我有一个每日库存数据的数据框，该数据框由datetimeindex索引。
有多个库存条目，因此有重复的datetimeindex值。
我正在寻找一种方法：
我已经能够管理前三个操作。我在遇到麻烦时将结果存储在原始数据帧中。
为了说明这一点，我创建了一个玩具数据集，其中包含一个“虚拟”索引（idx）列，该列稍后将用于帮助在第三个代码块中创建所需的输出。
import random
import pandas as pd
import numpy as np
datelist = pd.date_range(pd.datetime(2018,1,1), periods=PER).to_pydatetime().tolist() * 2
ids = [random.choice(['A', 'B']) for i in range(len(datelist))]
prices = random.sample(range(200), len(datelist))
idx = range(len(datelist))
df1 = pd.DataFrame(data=zip(idx, ids, prices), index=datelist, columns='idx label prices'.split())
print(df1.head(10))
df1
idx label prices
2018-01-01 0 B 40
2018-01-02 1 A 190
2018-01-03 2 A 159
2018-01-04 3 A 25
2018-01-05 4 A 89
2018-01-06 5 B 164
...
2018-01-31 30 A 102
2018-02-01 31 A 117
2018-02-02 32 A 120
2018-02-03 33 B 75
2018-02-04 34 B 170
...
所需的输出
idx label prices monthly_return
2018-01-01 0 B 40 0.000000
2018-01-02 1 A 190 0.000000
2018-01-03 2 A 159 0.000000
2018-01-04 3 A 25 0.000000
2018-01-05 4 A 89 0.000000
2018-01-06 5 B 164 0.000000
...
2018-01-31 30 A 102 -0.098039
2018-02-01 31 A 117 0.000000
2018-02-02 32 A 120 0.000000
...
2018-02-26 56 B 152 0.000000
2018-02-27 57 B 2 0.000000
2018-02-28 58 B 49 -0.040816
2018-03-01 59 B 188 0.000000
...
2018-01-28 89 A 88 0.000000
2018-01-29 90 A 26 0.000000
2018-01-30 91 B 128 0.000000
2018-01-31 92 A 144 -0.098039
...
2018-02-26 118 A 92 0.000000
2018-02-27 119 B 111 0.000000
2018-02-28 120 B 34 -0.040816
...
到目前为止，我尝试过的是：
dfX = df1.copy(deep=True)
dfX = df1.groupby('label').resample('M')['prices'].last().pct_change(1).shift(-1)
print(dfX)
哪个输出：
label
A 2018-01-31 -0.067961
2018-02-28 -0.364583
2018-03-31 0.081967
B 2018-01-31 1.636364
2018-02-28 -0.557471
2018-03-31 NaN
这与我想做的非常接近，但是我只能从月末获得pct_change数据，这很烦人以新列的形式存储在原始数据帧（df1）中。
类似的事情不起作用：
dfX = df1.copy(deep=True)
dfX['monthly_return'] = df1.groupby('label').resample('M')['prices'].last().pct_change(1).shift(-1)
因为它会产生错误：
TypeError: incompatible index of inserted column with frame index
我已经考虑将“ monthly_return”数据“上采样”回每日序列，但是由于原始数据集可能缺少日期（例如周末），因此这可能最终导致上述错误。此外，重置索引以清除此错误仍然会产生问题，因为分组的dfX的行数/频率与原始df1的行数/频率（每天的频率）不同。
我有预感，这可以通过使用多索引和数据帧合并来完成，但是我不确定如何去做。
答案 0 :(得分：0)
这会生成我想要的输出，但是它不像我期望的那样干净
df1的生成与以前相同（有问题的代码给出）：
idx label prices
2018-01-01 0 A 145
2018-01-02 1 B 86
2018-01-03 2 B 141
...
2018-01-25 86 B 12
2018-01-26 87 B 71
2018-01-27 88 B 186
2018-01-28 89 B 151
2018-01-29 90 A 161
2018-01-30 91 B 143
2018-01-31 92 B 88
...
然后：
def fun(x):
dates = x.date
x = x.set_index('date', drop=True)
x['monthly_return'] = x.resample('M').last()['prices'].pct_change(1).shift(-1)
x = x.reindex(dates)
return x
dfX = df1.copy(deep=True)
dfX.reset_index(inplace=True)
dfX.columns = 'date idx label prices'.split()
dfX = dfX.groupby('label').apply(fun).droplevel(level='label')
print(dfX)
输出所需结果（未排序）
idx label prices monthly_return
date
2018-01-01 0 A 145 NaN
2018-01-06 5 A 77 NaN
2018-01-08 7 A 48 NaN
2018-01-09 8 A 31 NaN
2018-01-11 10 A 20 NaN
2018-01-12 11 A 27 NaN
2018-01-14 13 A 109 NaN
2018-01-15 14 A 166 NaN
2018-01-17 16 A 130 NaN
2018-01-18 17 A 139 NaN
2018-01-19 18 A 191 NaN
2018-01-21 20 A 164 NaN
2018-01-22 21 A 112 NaN
2018-01-23 22 A 167 NaN
2018-01-25 24 A 140 NaN
2018-01-26 25 A 42 NaN
2018-01-30 29 A 107 NaN
2018-02-04 34 A 9 NaN
2018-02-07 37 A 84 NaN
2018-02-08 38 A 23 NaN
2018-02-10 40 A 30 NaN
2018-02-12 42 A 89 NaN
2018-02-15 45 A 79 NaN
2018-02-16 46 A 115 NaN
2018-02-19 49 A 197 NaN
2018-02-21 51 A 11 NaN
2018-02-26 56 A 111 NaN
2018-02-27 57 A 126 NaN
2018-03-01 59 A 135 NaN
2018-03-03 61 A 28 NaN
2018-01-01 62 A 120 NaN
2018-01-03 64 A 170 NaN
2018-01-05 66 A 45 NaN
2018-01-07 68 A 173 NaN
2018-01-08 69 A 158 NaN
2018-01-09 70 A 63 NaN
2018-01-11 72 A 62 NaN
2018-01-12 73 A 168 NaN
2018-01-14 75 A 169 NaN
2018-01-15 76 A 142 NaN
2018-01-17 78 A 83 NaN
2018-01-18 79 A 96 NaN
2018-01-21 82 A 25 NaN
2018-01-22 83 A 90 NaN
2018-01-23 84 A 59 NaN
2018-01-29 90 A 161 NaN
2018-02-01 93 A 150 NaN
2018-02-04 96 A 85 NaN
2018-02-06 98 A 124 NaN
2018-02-14 106 A 195 NaN
2018-02-16 108 A 136 NaN
2018-02-17 109 A 134 NaN
2018-02-18 110 A 183 NaN
2018-02-19 111 A 32 NaN
2018-02-24 116 A 102 NaN
2018-02-25 117 A 72 NaN
2018-02-27 119 A 38 NaN
2018-03-02 122 A 137 NaN
2018-03-03 123 A 171 NaN
2018-01-02 1 B 86 NaN
2018-01-03 2 B 141 NaN
2018-01-04 3 B 189 NaN
2018-01-05 4 B 60 NaN
2018-01-07 6 B 1 NaN
2018-01-10 9 B 87 NaN
2018-01-13 12 B 44 NaN
2018-01-16 15 B 147 NaN
2018-01-20 19 B 92 NaN
2018-01-24 23 B 81 NaN
2018-01-27 26 B 190 NaN
2018-01-28 27 B 24 NaN
2018-01-29 28 B 116 NaN
2018-01-31 30 B 98 1.181818
2018-02-01 31 B 121 NaN
2018-02-02 32 B 110 NaN
2018-02-03 33 B 66 NaN
2018-02-05 35 B 4 NaN
2018-02-06 36 B 13 NaN
2018-02-09 39 B 114 NaN
2018-02-11 41 B 16 NaN
2018-02-13 43 B 174 NaN
2018-02-14 44 B 78 NaN
2018-02-17 47 B 144 NaN
2018-02-18 48 B 14 NaN
2018-02-20 50 B 133 NaN
2018-02-22 52 B 156 NaN
2018-02-23 53 B 159 NaN
2018-02-24 54 B 177 NaN
2018-02-25 55 B 43 NaN
2018-02-28 58 B 19 -0.338542
2018-03-02 60 B 127 NaN
2018-01-02 63 B 2 NaN
2018-01-04 65 B 97 NaN
2018-01-06 67 B 8 NaN
2018-01-10 71 B 54 NaN
2018-01-13 74 B 106 NaN
2018-01-16 77 B 74 NaN
2018-01-19 80 B 188 NaN
2018-01-20 81 B 172 NaN
2018-01-24 85 B 51 NaN
2018-01-25 86 B 12 NaN
2018-01-26 87 B 71 NaN
2018-01-27 88 B 186 NaN
2018-01-28 89 B 151 NaN
2018-01-30 91 B 143 NaN
2018-01-31 92 B 88 1.181818
2018-02-02 94 B 75 NaN
2018-02-03 95 B 103 NaN
2018-02-05 97 B 82 NaN
2018-02-07 99 B 128 NaN
2018-02-08 100 B 123 NaN
2018-02-09 101 B 52 NaN
2018-02-10 102 B 18 NaN
2018-02-11 103 B 21 NaN
2018-02-12 104 B 50 NaN
2018-02-13 105 B 64 NaN
2018-02-15 107 B 185 NaN
2018-02-20 112 B 125 NaN
2018-02-21 113 B 108 NaN
2018-02-22 114 B 132 NaN
2018-02-23 115 B 180 NaN
2018-02-26 118 B 67 NaN
2018-02-28 120 B 192 -0.338542
2018-03-01 121 B 58 NaN
也许有一种更简洁，更Python化的方式来做到这一点。