#CONCAT1 multiple dfs and add nan value
dfs = [street, city, state, zipcode]
nan_value = 'DROP'
adds_merge = pd.concat(dfs, join='outer', axis=1).fillna(nan_value)
#dropping the mass amount of duplicated columns created by concat above
adds_merge = adds_merge.loc[:,~adds_merge.columns.duplicated()]

#CONCAT2
def concat_ordered_columns(frames):
    columns_ordered = []
    for frame in frames:
        columns_ordered.extend(x for x in frame.columns if x not in columns_ordered)
    log = pd.concat(frames)
    return log[columns_ordered]
data = [search_merge,dl_merge]
elpi_joins = concat_ordered_columns(data)

#MERGE
dl_merge = eventcat_dl.merge(elastic_dl, how='inner', left_on=['datetime','type','user'], right_on=['datetime','type','user'])

#JOIN
vajoin = visit_action.merge(visit_in, how='inner', left_on=['idvisitor', 'datetime','idvisit'], right_on=['idvisitor', 'datetime','idvisit'])