# Buggy code
def value_counts(self, **kwargs):
    """
    Return a QueryCompiler of Series containing counts of unique values.

    Returns
    -------
    PandasQueryCompiler
    """
    if kwargs.get("bins", None) is not None:
        new_modin_frame = self._modin_frame._apply_full_axis(
            0, lambda df: df.squeeze(axis=1).value_counts(**kwargs)
        )
        return self.__constructor__(new_modin_frame)

    def map_func(df, *args, **kwargs):
        return df.squeeze(axis=1).value_counts(**kwargs)

    def reduce_func(df, *args, **kwargs):
        normalize = kwargs.get("normalize", False)
        sort = kwargs.get("sort", True)
        ascending = kwargs.get("ascending", False)
        dropna = kwargs.get("dropna", True)

        try:
            result = df.squeeze(axis=1).groupby(df.index, sort=False).sum()
        # This will happen with Arrow buffer read-only errors. We don't want to copy
        # all the time, so this will try to fast-path the code first.
        except ValueError:
            result = df.copy().squeeze(axis=1).groupby(df.index, sort=False).sum()

        if not dropna and np.nan in df.index:
            result = result.append(
                pandas.Series([df.squeeze(axis=1).loc[[np.nan]].sum()], index=[np.nan])
            )
        if normalize:
            result = result / df.squeeze(axis=1).sum()

        result = result.sort_values(ascending=ascending) if sort else result

        # We want to sort both values and indices of the result object.
        # This function will sort indices for equal values.
        def sort_index_for_equal_values(result, ascending):
            """
            Sort indices for equal values of result object.

            Parameters
            ----------
            result : pandas.Series or pandas.DataFrame with one column
                The object whose indices for equal values is needed to sort.
            ascending : boolean
                Sort in ascending (if it is True) or descending (if it is False) order.

            Returns
            -------
            pandas.DataFrame
                A new DataFrame with sorted indices.
            """
            is_range = False
            is_end = False
            i = 0
            new_index = np.empty(len(result), dtype=type(result.index))
            while i < len(result):
                j = i
                if i < len(result) - 1:
                    while result[result.index[i]] == result[result.index[i + 1]]:
                        i += 1
                        if is_range is False:
                            is_range = True
                        if i == len(result) - 1:
                            is_end = True
                            break
                if is_range:
                    k = j
                    for val in sorted(result.index[j : i + 1], reverse=not ascending):
                        new_index[k] = val
                        k += 1
                    if is_end:
                        break
                    is_range = False
                else:
                    new_index[j] = result.index[j]
                i += 1
            return pandas.DataFrame(result, index=new_index)

        return sort_index_for_equal_values(result, ascending)

    return MapReduceFunction.register(map_func, reduce_func, preserve_index=False)(
        self, **kwargs
    )


# Traceback (Exception)
Traceback (most recent call last):
File "test.py", line 15, in <module>
print(f"modin:\n{modin_df.sum(min_count=1)}")
File "modin\pandas\base.py", line 3512, in __str__
return repr(self)
File "modin\pandas\series.py", line 307, in __repr__
temp_df = self._build_repr_df(num_rows, num_cols)
File "modin\pandas\base.py", line 108, in _build_repr_df
return self.iloc[indexer]._query_compiler.to_pandas()
File "modin\backends\pandas\query_compiler.py", line 191, in to_pandas
return self._modin_frame.to_pandas()
File "modin\engines\base\frame\data.py", line 1801, in to_pandas
"Internal and external indices do not match.",
File "modin\error_message.py", line 54, 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 do not match.

# Fixed code
def value_counts(self, **kwargs):
    """
    Return a QueryCompiler of Series containing counts of unique values.

    Returns
    -------
    PandasQueryCompiler
    """
    if kwargs.get("bins", None) is not None:
        new_modin_frame = self._modin_frame._apply_full_axis(
            0, lambda df: df.squeeze(axis=1).value_counts(**kwargs)
        )
        return self.__constructor__(new_modin_frame)

    def map_func(df, *args, **kwargs):
        return df.squeeze(axis=1).value_counts(**kwargs).to_frame()

    def reduce_func(df, *args, **kwargs):
        normalize = kwargs.get("normalize", False)
        sort = kwargs.get("sort", True)
        ascending = kwargs.get("ascending", False)
        dropna = kwargs.get("dropna", True)

        try:
            result = df.squeeze(axis=1).groupby(df.index, sort=False).sum()
        # This will happen with Arrow buffer read-only errors. We don't want to copy
        # all the time, so this will try to fast-path the code first.
        except ValueError:
            result = df.copy().squeeze(axis=1).groupby(df.index, sort=False).sum()

        if not dropna and np.nan in df.index:
            result = result.append(
                pandas.Series([df.squeeze(axis=1).loc[[np.nan]].sum()], index=[np.nan])
            )
        if normalize:
            result = result / df.squeeze(axis=1).sum()

        result = result.sort_values(ascending=ascending) if sort else result

        # We want to sort both values and indices of the result object.
        # This function will sort indices for equal values.
        def sort_index_for_equal_values(result, ascending):
            """
            Sort indices for equal values of result object.

            Parameters
            ----------
            result : pandas.Series or pandas.DataFrame with one column
                The object whose indices for equal values is needed to sort.
            ascending : boolean
                Sort in ascending (if it is True) or descending (if it is False) order.

            Returns
            -------
            pandas.DataFrame
                A new DataFrame with sorted indices.
            """
            is_range = False
            is_end = False
            i = 0
            new_index = np.empty(len(result), dtype=type(result.index))
            while i < len(result):
                j = i
                if i < len(result) - 1:
                    while result[result.index[i]] == result[result.index[i + 1]]:
                        i += 1
                        if is_range is False:
                            is_range = True
                        if i == len(result) - 1:
                            is_end = True
                            break
                if is_range:
                    k = j
                    for val in sorted(result.index[j : i + 1], reverse=not ascending):
                        new_index[k] = val
                        k += 1
                    if is_end:
                        break
                    is_range = False
                else:
                    new_index[j] = result.index[j]
                i += 1
            return pandas.DataFrame(result, index=new_index, columns=["__reduced__"])

        return sort_index_for_equal_values(result, ascending)

    return MapReduceFunction.register(
        map_func, reduce_func, axis=0, preserve_index=False
    )(self, **kwargs)