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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def append(self, other, ignore_index=False, verify_integrity=False, sort=None):
 """ 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 None Sort columns if the columns of `self` and `other` are not aligned. The default sorting is deprecated and will change to not-sorting in a future version of pandas. Explicitly pass ``sort=True`` to silence the warning and sort. Explicitly pass ``sort=False`` to silence the warning and not sort. .. versionadded:: 0.23.0 Returns ------- DataFrame See Also -------- concat : General function to concatenate DataFrame, Series or Panel objects. Notes ----- If a list of dict/series is passed and the keys are all contained in the DataFrame's index, the order of the columns in the resulting DataFrame will be unchanged. Iteratively appending rows to a DataFrame can be more computationally intensive than a single concatenate. A better solution is to append those rows to a list and then concatenate the list with the original DataFrame all at once. Examples -------- A B 0 1 2 1 3 4 A B 0 1 2 1 3 4 0 5 6 1 7 8 With `ignore_index` set to True: A B 0 1 2 1 3 4 2 5 6 3 7 8 The following, while not recommended methods for generating DataFrames, show two ways to generate a DataFrame from multiple data sources. Less efficient: A 0 0 1 1 2 2 3 3 4 4 More efficient: A 0 0 1 1 2 2 3 3 4 4 """

        if isinstance(other, (Series, dict)):
            if isinstance(other, dict):
                other = Series(other)
            if other.name is None and not ignore_index:
                raise TypeError('Can only append a Series if ignore_index=True'
                                ' or if the Series has a name')

            if other.name is None:
                index = None
            else:
                # other must have the same index name as self, otherwise
                # index name will be reset
                index = Index([other.name], name=self.index.name)

            idx_diff = other.index.difference(self.columns)
            try:
                combined_columns = self.columns.append(idx_diff)
            except TypeError:
                combined_columns = self.columns.astype(object).append(idx_diff)
            other = other.reindex(combined_columns, copy=False)
            other = DataFrame(other.values.reshape((1, len(other))),
                              index=index,
                              columns=combined_columns)
            other = other._convert(datetime=True, timedelta=True)
            if not self.columns.equals(combined_columns):
                self = self.reindex(columns=combined_columns)
        elif isinstance(other, list) and not isinstance(other[0], DataFrame):
            other = DataFrame(other)
            if (self.columns.get_indexer(other.columns) >= 0).all():
                other = other.reindex(columns=self.columns)

        from pandas.core.reshape.concat import concat
        if isinstance(other, (list, tuple)):
            to_concat = [self] + other
        else:
            to_concat = [self, other]
        return concat(to_concat, ignore_index=ignore_index,
                      verify_integrity=verify_integrity,
                      sort=sort)