提问人：balaji 提问时间：11/17/2023 更新时间：11/17/2023 访问量：40
如何计算票务年龄明智的 Pandas 数据帧
How to Calculate Ticket Age wise Pandas Dataframe
问：
我有一个这样的数据帧：
**Created Time**
2023-11-01 23:53:56
2023-11-01 22:32:02
2023-11-01 21:51:56
2023-10-18 12:19:46
2023-10-18 10:35:36
2023-10-17 10:27:21
2023-10-17 13:38:50
2023-10-17 11:44:44
2023-10-17 16:44:29
2023-10-17 11:54:54
2023-10-02 19:57:10
2023-10-02 17:43:55
2023-10-02 15:07:20
2023-10-02 11:35:39
2023-10-02 14:47:03
2023-08-03 17:08:57
2023-08-02 15:20:19
2023-08-02 14:50:45
2023-07-31 10:24:35
2023-07-29 14:48:46
2023-05-05 11:28:30
2023-05-05 16:37:14
2023-05-05 10:18:40
2023-05-05 10:38:03
2023-05-04 14:31:12
2022-10-29 16:19:22
2022-10-28 16:08:44
2022-10-27 11:25:03
2022-10-25 17:51:59
2022-10-23 11:23:57
2022-10-23 11:23:57
如您所见，上面是创建时间列，我想要下面格式化我的数据帧。
Ticket Age Created Time
1-15 Days 2023-11-01 23:53:56
1-15 Days 2023-11-01 22:32:02
1-15 Days 2023-11-01 21:51:56
1-15 Days 2023-10-18 12:19:46
1-15 Days 2023-10-18 10:35:36
16-30 Days 2023-10-17 10:27:21
16-30 Days 2023-10-17 13:38:50
16-30 Days 2023-10-17 11:44:44
16-30 Days 2023-10-17 16:44:29
16-30 Days 2023-10-17 11:54:54
1 - 3 Months 2023-10-02 19:57:10
1 - 3 Months 2023-10-02 17:43:55
1 - 3 Months 2023-10-02 15:07:20
1 - 3 Months 2023-10-02 11:35:39
1 - 3 Months 2023-10-02 14:47:03
3 - 6 Months 2023-08-03 17:08:57
3 - 6 Months 2023-08-02 15:20:19
3 - 6 Months 2023-08-02 14:50:45
3 - 6 Months 2023-07-31 10:24:35
3 - 6 Months 2023-07-29 14:48:46
6 Months - 1Yr 2023-05-05 11:28:30
6 Months - 1Yr 2023-05-05 16:37:14
6 Months - 1Yr 2023-05-05 10:18:40
6 Months - 1Yr 2023-05-05 10:38:03
6 Months - 1Yr 2023-05-04 14:31:12
More than 1Yr 2022-10-29 16:19:22
More than 1Yr 2022-10-28 16:08:44
More than 1Yr 2022-10-27 11:25:03
More than 1Yr 2022-10-25 17:51:59
More than 1Yr 2022-10-23 11:23:57
More than 1Yr 2022-10-23 11:23:57
我需要新票年龄和年龄 1-15 天、16-30 天、1-3 个月、6 个月 -1 年、超过 1 年
如何添加年龄类别明智的年龄组
我该怎么做？
请指教。
答：
0赞
Utkarsh
11/17/2023
#1
from datetime import datetime
def getDaysCategory(date_a_string, referenceDate) :
date_a = datetime.strptime(date_a_string, '%Y-%m-%d %H:%M:%S')
days = abs((date_a - referenceDate).days)
if (days <= 15) :
return "1-15 Days"
if (days >= 16 and days <= 30) :
return "16-30 Days"
if (days >= 31 and days <= 90) :
return "1-3 Months"
if (days >= 91 and days <= 180) :
return "3-6 Months"
if (days >= 181 and days <= 365) :
return "6 Months-1 Yr"
if (days > 365) :
return "More than 1Yr"
today = datetime.today()
for date in dataframe :
days = getDaysCategory(date, today)
dataframe是您的 DataFrame 对象，只需遍历它，您就会得到您的日期类别。
评论
0赞
balaji
11/18/2023
谢谢你的评论，但不起作用
0赞
Utkarsh
11/20/2023
你能更具体地说明什么不起作用吗？您是否遇到任何错误，或者输出是意外的？
0赞
balaji
11/21/2023
ValueError：时间数据“创建时间”与格式“%Y-%m-%d %H：%M：%S”不匹配
0赞
Utkarsh
11/21/2023
上面的格式对我有用，你确定你正确地传递了每个日期，或者你能分享代码吗？
0赞
balaji
11/27/2023
如何共享代码....
1赞
jezrael
11/27/2023
#2
使用 to_datetime 表示日期时间，现在减去时间并通过 Series.dt.days 获取以天为单位的时间增量，因此可以传递到第一个新列的 DataFrame.insert 剪切：
df['Created Time'] = pd.to_datetime(df['Created Time'])
days = df['Created Time'].rsub(pd.Timestamp('now')).dt.days.abs()
bins = [0,15,30,90,180,365,np.inf]
labels=["1-15 Days","16-30 Days","1-3 Months","3-6 Months","6 Months-1 Yr","More than 1Yr"]
df.insert(0,'Ticket Age', pd.cut(days, bins=bins, labels=labels))
print (df)
Ticket Age Created Time
0 16-30 Days 2023-11-01 23:53:56
1 16-30 Days 2023-11-01 22:32:02
2 16-30 Days 2023-11-01 21:51:56
3 1-3 Months 2023-10-18 12:19:46
4 1-3 Months 2023-10-18 10:35:36
5 1-3 Months 2023-10-17 10:27:21
6 1-3 Months 2023-10-17 13:38:50
7 1-3 Months 2023-10-17 11:44:44
8 1-3 Months 2023-10-17 16:44:29
9 1-3 Months 2023-10-17 11:54:54
10 1-3 Months 2023-10-02 19:57:10
11 1-3 Months 2023-10-02 17:43:55
12 1-3 Months 2023-10-02 15:07:20
13 1-3 Months 2023-10-02 11:35:39
14 1-3 Months 2023-10-02 14:47:03
15 3-6 Months 2023-08-03 17:08:57
16 3-6 Months 2023-08-02 15:20:19
17 3-6 Months 2023-08-02 14:50:45
18 3-6 Months 2023-07-31 10:24:35
19 3-6 Months 2023-07-29 14:48:46
20 6 Months-1 Yr 2023-05-05 11:28:30
21 6 Months-1 Yr 2023-05-05 16:37:14
22 6 Months-1 Yr 2023-05-05 10:18:40
23 6 Months-1 Yr 2023-05-05 10:38:03
24 6 Months-1 Yr 2023-05-04 14:31:12
25 More than 1Yr 2022-10-29 16:19:22
26 More than 1Yr 2022-10-28 16:08:44
27 More than 1Yr 2022-10-27 11:25:03
28 More than 1Yr 2022-10-25 17:51:59
29 More than 1Yr 2022-10-23 11:23:57
30 More than 1Yr 2022-10-23 11:23:57
评论
1赞
balaji
11/27/2023
非常感谢你，我真的很感谢你的帮助。工作正常 .............
1赞
balaji
11/27/2023
这真是太棒了:)谢谢你的解释，耶斯拉尔先生
评论