Pandas - 如何將多索引數據框中的列縮放到每個級別= 0組的頂行

Question

我有一個多索引數據幀dfu ：

                      open   high     low   close
Date       Time
2016-11-28 09:43:00  26.03  26.03  26.030  26.030
           09:48:00  25.90  25.90  25.760  25.760
           09:51:00  26.00  26.00  25.985  25.985
2016-11-29 09:30:00  24.98  24.98  24.98  24.9800
           09:33:00  25.00  25.00  24.99  24.9900
           09:35:00  25.33  25.46  25.33  25.4147

我想創建一個新列，['closeScaled']，通過執行函數foo計算，使用['open']列中當前level = 0值的第一行和當前行['close ']作為參數。 我懷疑解決方案會涉及到以下內容：

dfu['closeScaled']=dfu.apply(lambda x: foo(*get first row of current date*[0],x[3]))

我似乎無法弄清楚當前級別= 0部分的第一行 。

如果foo是：

def foo(firstOpen,currentClose):
    return (currentClose / firstOpen)

那么我希望closeScaled列包含（截斷到4位小數）：

                      open   high     low   close  closeScaled
Date       Time
2016-11-28 09:43:00  26.03  26.03  26.030  26.030  1.0000
           09:48:00  25.90  25.90  25.760  25.760  0.9896
           09:51:00  26.00  26.00  25.985  25.985  0.9982
2016-11-29 09:30:00  24.98  24.98  24.98  24.9800  1.0000
           09:33:00  25.00  25.00  24.99  24.9900  1.0004
           09:35:00  25.33  25.46  25.33  25.4147  1.0174

Answer 1

您可以使用groupby創建的div Series除以transform first round和最后round ：

print (dfu.groupby(level=0)['open'].transform('first'))
Date        Time    
2016-11-28  09:43:00    26.03
            09:48:00    26.03
            09:51:00    26.03
2016-11-29  09:30:00    24.98
            09:33:00    24.98
            09:35:00    24.98
Name: open, dtype: float64

dfu['closeScaled'] = dfu.close.div(dfu.groupby(level=0)['open'].transform('first')).round(4)
print (dfu)
                      open   high     low    close  closeScaled
Date       Time                                                
2016-11-28 09:43:00  26.03  26.03  26.030  26.0300       1.0000
           09:48:00  25.90  25.90  25.760  25.7600       0.9896
           09:51:00  26.00  26.00  25.985  25.9850       0.9983
2016-11-29 09:30:00  24.98  24.98  24.980  24.9800       1.0000
           09:33:00  25.00  25.00  24.990  24.9900       1.0004
           09:35:00  25.33  25.46  25.330  25.4147       1.0174

如果需要將浮點值截斷為4位小數：

第一個倍數為10000 ，轉換為int並除以10000 。

dfu['closeScaled'] = dfu.close.div(dfu.groupby(level=0)['open'].transform('first'))
                              .mul(10000).astype(int).div(10000)
print (dfu)
                      open   high     low    close  closeScaled
Date       Time                                                
2016-11-28 09:43:00  26.03  26.03  26.030  26.0300       1.0000
           09:48:00  25.90  25.90  25.760  25.7600       0.9896
           09:51:00  26.00  26.00  25.985  25.9850       0.9982
2016-11-29 09:30:00  24.98  24.98  24.980  24.9800       1.0000
           09:33:00  25.00  25.00  24.990  24.9900       1.0004
           09:35:00  25.33  25.46  25.330  25.4147       1.0174

---

#http://stackoverflow.com/a/783927/2901002
def truncate(f, n):
    '''Truncates/pads a float f to n decimal places without rounding'''
    s = '{}'.format(f)
    if 'e' in s or 'E' in s:
        return '{0:.{1}f}'.format(f, n)
    i, p, d = s.partition('.')
    return '.'.join([i, (d+'0'*n)[:n]])

dfu['closeScaled'] = dfu.close.div(dfu.groupby(level=0)['open'].transform('first'))
                        .apply(lambda x: truncate(x,4)).astype(float)
print (dfu)
                      open   high     low    close  closeScaled
Date       Time                                                
2016-11-28 09:43:00  26.03  26.03  26.030  26.0300       1.0000
           09:48:00  25.90  25.90  25.760  25.7600       0.9896
           09:51:00  26.00  26.00  25.985  25.9850       0.9982
2016-11-29 09:30:00  24.98  24.98  24.980  24.9800       1.0000
           09:33:00  25.00  25.00  24.990  24.9900       1.0004
           09:35:00  25.33  25.46  25.330  25.4147       1.0174

Answer 2

使用groupby + apply + lambda

df.groupby(level=0).apply(
    lambda df: df.assign(closeScaled=df.close.div(df.open.iloc[0]).round(4))
)

                      open   high     low    close  closeScaled
Date       Time                                                
2016-11-28 09:43:00  26.03  26.03  26.030  26.0300       1.0000
           09:48:00  25.90  25.90  25.760  25.7600       0.9896
           09:51:00  26.00  26.00  25.985  25.9850       0.9983
2016-11-29 09:30:00  24.98  24.98  24.980  24.9800       1.0000
           09:33:00  25.00  25.00  24.990  24.9900       1.0004
           09:35:00  25.33  25.46  25.330  25.4147       1.0174