递增数字递减日期列
Increasing Number Decreasing Date Column
需要帮助，我正在尝试根据用户输入获取日期列，它会随着总年龄列数的增加以一个月为间隔减少当前日期。
CurrentDf = {
"VIN": ['v1','v1','v1','v2','v2','v2','v2','v3','v3','v3'],
"Total Age": [10,10,10, 08,08,08,08,11,11,11],
"Months": [05,07,09,01,06,07,08,02,05,07],
"Monthly Revenue": [1108,4330,7121,1998,1783,9628, 2082,8763,5683, 5780]
}
Total_Age = [int(x) for x in input('Please enter Total Age?= ').split(',')]
当前日期：
VIN
Total Age
Months
Monthly Revenue
v1
10
05
1108
v1
10
07
4330
v1
10
09
7121
v2
08
01
1998
v2
08
06
1783
v2
08
07
9628
v2
08
08
2082
v3
11
02
8763
v3
11
05
5683
v3
11
07
5780
用户输入：[8]
输出DF:
VIN
Total Age
Months
Monthly Revenue
Date
v1
10
05
1108
2022-04-01
v1
10
07
4330
2022-04-01
v1
10
09
7121
2022-04-01
v2
08
01
1998
2022-05-01
v2
08
06
1783
2022-05-01
v2
08
07
9628
2022-05-01
v2
08
08
2082
2022-05-01
v3
11
02
8763
2022-03-01
v3
11
05
5683
2022-03-01
v3
11
07
5780
2022-03-01
如果我理解正确，请尝试对年龄进行排名以产生所需的月份偏移量：
input_age = int(input('Please enter Total Age?= '))
df["Date"] = df["Total Age"].astype(int).sub(input_age).rank(method='dense').apply(lambda x: pd.Timestamp.today().date()-pd.DateOffset(months=x-1))
input_age = 8 的输出：
>>> df
VIN Total Age Months Monthly Revenue Date
0 v1 10 05 1108 2022-04-01
1 v1 10 07 4330 2022-04-01
2 v1 10 09 7121 2022-04-01
3 v2 08 01 1998 2022-05-01
4 v2 08 06 1783 2022-05-01
5 v2 08 07 9628 2022-05-01
6 v2 08 08 2082 2022-05-01
7 v3 11 02 8763 2022-03-01
8 v3 11 05 5683 2022-03-01
9 v3 11 07 5780 2022-03-01
输入 df:
df = pd.DataFrame({"VIN": ['v1','v1','v1','v2','v2','v2','v2','v3','v3','v3'],
"Total Age": ['10','10','10','08','08','08','08','11','11','11'],
"Months": ['05','07','09','01','06','07','08','02','05','07'],
"Monthly Revenue": [1108,4330,7121,1998,1783,9628, 2082,8763,5683, 5780]
})
需要帮助，我正在尝试根据用户输入获取日期列，它会随着总年龄列数的增加以一个月为间隔减少当前日期。
CurrentDf = {
"VIN": ['v1','v1','v1','v2','v2','v2','v2','v3','v3','v3'],
"Total Age": [10,10,10, 08,08,08,08,11,11,11],
"Months": [05,07,09,01,06,07,08,02,05,07],
"Monthly Revenue": [1108,4330,7121,1998,1783,9628, 2082,8763,5683, 5780]
}
Total_Age = [int(x) for x in input('Please enter Total Age?= ').split(',')]
当前日期：
| VIN | Total Age | Months | Monthly Revenue |
|---|---|---|---|
| v1 | 10 | 05 | 1108 |
| v1 | 10 | 07 | 4330 |
| v1 | 10 | 09 | 7121 |
| v2 | 08 | 01 | 1998 |
| v2 | 08 | 06 | 1783 |
| v2 | 08 | 07 | 9628 |
| v2 | 08 | 08 | 2082 |
| v3 | 11 | 02 | 8763 |
| v3 | 11 | 05 | 5683 |
| v3 | 11 | 07 | 5780 |
用户输入：[8]
输出DF:
| VIN | Total Age | Months | Monthly Revenue | Date |
|---|---|---|---|---|
| v1 | 10 | 05 | 1108 | 2022-04-01 |
| v1 | 10 | 07 | 4330 | 2022-04-01 |
| v1 | 10 | 09 | 7121 | 2022-04-01 |
| v2 | 08 | 01 | 1998 | 2022-05-01 |
| v2 | 08 | 06 | 1783 | 2022-05-01 |
| v2 | 08 | 07 | 9628 | 2022-05-01 |
| v2 | 08 | 08 | 2082 | 2022-05-01 |
| v3 | 11 | 02 | 8763 | 2022-03-01 |
| v3 | 11 | 05 | 5683 | 2022-03-01 |
| v3 | 11 | 07 | 5780 | 2022-03-01 |
如果我理解正确，请尝试对年龄进行排名以产生所需的月份偏移量：
input_age = int(input('Please enter Total Age?= '))
df["Date"] = df["Total Age"].astype(int).sub(input_age).rank(method='dense').apply(lambda x: pd.Timestamp.today().date()-pd.DateOffset(months=x-1))
input_age = 8 的输出：
>>> df
VIN Total Age Months Monthly Revenue Date
0 v1 10 05 1108 2022-04-01
1 v1 10 07 4330 2022-04-01
2 v1 10 09 7121 2022-04-01
3 v2 08 01 1998 2022-05-01
4 v2 08 06 1783 2022-05-01
5 v2 08 07 9628 2022-05-01
6 v2 08 08 2082 2022-05-01
7 v3 11 02 8763 2022-03-01
8 v3 11 05 5683 2022-03-01
9 v3 11 07 5780 2022-03-01
输入 df:
df = pd.DataFrame({"VIN": ['v1','v1','v1','v2','v2','v2','v2','v3','v3','v3'],
"Total Age": ['10','10','10','08','08','08','08','11','11','11'],
"Months": ['05','07','09','01','06','07','08','02','05','07'],
"Monthly Revenue": [1108,4330,7121,1998,1783,9628, 2082,8763,5683, 5780]
})