我正在尝试创建一个新变量，该变量在以下数据帧上执行年月之间的SALES_AMOUNT差异。我认为我的代码应该考虑使用这个groupby但我不知道如何在groupby之后添加条件[df2 df.Control - df.Control.shift(1) == 12]以便在年份之间执行正确的差异
df['LY'] = df.groupby(['month']).SALES_AMOUNT.shift(1)
数据帧：
SALES_AMOUNT Store Control year month
0 16793.14 A 3 2013 3
1 42901.61 A 5 2013 5
2 63059.72 A 6 2013 6
3 168471.43 A 10 2013 10
4 58570.72 A 11 2013 11
5 67526.71 A 12 2013 12
6 50649.07 A 14 2014 2
7 48819.97 A 18 2014 6
8 97100.77 A 19 2014 7
9 67778.40 A 21 2014 9
10 90327.52 A 22 2014 10
11 75703.12 A 23 2014 11
12 26098.50 A 24 2014 12
13 81429.36 A 25 2015 1
14 19539.85 A 26 2015 2
15 71727.66 A 27 2015 3
16 20117.79 A 28 2015 4
17 44252.19 A 29 2015 6
18 68578.82 A 30 2015 7
19 91483.39 A 31 2015 8
20 39220.87 A 32 2015 10
21 12224.11 A 33 2015 11
结果应如下所示：
SALES_AMOUNT Store Control year month year_diff
0 16793.14 A 3 2013 3 Nan
1 42901.61 A 5 2013 5 Nan
2 63059.72 A 6 2013 6 Nan
3 168471.43 A 10 2013 10 Nan
4 58570.72 A 11 2013 11 Nan
5 67526.71 A 12 2013 12 Nan
6 50649.07 A 14 2014 2 Nan
7 48819.97 A 18 2014 6 -14239.75
8 97100.77 A 19 2014 7 Nan
9 67778.40 A 21 2014 9 Nan
10 90327.52 A 22 2014 10 -78143.91
11 75703.12 A 23 2014 11 17132.4
12 26098.50 A 24 2014 12 -41428.21
13 81429.36 A 25 2015 1 Nan
14 19539.85 A 26 2015 2 -31109.22
15 71727.66 A 27 2015 3 Nan
16 20117.79 A 28 2015 4 Nan
17 44252.19 A 29 2015 6 -4567.78
18 68578.82 A 30 2015 7 -28521.95
19 91483.39 A 31 2015 8 Nan
20 39220.87 A 32 2015 10 -51106.65
21 12224.11 A 33 2015 11 -63479.01
答案 0 :(得分：1)
我认为您正在寻找的是以下内容：
df = df.sort_values(by=['month', 'year'])
df['SALES_AMOUNT_shifted'] = df.groupby(['month'])['SALES_AMOUNT'].shift(1).tolist()
df['LY'] = df['SALES_AMOUNT'] - df['SALES_AMOUNT_shifted']
按月份和年份排序后，月份组将以一致的方式组织，然后转换才有意义。
- 更新 -
应用上述解决方案后，您可以将年份差异大于1的所有实例设置为None。
df['year_diff'] = df['year'] - df.groupby(['month'])['year'].shift()
df['year_diff'] = df['year_diff'].fillna(0)
df.loc[df['year_diff'] != 1, 'LY'] = None
使用此功能，我将获得您添加的所需输出。
答案 1 :(得分：0)
这有用吗？我也非常感谢以熊猫为中心的解决方案，因为我花了一些时间在这上面并且无法想出一个。
df = pd.read_clipboard().set_index('Control')
df['yoy_diff'] = np.nan
for i in df.index:
for j in df.index:
if j - i == 12:
df['yoy_diff'].loc[j] = df.loc[j, 'SALES_AMOUNT'] - df.loc[i, 'SALES_AMOUNT']
df
输出：
SALES_AMOUNT Store year month yoy_diff
Control
3 16793.14 A 2013 3 NaN
5 42901.61 A 2013 5 NaN
6 63059.72 A 2013 6 NaN
10 168471.43 A 2013 10 NaN
11 58570.72 A 2013 11 NaN
12 67526.71 A 2013 12 NaN
14 50649.07 A 2014 2 NaN
18 48819.97 A 2014 6 -14239.75
19 97100.77 A 2014 7 NaN
21 67778.40 A 2014 9 NaN
22 90327.52 A 2014 10 -78143.91
23 75703.12 A 2014 11 17132.40
24 26098.50 A 2014 12 -41428.21
25 81429.36 A 2015 1 NaN
26 19539.85 A 2015 2 -31109.22
27 71727.66 A 2015 3 NaN
28 20117.79 A 2015 4 NaN
29 44252.19 A 2015 6 NaN
30 68578.82 A 2015 7 19758.85
31 91483.39 A 2015 8 -5617.38
32 39220.87 A 2015 10 NaN
33 12224.11 A 2015 11 -55554.29