递归计算DataFrame值

Question

我正在尝试"递归地"计算pandas数据帧的列值.

假设存在两个不同日期的数据,每个数据有10个观察值,并且您想要计算一些变量r,其中只给出r的第一个值(每天),并且您想要计算剩余的2*9个条目,而每个后续值取决于在前一个r和一个额外的'同时'变量'x'上.

第一个问题是我想单独执行每一天的计算,即我想pandas.groupby()在我的所有计算中使用该函数...但是当我尝试将数据子集化并使用该shift(1)函数时,我只得到"NaN"项

data.groupby(data.index)['r'] =   ( (1+data.groupby(data.index)['x']*0.25) * (1+data.groupby(data.index)['r'].shift(1)))

对于我的第二种方法,我使用for循环来遍历索引(日期):

for i in range(2,21):
    data[data['rank'] == i]['r'] =  ( (1+data[data['rank'] == i]['x']*0.25) * (1+data[data['rank'] == i]['r'].shift(1))

但是,这对我不起作用.有没有办法在DataFrames上执行这样的计算？也许像滚动申请？

数据:

df = pd.DataFrame({
  'rank' : [1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10],
  'x' : [0.00275,0.00285,0.0031,0.0036,0.0043,0.0052,0.0063,0.00755,0.00895,0.0105,0.0027,0.00285,0.0031,0.00355,0.00425,0.0051,0.00615,0.00735,0.00875,0.0103],
  'r' : [0.00158,'NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN',0.001485,'NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN']
  },index=['2014-01-02', '2014-01-02', '2014-01-02', '2014-01-02',
           '2014-01-02', '2014-01-02', '2014-01-02', '2014-01-02',
           '2014-01-02', '2014-01-02', '2014-01-03', '2014-01-03',
           '2014-01-03', '2014-01-03', '2014-01-03', '2014-01-03',
           '2014-01-03', '2014-01-03', '2014-01-03', '2014-01-03'])

Answer 1

要进行滚动应用，您可以使用pandas.groupby().apply(). 在应用程序中，您可以使用循环来进行每组计算。内部循环也可能用 完成scipy.lfilter，但我无法理解你所追求的确切公式，所以我只是把那部分放在一边。

代码：

def rolling_apply(group):
    r = [group.r.iloc[0]]
    for x in group.x:
        r.append((1 + r[-1]) * (1 + x * 0.25))
    group.r = r[1:]
    return group

df['R'] = df.groupby(df.index).apply(rolling_apply).r

结果：

                   r  rank        x          R
2014-01-02   0.00158     1  0.00275   1.002269
2014-01-02       NaN     2  0.00285   2.003695
2014-01-02       NaN     3  0.00310   3.006023
2014-01-02       NaN     4  0.00360   4.009628
2014-01-02       NaN     5  0.00430   5.015014
2014-01-02       NaN     6  0.00520   6.022833
2014-01-02       NaN     7  0.00630   7.033894
2014-01-02       NaN     8  0.00755   8.049058
2014-01-02       NaN     9  0.00895   9.069306
2014-01-02       NaN    10  0.01050  10.095737
2014-01-03  0.001485     1  0.00270   1.002161
2014-01-03       NaN     2  0.00285   2.003588
2014-01-03       NaN     3  0.00310   3.005915
2014-01-03       NaN     4  0.00355   4.009471
2014-01-03       NaN     5  0.00425   5.014793
2014-01-03       NaN     6  0.00510   6.022462
2014-01-03       NaN     7  0.00615   7.033259
2014-01-03       NaN     8  0.00735   8.048020
2014-01-03       NaN     9  0.00875   9.067813
2014-01-03       NaN    10  0.01030  10.093737

测试数据：

df = pd.DataFrame({
    'rank': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'x': [0.00275, 0.00285, 0.0031, 0.0036, 0.0043, 0.0052, 0.0063, 0.00755,
          0.00895, 0.0105, 0.0027, 0.00285, 0.0031, 0.00355, 0.00425,
          0.0051, 0.00615, 0.00735, 0.00875, 0.0103],
    'r': [0.00158, 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN',
          'NaN', 0.001485, 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN',
          'NaN', 'NaN']
}, index=['2014-01-02', '2014-01-02', '2014-01-02', '2014-01-02',
          '2014-01-02', '2014-01-02', '2014-01-02', '2014-01-02',
          '2014-01-02', '2014-01-02', '2014-01-03', '2014-01-03',
          '2014-01-03', '2014-01-03', '2014-01-03', '2014-01-03',
          '2014-01-03', '2014-01-03', '2014-01-03', '2014-01-03'])

更新：

既然知道了所需的实际递归方程，这里是应用函数的更新：

def rolling_apply(group):
    r = [group.r.iloc[0]]
    for x in group.x[:-1]:
        r.append((1 + r[-1]) * (1 + x * 0.25) - 1)
    group.r = r
    return group

df.r = df.groupby(df.index).apply(rolling_apply).r