python - 无法操作 datetime.timedelta(0, 3600)， block 值必须是 str，而不是 datetime.timedelta

Question

这里我有一个包含日期、时间和一个输入的数据集。这里我想添加特定时间的 timedelta 并添加到日期时间列。

所以这里首先我将指定时间转换为 00:00:00，它将作为开始时间。从那时起，我想添加一小时，一小时直到 6 的范围。然后我想将其添加到日期时间列中。

我编写了代码，但它给了我错误:无法操作 datetime.timedelta(0, 3600)， block 值必须是 str，而不是 datetime.timedelta

我的代码是:

data['date_time']= pd.to_datetime(data['date'] + " " + data['time'],
                        format='%d/%m/%Y %H:%M:%S', dayfirst=True)
mask = data['X3'].eq(5)
data['duration'] = data['date_time'].mask(mask, data['date_time'].dt.floor('d'))

T= pd.DataFrame({"data['duration']":[ "00:00:00" for i in range(3) ]},index=np.random.randint(0,100,3))
for it in range(1,4):
 Time = T +timedelta(hours=1*it)

出现错误:

TypeError                                 Traceback (most recent call last)
~\Anaconda3\lib\site-packages\pandas\core\ops.py in na_op(x, y)
   1466         try:
-> 1467             result = expressions.evaluate(op, str_rep, x, y, **eval_kwargs)
   1468         except TypeError:

~\Anaconda3\lib\site-packages\pandas\core\computation\expressions.py in evaluate(op, op_str, a, b, use_numexpr, **eval_kwargs)
    204     if use_numexpr:
--> 205         return _evaluate(op, op_str, a, b, **eval_kwargs)
    206     return _evaluate_standard(op, op_str, a, b)

~\Anaconda3\lib\site-packages\pandas\core\computation\expressions.py in _evaluate_numexpr(op, op_str, a, b, truediv, reversed, **eval_kwargs)
    119     if result is None:
--> 120         result = _evaluate_standard(op, op_str, a, b)
    121 

~\Anaconda3\lib\site-packages\pandas\core\computation\expressions.py in _evaluate_standard(op, op_str, a, b, **eval_kwargs)
     64     with np.errstate(all='ignore'):
---> 65         return op(a, b)
     66 

TypeError: must be str, not datetime.timedelta

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
~\Anaconda3\lib\site-packages\pandas\core\internals.py in eval(self, func, other, errors, try_cast, mgr)
   1414             with np.errstate(all='ignore'):
-> 1415                 result = get_result(other)
   1416 

~\Anaconda3\lib\site-packages\pandas\core\internals.py in get_result(other)
   1382             else:
-> 1383                 result = func(values, other)
   1384 

~\Anaconda3\lib\site-packages\pandas\core\ops.py in na_op(x, y)
   1494                     with np.errstate(all='ignore'):
-> 1495                         result[mask] = op(xrav, y)
   1496             else:

TypeError: must be str, not datetime.timedelta

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
<ipython-input-95-771f9e94063d> in <module>()
     13 T= pd.DataFrame({"data['duration']":[ "00:00:00" for i in range(3) ]},index=np.random.randint(0,100,3))
     14 for it in range(1,7):
---> 15     Time = T +timedelta(hours=1*it)

~\Anaconda3\lib\site-packages\pandas\core\ops.py in f(self, other, axis, level, fill_value)
   1527                 self = self.fillna(fill_value)
   1528 
-> 1529             return self._combine_const(other, na_op, try_cast=True)
   1530 
   1531     f.__name__ = op_name

~\Anaconda3\lib\site-packages\pandas\core\frame.py in _combine_const(self, other, func, errors, try_cast)
   4774         new_data = self._data.eval(func=func, other=other,
   4775                                    errors=errors,
-> 4776                                    try_cast=try_cast)
   4777         return self._constructor(new_data)
   4778 

~\Anaconda3\lib\site-packages\pandas\core\internals.py in eval(self, **kwargs)
   3685 
   3686     def eval(self, **kwargs):
-> 3687         return self.apply('eval', **kwargs)
   3688 
   3689     def quantile(self, **kwargs):

~\Anaconda3\lib\site-packages\pandas\core\internals.py in apply(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)
   3579 
   3580             kwargs['mgr'] = self
-> 3581             applied = getattr(b, f)(**kwargs)
   3582             result_blocks = _extend_blocks(applied, result_blocks)
   3583 

~\Anaconda3\lib\site-packages\pandas\core\internals.py in eval(self, func, other, errors, try_cast, mgr)
   1420             raise
   1421         except Exception as detail:
-> 1422             result = handle_error()
   1423 
   1424         # technically a broadcast error in numpy can 'work' by returning a

~\Anaconda3\lib\site-packages\pandas\core\internals.py in handle_error()
   1403                 raise TypeError(
   1404                     'Could not operate {other!r} with block values '
-> 1405                     '{detail!s}'.format(other=other, detail=detail))  # noqa
   1406             else:
   1407                 # return the values

TypeError: Could not operate datetime.timedelta(0, 3600) with block values must be str, not datetime.timedelta

我期望的输出是:

date	time	       x3		expected output           x3       (add) timedelta	
10/3/2018	6:15:00 	7	10/3/2018 6:15:00			
10/3/2018	6:45:00 	5	10/3/2018 6:45:00	     5	        0:00:00	
10/3/2018	7:45:00 	7	10/3/2018 7:45:00		              1:00:00	
10/3/2018	9:00:00 	7	10/3/2018 8:45:00		              2:00:00	
10/3/2018	9:25:00 	7	10/3/2018 9:30:00    second 5		          0:00:00
10/3/2018	9:30:00 	5	10/3/2018 9:45:00		              3:00:00	
10/3/2018	11:00:00	7	10/3/2018 10:30:00			                  1:00:00
10/3/2018	11:30:00	7	10/3/2018 10:45:00			                  2:00:00
10/3/2018	13:30:00	7	10/3/2018 10:45:00		            4:00:00	
10/3/2018	13:50:00	5	10/3/2018 11:30:00			                  3:00:00
10/3/2018	15:00:00	7	10/3/2018 12:30:00			                  4:00:00
10/3/2018	15:25:00	7				
10/3/2018	16:25:00	7				
10/3/2018	18:00:00	5				
10/3/2018	19:00:00	7				
10/3/2018	19:30:00	7

我的 csv 文件: My csv file

所以在这里我想通过添加这个时间增量来增加数据的长度。谁能帮我解决这个错误？

For the reference

expected output           x3       (add) timedelta	
	10/3/2018 6:15:00			
	10/3/2018 6:45:00	     5	              0:00:00	
	10/3/2018 7:45:00		              1:00:00	
	10/3/2018 8:45:00		              2:00:00	
	10/3/2018 9:30:00    second 5		                  0:00:00
	10/3/2018 9:45:00		              3:00:00	
	10/3/2018 10:30:00			                  1:00:00
	10/3/2018 10:45:00			                  2:00:00
	10/3/2018 10:45:00		            4:00:00	
	10/3/2018 11:30:00			                  3:00:00
	10/3/2018 12:30:00			                  4:00:00
				

Here you can till to 10:45:00 I need to give time range (4) after starting the new time also

Answer 1

我尝试创建没有循环的解决方案:

#datetime column
data['date_time']= pd.to_datetime(data['date'] + " " + data['time'],
                        format='%d/%m/%Y %H:%M:%S', dayfirst=True)
#set starts 00:00:00
mask = data['x3'].eq(5)
data['duration'] = data['date_time'].mask(mask, data['date_time'].dt.floor('d'))

#create helper group column
m = mask.cumsum()
#create counter per groups, but first group (values before first 5) are set to 0
data['g'] = data[m != 0].groupby(m).cumcount()
#all values > 5 aare set to 0, not > 6 because python counts from 0
data['g'] = data['g'].fillna(0).mask(data['g'] > 5, 0)
#get values of date_time only for 5 and forward filling it
first = data.loc[mask, 'date_time'].reindex(data.index, method='ffill')
#converting hours to timedeltas and add to duration column
data['duration'] = pd.to_timedelta(data['g'], unit='h') + first

<小时/>

print (data)
         date      time  x3           date_time            duration    g
0   10/3/2018   6:15:00   7 2018-03-10 06:15:00                 NaT  0.0
1   10/3/2018   6:45:00   5 2018-03-10 06:45:00 2018-03-10 06:45:00  0.0
2   10/3/2018   7:45:00   7 2018-03-10 07:45:00 2018-03-10 07:45:00  1.0
3   10/3/2018   9:00:00   7 2018-03-10 09:00:00 2018-03-10 08:45:00  2.0
4   10/3/2018   9:25:00   7 2018-03-10 09:25:00 2018-03-10 09:45:00  3.0
5   10/3/2018   9:30:00   5 2018-03-10 09:30:00 2018-03-10 09:30:00  0.0
6   10/3/2018  11:00:00   7 2018-03-10 11:00:00 2018-03-10 10:30:00  1.0
7   10/3/2018  11:30:00   7 2018-03-10 11:30:00 2018-03-10 11:30:00  2.0
8   10/3/2018  13:30:00   7 2018-03-10 13:30:00 2018-03-10 12:30:00  3.0
9   10/3/2018  13:50:00   5 2018-03-10 13:50:00 2018-03-10 13:50:00  0.0
10  10/3/2018  15:00:00   7 2018-03-10 15:00:00 2018-03-10 14:50:00  1.0
11  10/3/2018  15:25:00   7 2018-03-10 15:25:00 2018-03-10 15:50:00  2.0
12  10/3/2018  16:25:00   7 2018-03-10 16:25:00 2018-03-10 16:50:00  3.0
13  10/3/2018  18:00:00   5 2018-03-10 18:00:00 2018-03-10 18:00:00  0.0
14  10/3/2018  19:00:00   7 2018-03-10 19:00:00 2018-03-10 19:00:00  1.0
15  10/3/2018  19:30:00   7 2018-03-10 19:30:00 2018-03-10 20:00:00  2.0
16  10/3/2018  20:00:00   7 2018-03-10 20:00:00 2018-03-10 21:00:00  3.0
17  10/3/2018  22:05:00   7 2018-03-10 22:05:00 2018-03-10 22:00:00  4.0
18  10/3/2018  22:15:00   5 2018-03-10 22:15:00 2018-03-10 22:15:00  0.0
19  10/3/2018  23:40:00   7 2018-03-10 23:40:00 2018-03-10 23:15:00  1.0
20  10/4/2018   6:58:00   5 2018-04-10 06:58:00 2018-04-10 06:58:00  0.0
21  10/4/2018  13:00:00   7 2018-04-10 13:00:00 2018-04-10 07:58:00  1.0
22  10/4/2018  16:00:00   7 2018-04-10 16:00:00 2018-04-10 08:58:00  2.0
23  10/4/2018  17:00:00   7 2018-04-10 17:00:00 2018-04-10 09:58:00  3.0
24  10/4/2018  18:00:00   7 2018-04-10 18:00:00 2018-04-10 10:58:00  4.0
25  10/5/2018   7:00:00   7 2018-05-10 07:00:00 2018-04-10 11:58:00  5.0
26  10/5/2018   8:00:00   7 2018-05-10 08:00:00 2018-04-10 06:58:00  0.0
27  10/5/2018   9:00:00   7 2018-05-10 09:00:00 2018-04-10 06:58:00  0.0