我是python的初学者，因此我的问题可能会变得很简单。感谢您的支持或导致我遇到问题的任何线索。
问题：
大约有10个不同的州；订单跨不同的状态移动，状态结束时会生成时间戳。例如，下面有四个状态A，B，C，D。
A 10 AM
B 1 PM
C 4 Pm
D 5 PM
在B = 1PM -10AM = 3中花费的时间。
有时候，同一状态可能会发生多次。因此，我们需要一个变量来存储单个状态的时差值
到目前为止，已附加原始数据csv和我的代码。此计算需要执行多个命令。但是，为简单起见，我现在仅提供一个订单的数据。
样本数据：
Order States modified_at
1 Resolved 2018-06-18T15:05:52.2460000
1 Edited 2018-05-24T21:44:07.9030000
1 Pending PO Creation 2018-06-06T19:52:51.5990000
1 Assigned 2018-05-24T17:46:03.2090000
1 Edited 2018-06-04T15:02:57.5130000
1 Draft 2018-05-24T17:45:07.9960000
1 PO Placed 2018-06-06T20:49:37.6540000
1 Edited 2018-06-04T11:18:13.9830000
1 Edited 2018-05-24T17:45:39.4680000
1 Pending Approval 2018-05-24T21:48:23.9180000
1 Edited 2018-06-06T21:00:19.6350000
1 Submitted 2018-05-24T21:44:37.8830000
1 Edited 2018-05-30T11:19:36.5460000
1 Edited 2018-05-25T11:16:07.9690000
1 Edited 2018-05-24T21:43:35.0770000
1 Assigned 2018-06-07T18:39:00.2580000
1 Pending Review 2018-05-24T17:45:10.5980000
1 Pending PO Submission 2018-06-06T14:16:26.6580000
我尝试的代码：
import pandas as pd
import datetime as datetime
from dateutil.relativedelta import relativedelta
fileName = "SamplePR.csv"
df = pd.read_csv(fileName, delimiter=',')
df['modified_at'] = pd.to_datetime(df.modified_at)
df = df.sort_values(by='modified_at')
df = df.reset_index(drop=True)
df1 = df[:-1]
df2 = df[1:]
dfm1 = df1['modified_at']
dfm2 = df2['modified_at']
dfm1 = dfm1.reset_index(drop=True)
dfm2 = dfm2.reset_index(drop=True)
for i in range(len(df)-1):
start = datetime.datetime.strptime(str(dfm1[i]), '%Y-%m-%d %H:%M:%S')
ends = datetime.datetime.strptime(str(dfm2[i]), '%Y-%m-%d %H:%M:%S')
diff = relativedelta(ends, start)
print (diff)
到目前为止，我尝试按时间对列表进行排序，然后计算2种状态之间的差异。如果有人可以提供逻辑帮助或指出正确的方向，我们将不胜感激
答案 0 :(得分：1)
您可以使用pandas中的diff来获取两行之间的差异
这是示例代码。
In [1]: import pandas as pd
In [2]: from io import StringIO
In [3]: data = StringIO('''Order,States,modified_at
...: 1,Resolved,2018-06-18T15:05:52.2460000
...: 1,Edited,2018-05-24T21:44:07.9030000
...: 1,Pending PO Creation,2018-06-06T19:52:51.5990000
...: ''')
In [4]: df = pd.read_csv(data, sep=',')
In [5]: df['modified_at'] = pd.to_datetime(df['modified_at']) #convert the type to datetime
In [6]: df
Out[6]:
Order States modified_at
0 1 Resolved 2018-06-18 15:05:52.246
1 1 Edited 2018-05-24 21:44:07.903
2 1 Pending PO Creation 2018-06-06 19:52:51.599
In [7]: df['diff'] = df['modified_at'].diff() #get the diff and add to a new column
In [8]: df
Out[8]:
Order States modified_at diff
0 1 Resolved 2018-06-18 15:05:52.246 NaT
1 1 Edited 2018-05-24 21:44:07.903 -25 days +06:38:15.657000
2 1 Pending PO Creation 2018-06-06 19:52:51.599 12 days 22:08:43.696000
答案 1 :(得分：1)
欢迎访问，如果您只是要检查日期戳之间的时差，请使用to_datetime转换为datestamp并通过移动
进行区别index Order States modified_at
0 0 1 Resolved 2018-06-18 15:05:52.246
1 1 1 Edited 2018-05-24 21:44:07.903
2 0 1 Edited 2018-06-06 21:00:19.635
3 1 1 Submitted 2018-05-24 21:44:37.883
4 2 1 Edited 2018-05-30 11:19:36.546
5 3 1 Edited 2018-05-25 11:16:07.969
6 4 1 Edited 2018-05-24 21:43:35.077
7 5 1 Assigned 2018-06-07 18:39:00.258
df.modified_at = pd.to_datetime(df.modified_at)
df['time_spent'] = df.modified_at - df.modified_at.shift()
出局：
0 NaT
1 -25 days +06:38:15.657000
2 12 days 23:16:11.732000
3 -13 days +00:44:18.248000
4 5 days 13:34:58.663000
5 -6 days +23:56:31.423000
6 -1 days +10:27:27.108000
7 13 days 20:55:25.181000
Name: modified_at, dtype: timedelta64[ns]
您可以根据需要使用数据透视表
df.time_spent = df.time_spent.dt.seconds
pd.pivot_table(df,values='time_spent',index=['Order'],columns=['States'],aggfunc=np.sum)
出局：
States Assigned Edited Resolved Submitted
Order
0 NaN 83771.0 0.0 NaN
1 NaN 23895.0 NaN 2658.0
2 NaN 48898.0 NaN NaN
3 NaN 86191.0 NaN NaN
4 NaN 37647.0 NaN NaN
5 75325.0 NaN NaN NaN
答案 2 :(得分：-1)
$datetime1 = new DateTime('2016-11-30 03:55:06');//start time
$datetime2 = new DateTime('2016-11-30 11:55:06');//end time
$interval = $datetime1->diff($datetime2);
echo $interval->format('%Y years %m months %d days %H hours %i minutes %s seconds');//00 years 0 months 0 days 08 hours 0 minutes 0 seconds