer,
            read_options=ro,
            parse_options=po,
            convert_options=co,
        )

        return cls.from_arrow(at)
    except (pa.ArrowNotImplementedError, NotImplementedError):
        if eng in ["arrow"]:
            raise

        ErrorMessage.default_to_pandas("`read_csv`")
        return cls._read(**mykwargs)


# Traceback (AssertionError)
Traceback (most recent call last):
File "test.py", line 46, in <module>
df_equals(df_pandas, df_modin)
File "/modin/modin/pandas/test/utils.py", line 542, in df_equals
check_categorical=False,
File "/miniconda3/envs/modin_omnisci/lib/python3.7/site-packages/pandas/_testing.py", line 1704, in assert_frame_equal
atol=atol,
File "/miniconda3/envs/modin_omnisci/lib/python3.7/site-packages/pandas/_testing.py", line 1427, in assert_series_equal
raise AssertionError(msg)
AssertionError: [datetimelike_compat=True] ['2000-01-01' '2000-01-01' '2000-01-01' '2000-01-01' '2000-01-01'
'2000-01-01' '2000-01-01' '2000-01-01' '2000-01-01' '2000-01-01'
'2000-01-01' '2000-01-01' '2000-01-01' '2000-01-01' '2000-01-01'
'2000-01-01' '2000-01-01' '2000-01-01' '2000-01-01' '2000-01-01'
'2000-01-01' '2000-01-01' '2000-01-01' '2000-01-01' '2000-01-02'
'2000-01-02' '2000-01-02' '2000-01-02' '2000-01-02' '2000-01-02'
'2000-01-02' '2000-01-02' '2000-01-02' '2000-01-02' '2000-01-02'
'2000-01-02' '2000-01-02' '2000-01-02' '2000-01-02' '2000-01-02'
'2000-01-02' '2000-01-02' '2000-01-02' '2000-01-02' '2000-01-02'
'2000-01-02' '2000-01-02' '2000-01-02' '2000-01-03' '2000-01-03'
'2000-01-03' '2000-01-03' '2000-01-03' '2000-01-03' '2000-01-03'
'2000-01-03' '2000-01-03' '2000-01-03' '2000-01-03' '2000-01-03'
'2000-01-03' '2000-01-03' '2000-01-03' '2000-01-03'] is not equal to <DatetimeArray>
['2000-01-01 00:00:00', '2000-01-01 00:00:00', '2000-01-01 00:00:00',
'2000-01-01 00:00:00', '2000-01-01 00:00:00', '2000-01-01 00:00:00',
'2000-01-01 00:00:00', '2000-01-01 00:00:00', '2000-01-01 00:00:00',
'2000-01-01 00:00:00', '2000-01-01 00:00:00', '2000-01-01 00:00:00',
'2000-01-01 00:00:00', '2000-01-01 00:00:00', '2000-01-01 00:00:00',
'2000-01-01 00:00:00', '2000-01-01 00:00:00', '2000-01-01 00:00:00',
'2000-01-01 00:00:00', '2000-01-01 00:00:00', '2000-01-01 00:00:00',
'2000-01-01 00:00:00', '2000-01-01 00:00:00', '2000-01-01 00:00:00',
'2000-01-02 00:00:00', '2000-01-02 00:00:00', '2000-01-02 00:00:00',
'2000-01-02 00:00:00', '2000-01-02 00:00:00', '2000-01-02 00:00:00',
'2000-01-02 00:00:00', '2000-01-02 00:00:00', '2000-01-02 00:00:00',
'2000-01-02 00:00:00', '2000-01-02 00:00:00', '2000-01-02 00:00:00',
'2000-01-02 00:00:00', '2000-01-02 00:00:00', '2000-01-02 00:00:00',
'2000-01-02 00:00:00', '2000-01-02 00:00:00', '2000-01-02 00:00:00',
'2000-01-02 00:00:00', '2000-01-02 00:00:00', '2000-01-02 00:00:00',
'2000-01-02 00:00:00', '2000-01-02 00:00:00', '2000-01-02 00:00:00',
'2000-01-03 00:00:00', '2000-01-03 00:00:00', '2000-01-03 00:00:00',
'2000-01-03 00:00:00', '2000-01-03 00:00:00', '2000-01-03 00:00:00',
'2000-01-03 00:00:00', '2000-01-03 00:00:00', '2000-01-03 00:00:00',
'2000-01-03 00:00:00', '2000-01-03 00:00:00', '2000-01-03 00:00:00',
'2000-01-03 00:00:00', '2000-01-03 00:00:00', '2000-01-03 00:00:00',
'2000-01-03 00:00:00']
Length: 64, dtype: datetime64[ns].

# Fixed code
def read_csv(
    cls,
    filepath_or_buffer,
    sep=",",
    delimiter=None,
    header="infer",
    names=None,
    index_col=None,
    usecols=None,
    squeeze=False,
    prefix=None,
    mangle_dupe_cols=True,
    dtype=None,
    engine=None,
    converters=None,
    true_values=None,
    false_values=None,
    skipinitialspace=False,
    skiprows=None,
    nrows=None,
    na_values=None,
    keep_default_na=True,
    na_filter=True,
    verbose=False,
    skip_blank_lines=True,
    parse_dates=False,
    infer_datetime_format=False,
    keep_date_col=False,
    date_parser=None,
    dayfirst=False,
    cache_dates=True,
    iterator=False,
    chunksize=None,
    compression="infer",
    thousands=None,
    decimal=b".",
    lineterminator=None,
    quotechar='"',
    quoting=0,
    escapechar=None,
    comment=None,
    encoding=None,
    dialect=None,
    error_bad_lines=True,
    warn_bad_lines=True,
    skipfooter=0,
    doublequote=True,
    delim_whitespace=False,
    low_memory=True,
    memory_map=False,
    float_precision=None,
    storage_options=None,
):
    items = locals().copy()
    mykwargs = {k: items[k] for k in items if k in cls.arg_keys}
    eng = str(engine).lower().strip()
    try:
        if eng in ["pandas", "c"]:
            return cls._read(**mykwargs)

        if isinstance(dtype, dict):
            column_types = {c: cls._dtype_to_arrow(t) for c, t in dtype.items()}
        else:
            column_types = cls._dtype_to_arrow(dtype)

        if (type(parse_dates) is list) and type(column_types) is dict:
            for c in parse_dates:
                column_types[c] = pa.timestamp("s")

        if names:
            if header == 0:
                skiprows = skiprows + 1 if skiprows is not None else 1
            elif header is None or header == "infer":
                pass
            else:
                raise NotImplementedError(
                    "read_csv with 'arrow' engine and provided 'names' parameter supports only 0, None and 'infer' header values"
                )
        else:
            if header == 0 or header == "infer":
                pass
            else:
                raise NotImplementedError(
                    "read_csv with 'arrow' engine without 'names' parameter provided supports only 0 and 'infer' header values"
                )

        if delimiter is None:
            delimiter = sep

        if delim_whitespace and delimiter != ",":
            raise ValueError(
                "Specified a delimiter and delim_whitespace=True; you can only specify one."
            )

        po = ParseOptions(
            delimiter="\\s+" if delim_whitespace else delimiter,
            quote_char=quotechar,
            double_quote=doublequote,
            escape_char=escapechar,
            newlines_in_values=False,
            ignore_empty_lines=skip_blank_lines,
        )
        co = ConvertOptions(
            check_utf8=None,
            column_types=column_types,
            null_values=None,
            true_values=None,
            false_values=None,
            # timestamp fields should be handled as strings if parse_dates
            # didn't passed explicitly as an array or a dict
            timestamp_parsers=[""] if isinstance(parse_dates, bool) else None,
            strings_can_be_null=None,
            include_columns=None,
            include_missing_columns=None,
            auto_dict_encode=None,
            auto_dict_max_cardinality=None,
        )
        ro = ReadOptions(
            use_threads=True,
            block_size=None,
            skip_rows=skiprows,
            column_names=names,
            autogenerate_column_names=None,
        )

        at = read_csv(
            filepath_or_buffer,
            read_options=ro,
            parse_options=po,
            convert_options=co,
        )

        return cls.from_arrow(at)
    except (pa.ArrowNotImplementedError, NotImplementedError):
        if eng in ["arrow"]:
            raise

        ErrorMessage.default_to_pandas("`read_csv`")
        return cls._read(**mykwargs)