Pandas 转换为非 NaN 值并检查是否重复条目

如何解决Pandas 转换为非 NaN 值并检查是否重复条目

如果有人可以帮助我将 df.Decision 列隔离为一行中的单个“购买”或“出售”实例。例如，如果有 3 个“购买”决策，无论是否有 NaN 值将它们分开，我只需要保留第一个“购买”。类似的逻辑也适用于“卖出”。

当前数据

日期	ColA	ColB	ColC	决定
2018-03-21	41.6871345068477	39.1196017702354	39.8100609746974
2018-03-22	41.83569164767	39.1196017702354	39.8100609746974	购买
2018-04-02	42.0277334284587	39.5353679158337	39.8100609746974	购买
2018-04-30	41.0131864593112	42.1811215382421	40.3090368348783
2018-05-01	41.0131864593112	42.0844982888835	40.3090368348783
2018-05-02	41.0131864593112	41.9045373766682	40.3090368348783	购买
2018-09-28	54.0546533518404	50.7025748467743	48.5804868844005
2018-10-01	54.1167056669686	50.7652351622538	48.5804868844005
2018-10-02	54.179969640969	50.7993057048438	48.5804868844005	购买
2018-10-03	54.6021709547574	50.8035639654775	48.5804868844005	购买
2018-10-04	54.6021709547574	51.1600610997758	48.7459608850365
2018-11-01	53.4815867079232	53.8788384068764	50.8680059009101
2018-11-02	53.4012843800357	53.8545041548076	50.8680059009101	卖
2018-11-05	52.5179537180688	53.9007386980484	50.8680059009101	卖
2018-11-06	52.5179537180688	54.1130540704967	50.8680059009101	卖
2018-11-07	52.5179537180688	54.2608827598324	50.9081548909462
2018-11-08	52.381683825919	54.6830840736208	51.3303562047346	卖
2018-11-09	51.9022943297893	54.6830840736208	51.3303562047346	卖
2018-11-12	51.312945372196	54.869846946646	51.3303562047346	卖
2018-11-13	51.0272439215888	54.873497352104	51.3303562047346	卖
2019-02-28	40.0868369032957	37.9514787484214	42.9921818000566
2019-03-01	40.0917199269724	37.7384198717488	42.9921818000566
2019-03-04	40.5566646362643	37.6938570296322	42.9921818000566	购买
2019-04-23	48.1070706672322	43.6878883048808	40.3077255381675	购买
2019-04-24	48.1965810367431	43.817865832258	40.4377030655446
2019-04-25	48.1965810367431	43.9423243081189	40.5112749854225
2019-04-26	48.1965810367431	44.0116014371635	40.7923506041967	购买
2019-04-29	48.1965810367431	45.2089733480352	41.8874654967458

预期数据

日期	ColA	ColB	ColC	决定
2018-03-21	41.6871345068477	39.1196017702354	39.8100609746974
2018-03-22	41.83569164767	39.1196017702354	39.8100609746974	购买
2018-04-02	42.0277334284587	39.5353679158337	39.8100609746974
2018-04-30	41.0131864593112	42.1811215382421	40.3090368348783
2018-05-01	41.0131864593112	42.0844982888835	40.3090368348783
2018-05-02	41.0131864593112	41.9045373766682	40.3090368348783
2018-09-28	54.0546533518404	50.7025748467743	48.5804868844005
2018-10-01	54.1167056669686	50.7652351622538	48.5804868844005
2018-10-02	54.179969640969	50.7993057048438	48.5804868844005
2018-10-03	54.6021709547574	50.8035639654775	48.5804868844005
2018-10-04	54.6021709547574	51.1600610997758	48.7459608850365
2018-11-01	53.4815867079232	53.8788384068764	50.8680059009101
2018-11-02	53.4012843800357	53.8545041548076	50.8680059009101	卖
2018-11-05	52.5179537180688	53.9007386980484	50.8680059009101
2018-11-06	52.5179537180688	54.1130540704967	50.8680059009101
2018-11-07	52.5179537180688	54.2608827598324	50.9081548909462
2018-11-08	52.381683825919	54.6830840736208	51.3303562047346
2018-11-09	51.9022943297893	54.6830840736208	51.3303562047346
2018-11-12	51.312945372196	54.869846946646	51.3303562047346
2018-11-13	51.0272439215888	54.873497352104	51.3303562047346
2019-02-28	40.0868369032957	37.9514787484214	42.9921818000566
2019-03-01	40.0917199269724	37.7384198717488	42.9921818000566
2019-03-04	40.5566646362643	37.6938570296322	42.9921818000566	购买
2019-04-23	48.1070706672322	43.6878883048808	40.3077255381675
2019-04-24	48.1965810367431	43.817865832258	40.4377030655446
2019-04-25	48.1965810367431	43.9423243081189	40.5112749854225
2019-04-26	48.1965810367431	44.0116014371635	40.7923506041967
2019-04-29	48.1965810367431	45.2089733480352	41.8874654967458

为了解决这个问题，我开始使用以下逻辑，但我无法让它正常工作。

df[df.Decision.notnull()].shift().eq('Buy').Decision

解决方法

这些是决策不变的行：

rows = df['Decision'].ffill() == df['Decision'].ffill().shift(1)

将他们的决策标签转换为 NaN：

df.loc[rows,'Decision'] = np.nan