Python 熊猫适用于多功能（pd系列和其他）

我有一个df，经过一些转换后看起来像这样

            date    2010-01-01  2010-01-08  2010-01-15  2010-01-22  2010-01-29  2010-02-05  2010-02-12  2010-02-19  2010-02-26  2010-03-05  ... 2020-08-28  2020-09-04  2020-09-11  2020-09-18  2020-09-25  2020-10-02  2020-10-09  2020-10-16  2020-10-23  2020-10-30
gestionnaire    nom_de_produit  code_isin_part  classification_produit                                                                                  
ACTIS ASSET MANAGEMENT  ALTERNA PLUS    FR0010466128    Sans classification 11.29   11.49   11.51   11.44   11.37   11.29   11.29   11.35   11.38   11.51   ... 15.87   15.84   15.87   15.86   15.72   15.77   15.88   15.91   15.95   15.75
GTA FRANCE  FR0010602615    Actions de pays de la zone euro 81.71   84.26   82.78   80.31   78.50   74.93   75.27   78.89   77.70   81.22   ... 127.12  124.41  126.55  126.78  122.47  125.28  127.37  127.91  127.28  120.65
RENTOBLIG   FR0010698472    Obligations et/ou titres de créances libellés en euros  nan 121.69  121.98  121.74  121.32  121.17  120.87  120.73  121.22  121.99  ... 158.62  158.85  158.89  159.00  157.70  158.14  159.56  159.57  160.21  159.44
FR0013180288    Obligations et/ou titres de créances libellés en euros  nan nan nan nan nan nan nan nan nan nan ... 105.32  105.48  105.51  105.59  104.73  105.03  105.98  105.99  106.43  105.92
SCR OPTIMUM FR0012453348    Sans classification nan nan nan nan nan nan nan nan nan nan ... 92,562.20   92,220.46   91,434.86   91,524.37   91,542.14   91,229.36   91,226.54   91,123.66   92,072.99   91,480.94
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
TRUSTEAM FINANCE    TRUSTEAM ROC EUROPE FR0011507219    Actions des pays de l'Union Européenne  nan nan nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan nan nan
FR0011896430    Actions des pays de l'Union Européenne  nan nan nan nan nan nan nan nan nan nan ... 197.71  191.56  193.59  197.04  194.41  199.31  201.71  203.33  197.97  190.02
FR0013281185    Actions des pays de l'Union Européenne  nan nan nan nan nan nan nan nan nan nan ... 191.65  185.65  187.59  190.90  188.31  193.02  195.31  196.84  191.61  183.89
TRUSTEAM ROC FLEX   FR0007018239    Sans classification 178.11  179.38  179.51  177.38  175.57  174.82  174.81  175.79  175.43  177.50  ... 227.48  228.16  228.81  228.20  226.08  227.64  230.02  230.21  230.49  227.22
TRUSTEAM ROC PME    FR0010220038    Actions de pays de la zone euro 87.59   89.57   90.00   89.82   90.08   88.22   88.30   88.64   88.69   90.75   ... 181.93  183.06  186.22  186.15  178.61  182.79  187.55  184.17  185.64  

我想计算日历性能和其他统计数据。为此，我愿意：

df.apply([
             lambda x: pd.Series([100*x[year].iloc[-1]/x[year].iloc[0] - 100 for year in pd.date_range(start='2015', end='2021', freq='A').strftime('%Y')]),
         ],
             axis=1)
         )

没关系

但当我补充说：

df.apply([lambda x: pd.Series([100*x[year].iloc[-1]/x[year].iloc[0] - 100 for year in pd.date_range(start='2015', end='2021', freq='A').strftime('%Y')]),
             lambda x: 100*x[-1]/x[-52]-100 
         ],
             axis=1)
         )

我收到一条错误消息，它是：

ValueError: cannot combine transform and aggregation operations

有什么想法吗