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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 execute_series_lead_lag_timedelta( op, data, offset, default, aggcontext=None, **kwargs ):
 """An implementation of shifting a column relative to another one that is in units of time rather than rows. """

    # lagging adds time (delayed), leading subtracts time (moved up)
    func = operator.add if isinstance(op, ops.Lag) else operator.sub
    group_by = aggcontext.group_by
    order_by = aggcontext.order_by

    # get the parent object from which `data` originated
    parent = aggcontext.parent

    # get the DataFrame from the parent object, handling the DataFrameGroupBy
    # case
    parent_df = getattr(parent, 'obj', parent)

    # index our parent df by grouping and ordering keys
    indexed_original_df = parent_df.set_index(group_by + order_by)

    # perform the time shift
    adjusted_parent_df = parent_df.assign(
        **{k: func(parent_df[k], offset) for k in order_by}
    )

    # index the parent *after* adjustment
    adjusted_indexed_parent = adjusted_parent_df.set_index(group_by + order_by)

    # get the column we care about
    result = adjusted_indexed_parent[getattr(data, 'obj', data).name]

    # reindex the shifted data by the original frame's index
    result = result.reindex(indexed_original_df.index)

    # add a default if necessary
    return post_lead_lag(result, default)