概述
df['b']='' df['shift']='' df['shift']=df['open'].shift(-1) df['b']=df[x for x in df['shift'] if df["MA10"]>df["MA100"]]
>>> df = pd.read_csv("bondstack.csv")
>>> df["shift"] = df["open"].shift(-1)
>>> df["b"] = df.apply(lambda row: row["shift"] if row["MA10"] > row["MA100"] else np.nan,axis=1)哪个产生
>>> df[["MA10","MA100","shift","b"]][:10]
MA10 MA100 shift b
0 16.915625 17.405625 16.734375 NaN
1 16.871875 17.358750 17.171875 NaN
2 16.893750 17.317187 17.359375 NaN
3 16.950000 17.279062 17.359375 NaN
4 17.137500 17.254062 18.640625 NaN
5 17.365625 17.229063 18.921875 18.921875
6 17.550000 17.200312 18.296875 18.296875
7 17.681250 17.177500 18.640625 18.640625
8 17.812500 17.159375 18.609375 18.609375
9 17.943750 17.142813 18.234375 18.234375对于更加矢量化的方法,您可以使用
>>> df = pd.read_csv("bondstack.csv")
>>> df["b"] = np.nan
>>> df["b"][df["MA10"] > df["MA100"]] = df["open"].shift(-1)或者我的首选方法：
>>> df = pd.read_csv("bondstack.csv")
>>> df["b"] = df["open"].shift(-1).where(df["MA10"] > df["MA100"])总结
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