def append(self, other: 'DataFrame', ignore_index: bool=False, verify_integrity: bool=False, sort: bool=False) -> 'DataFrame':
    "\n        Append rows of other to the end of caller, returning a new object.\n\n        Columns in other that are not in the caller are added as new columns.\n\n        Parameters\n        ----------\n        other : DataFrame or Series/dict-like object, or list of these\n            The data to append.\n\n        ignore_index : boolean, default False\n            If True, do not use the index labels.\n\n        verify_integrity : boolean, default False\n            If True, raise ValueError on creating index with duplicates.\n\n        sort : boolean, default False\n            Currently not supported.\n\n        Returns\n        -------\n        appended : DataFrame\n\n        Examples\n        --------\n        >>> df = ps.DataFrame([[1, 2], [3, 4]], columns=list('AB'))\n\n        >>> df.append(df)\n           A  B\n        0  1  2\n        1  3  4\n        0  1  2\n        1  3  4\n\n        >>> df.append(df, ignore_index=True)\n           A  B\n        0  1  2\n        1  3  4\n        2  1  2\n        3  3  4\n        "
    if isinstance(other, ps.Series):
        raise TypeError('DataFrames.append() does not support appending Series to DataFrames')
    if sort:
        raise NotImplementedError("The 'sort' parameter is currently not supported")
    if (not ignore_index):
        index_scols = self._internal.index_spark_columns
        if (len(index_scols) != other._internal.index_level):
            raise ValueError('Both DataFrames have to have the same number of index levels')
        if (verify_integrity and (len(index_scols) > 0)):
            if (self._internal.spark_frame.select(index_scols).intersect(other._internal.spark_frame.select(other._internal.index_spark_columns)).count() > 0):
                raise ValueError('Indices have overlapping values')
    from pyspark.pandas.namespace import concat
    return cast(DataFrame, concat([self, other], ignore_index=ignore_index))
Append rows of other to the end of caller, returning a new object.

Columns in other that are not in the caller are added as new columns.

Parameters
----------
other : DataFrame or Series/dict-like object, or list of these
    The data to append.

ignore_index : boolean, default False
    If True, do not use the index labels.

verify_integrity : boolean, default False
    If True, raise ValueError on creating index with duplicates.

sort : boolean, default False
    Currently not supported.

Returns
-------
appended : DataFrame

Examples
--------
>>> df = ps.DataFrame([[1, 2], [3, 4]], columns=list('AB'))

>>> df.append(df)
   A  B
0  1  2
1  3  4
0  1  2
1  3  4

>>> df.append(df, ignore_index=True)
   A  B
0  1  2
1  3  4
2  1  2
3  3  4
def append(self, other: 'DataFrame', ignore_index: bool=False, verify_integrity: bool=False, sort: bool=False) -> 'DataFrame':
    "\n        Append rows of other to the end of caller, returning a new object.\n\n        Columns in other that are not in the caller are added as new columns.\n\n        Parameters\n        ----------\n        other : DataFrame or Series/dict-like object, or list of these\n            The data to append.\n\n        ignore_index : boolean, default False\n            If True, do not use the index labels.\n\n        verify_integrity : boolean, default False\n            If True, raise ValueError on creating index with duplicates.\n\n        sort : boolean, default False\n            Currently not supported.\n\n        Returns\n        -------\n        appended : DataFrame\n\n        Examples\n        --------\n        >>> df = ps.DataFrame([[1, 2], [3, 4]], columns=list('AB'))\n\n        >>> df.append(df)\n           A  B\n        0  1  2\n        1  3  4\n        0  1  2\n        1  3  4\n\n        >>> df.append(df, ignore_index=True)\n           A  B\n        0  1  2\n        1  3  4\n        2  1  2\n        3  3  4\n        "
    if isinstance(other, ps.Series):
        raise TypeError('DataFrames.append() does not support appending Series to DataFrames')
    if sort:
        raise NotImplementedError("The 'sort' parameter is currently not supported")
    if (not ignore_index):
        index_scols = self._internal.index_spark_columns
        if (len(index_scols) != other._internal.index_level):
            raise ValueError('Both DataFrames have to have the same number of index levels')
        if (verify_integrity and (len(index_scols) > 0)):
            if (self._internal.spark_frame.select(index_scols).intersect(other._internal.spark_frame.select(other._internal.index_spark_columns)).count() > 0):
                raise ValueError('Indices have overlapping values')
    from pyspark.pandas.namespace import concat
    return cast(DataFrame, concat([self, other], ignore_index=ignore_index))