Python 使用np.select创建具有多索引dataframe的新列

我正试图确定我的数据是否越过了一条线，从哪个方向。我使用np.select在单个索引帧上工作，但是，当我尝试在多索引数据帧上执行相同操作时，我得到了所有NaN

                    Close   Hcross
Symbol Date                    
AAPL   2019-12-02   264.16  NaN
       2019-12-03   259.45  NaN
       2019-12-04   261.74  NaN
       2019-12-05   265.58  NaN
       2019-12-06   270.71  NaN
       2019-12-09   266.92  NaN
       2019-12-10   268.48  NaN
       2019-12-11   270.77  NaN
       2019-12-12   271.46  NaN
       2019-12-13   275.15  NaN
AMZN   2019-12-02  1781.60  NaN
       2019-12-03  1769.96  NaN
       2019-12-04  1760.69  NaN
       2019-12-05  1740.48  NaN
       2019-12-06  1751.60  NaN
       2019-12-09  1749.51  NaN
       2019-12-10  1739.21  NaN
       2019-12-11  1748.72  NaN
       2019-12-12  1760.33  NaN
       2019-12-13  1760.94  NaN

这是我的助手函数：

def calc_crossings_helper(df, line):
# define crossing conditions - corresponding choices are [1,-1] to denote direction, otherwise NaN
line_crossed_cond = [(df['Close'] < df[line]) & (df['Close'].shift(1) > df[line].shift(1)),
                     (df['Close'] > df[line]) & (df['Close'].shift(1) < df[line].shift(1))] 
return np.select(line_crossed_cond, [1, -1], default = np.nan)

                    Close   Hcross
Symbol Date                    
AAPL   2019-12-02   264.16  NaN
       2019-12-03   259.45  NaN
       2019-12-04   261.74  NaN
       2019-12-05   265.58  NaN
       2019-12-06   270.71  NaN
       2019-12-09   266.92  NaN
       2019-12-10   268.48  NaN
       2019-12-11   270.77  NaN
       2019-12-12   271.46  NaN
       2019-12-13   275.15  NaN
AMZN   2019-12-02  1781.60  NaN
       2019-12-03  1769.96  NaN
       2019-12-04  1760.69  NaN
       2019-12-05  1740.48  NaN
       2019-12-06  1751.60  NaN
       2019-12-09  1749.51  NaN
       2019-12-10  1739.21  NaN
       2019-12-11  1748.72  NaN
       2019-12-12  1760.33  NaN
       2019-12-13  1760.94  NaN

helper函数返回：

Symbol
AAPL    [nan, nan, -1.0, nan, nan, 1.0, nan, -1.0, nan...
AMZN    [nan, nan, nan, nan, nan, nan, nan, -1.0, nan,...

                    Close   Hcross
Symbol Date                    
AAPL   2019-12-02   264.16  NaN
       2019-12-03   259.45  NaN
       2019-12-04   261.74  NaN
       2019-12-05   265.58  NaN
       2019-12-06   270.71  NaN
       2019-12-09   266.92  NaN
       2019-12-10   268.48  NaN
       2019-12-11   270.77  NaN
       2019-12-12   271.46  NaN
       2019-12-13   275.15  NaN
AMZN   2019-12-02  1781.60  NaN
       2019-12-03  1769.96  NaN
       2019-12-04  1760.69  NaN
       2019-12-05  1740.48  NaN
       2019-12-06  1751.60  NaN
       2019-12-09  1749.51  NaN
       2019-12-10  1739.21  NaN
       2019-12-11  1748.72  NaN
       2019-12-12  1760.33  NaN
       2019-12-13  1760.94  NaN

但是df['Hcross']列被分配了所有的NaN

                    Close   Hcross
Symbol Date                    
AAPL   2019-12-02   264.16  NaN
       2019-12-03   259.45  NaN
       2019-12-04   261.74  NaN
       2019-12-05   265.58  NaN
       2019-12-06   270.71  NaN
       2019-12-09   266.92  NaN
       2019-12-10   268.48  NaN
       2019-12-11   270.77  NaN
       2019-12-12   271.46  NaN
       2019-12-13   275.15  NaN
AMZN   2019-12-02  1781.60  NaN
       2019-12-03  1769.96  NaN
       2019-12-04  1760.69  NaN
       2019-12-05  1740.48  NaN
       2019-12-06  1751.60  NaN
       2019-12-09  1749.51  NaN
       2019-12-10  1739.21  NaN
       2019-12-11  1748.72  NaN
       2019-12-12  1760.33  NaN
       2019-12-13  1760.94  NaN

---

我想我需要以某种方式展平从helper函数返回的数组，但我不知道如何展平。

一个简单的修复方法是返回一个像DataFrame这样的索引序列。这提供了正确的对齐方式，因为np.select返回与数据帧长度相同的数组

                    Close   Hcross
Symbol Date                    
AAPL   2019-12-02   264.16  NaN
       2019-12-03   259.45  NaN
       2019-12-04   261.74  NaN
       2019-12-05   265.58  NaN
       2019-12-06   270.71  NaN
       2019-12-09   266.92  NaN
       2019-12-10   268.48  NaN
       2019-12-11   270.77  NaN
       2019-12-12   271.46  NaN
       2019-12-13   275.15  NaN
AMZN   2019-12-02  1781.60  NaN
       2019-12-03  1769.96  NaN
       2019-12-04  1760.69  NaN
       2019-12-05  1740.48  NaN
       2019-12-06  1751.60  NaN
       2019-12-09  1749.51  NaN
       2019-12-10  1739.21  NaN
       2019-12-11  1748.72  NaN
       2019-12-12  1760.33  NaN
       2019-12-13  1760.94  NaN

def calc_crossings_helper(df, line):
    # define crossing conditions - corresponding choices are [1,-1] to denote direction, otherwise NaN
    line_crossed_cond = [(df['Close'] < df[line]) & (df['Close'].shift(1) > df[line].shift(1)),
                         (df['Close'] > df[line]) & (df['Close'].shift(1) < df[line].shift(1))] 

    return pd.Series(np.select(line_crossed_cond, [1, -1], default = np.nan), index=df.index)

更好的是，考虑到您的数据帧排序，不需要groupby.apply。您可以在符号级别使用shift来添加分组条件，因此只需要一个np.select调用

                    Close   Hcross
Symbol Date                    
AAPL   2019-12-02   264.16  NaN
       2019-12-03   259.45  NaN
       2019-12-04   261.74  NaN
       2019-12-05   265.58  NaN
       2019-12-06   270.71  NaN
       2019-12-09   266.92  NaN
       2019-12-10   268.48  NaN
       2019-12-11   270.77  NaN
       2019-12-12   271.46  NaN
       2019-12-13   275.15  NaN
AMZN   2019-12-02  1781.60  NaN
       2019-12-03  1769.96  NaN
       2019-12-04  1760.69  NaN
       2019-12-05  1740.48  NaN
       2019-12-06  1751.60  NaN
       2019-12-09  1749.51  NaN
       2019-12-10  1739.21  NaN
       2019-12-11  1748.72  NaN
       2019-12-12  1760.33  NaN
       2019-12-13  1760.94  NaN