我正在尝试遍历一堆不同的日期，并将其持续时间写出到预定义列。到目前为止我的代码看起来像这样（日期比显示的还多）：
First = ['ADate','BDate','CDate']
Second = ['BDate','CDate','DDate']
Result = ['AtoB','BtoC','CtoD']
for x in First:
for y in Second:
for z in Result:
Data[z] = (Data.y - Data.x).astype('timedelta64[m]')
这给了我错误“DataFrame对象没有属性'y。'”我做错了什么，我怎样才能使这个工作？谢谢！
答案 0 :(得分：2)
通过提供上下文，我可以假设您需要计算：
Data[z] = (y - x).astype('timedelta64[m]')
虽然，如果你能使问题更清楚一些，我们可以帮助你进一步......
答案 1 :(得分：2)
您似乎需要添加[]，但数据会被覆盖：
First = ['ADate','BDate','CDate']
Second = ['BDate','CDate','DDate']
Result = ['AtoB','BtoC','CtoD']
for x in First:
for y in Second:
for z in Result:
Data[z] = (Data[y] - Data[x]).astype('timedelta64[m]')
但如果需要列的所有组合：
for x in First:
for y in Second:
for z in Result:
#new column from x,y,z values
Data[z + x + y] = (Data[y] - Data[x]).astype('timedelta64[m]')
如果需要从First的{{1}}列中删除Second中的列，我认为需要重命名列以对齐数据：
d = dict(zip(First, Second))
Data[Result] = (Data[Second] - Data[First].rename(columns=d)).astype('timedelta64[m]')
样品：
rng = pd.date_range('2015-01-01', periods=5, freq='Min')
np.random.seed(201)
rng1 = np.random.choice(rng, size=5)
np.random.seed(110)
rng2 = np.random.choice(rng, size=5)
np.random.seed(112)
rng3 = np.random.choice(rng, size=5)
np.random.seed(114)
rng4 = np.random.choice(rng, size=5)
Data = pd.DataFrame({'ADate': rng1,
'BDate':rng2,
'CDate':rng3,
'DDate':rng4})
print (Data)
ADate BDate CDate \
0 2015-01-01 00:00:00 2015-01-01 00:00:00 2015-01-01 00:04:00
1 2015-01-01 00:01:00 2015-01-01 00:03:00 2015-01-01 00:03:00
2 2015-01-01 00:00:00 2015-01-01 00:01:00 2015-01-01 00:00:00
3 2015-01-01 00:01:00 2015-01-01 00:00:00 2015-01-01 00:02:00
4 2015-01-01 00:04:00 2015-01-01 00:00:00 2015-01-01 00:01:00
DDate
0 2015-01-01 00:02:00
1 2015-01-01 00:03:00
2 2015-01-01 00:02:00
3 2015-01-01 00:03:00
4 2015-01-01 00:04:00
First = ['ADate','BDate','CDate']
Second = ['BDate','CDate','DDate']
Result = ['AtoB','BtoC','CtoD']
d = dict(zip(First, Second,))
Data[Result] = (Data[Second] - Data[First].rename(columns=d)).astype('timedelta64[m]')
print (Data)
ADate BDate CDate \
0 2015-01-01 00:00:00 2015-01-01 00:00:00 2015-01-01 00:04:00
1 2015-01-01 00:01:00 2015-01-01 00:03:00 2015-01-01 00:03:00
2 2015-01-01 00:00:00 2015-01-01 00:01:00 2015-01-01 00:00:00
3 2015-01-01 00:01:00 2015-01-01 00:00:00 2015-01-01 00:02:00
4 2015-01-01 00:04:00 2015-01-01 00:00:00 2015-01-01 00:01:00
DDate AtoB BtoC CtoD
0 2015-01-01 00:02:00 0.0 4.0 -2.0
1 2015-01-01 00:03:00 2.0 0.0 0.0
2 2015-01-01 00:02:00 1.0 -1.0 2.0
3 2015-01-01 00:03:00 -1.0 2.0 1.0
4 2015-01-01 00:04:00 -4.0 1.0 3.0
for x in First:
for y in Second:
for z in Result:
Data[z] = (Data[y] - Data[x]).astype('timedelta64[m]')
print (Data)
ADate BDate CDate \
0 2015-01-01 00:00:00 2015-01-01 00:00:00 2015-01-01 00:04:00
1 2015-01-01 00:01:00 2015-01-01 00:03:00 2015-01-01 00:03:00
2 2015-01-01 00:00:00 2015-01-01 00:01:00 2015-01-01 00:00:00
3 2015-01-01 00:01:00 2015-01-01 00:00:00 2015-01-01 00:02:00
4 2015-01-01 00:04:00 2015-01-01 00:00:00 2015-01-01 00:01:00
DDate AtoB BtoC CtoD
0 2015-01-01 00:02:00 -2.0 -2.0 -2.0
1 2015-01-01 00:03:00 0.0 0.0 0.0
2 2015-01-01 00:02:00 2.0 2.0 2.0
3 2015-01-01 00:03:00 1.0 1.0 1.0
4 2015-01-01 00:04:00 3.0 3.0 3.0
for x in First:
for y in Second:
for z in Result:
Data[x + '_' + y + '_' + z] = (Data[y] - Data[x]).astype('timedelta64[m]')
print (Data)
ADate BDate CDate \
0 2015-01-01 00:00:00 2015-01-01 00:00:00 2015-01-01 00:04:00
1 2015-01-01 00:01:00 2015-01-01 00:03:00 2015-01-01 00:03:00
2 2015-01-01 00:00:00 2015-01-01 00:01:00 2015-01-01 00:00:00
3 2015-01-01 00:01:00 2015-01-01 00:00:00 2015-01-01 00:02:00
4 2015-01-01 00:04:00 2015-01-01 00:00:00 2015-01-01 00:01:00
DDate ADate_BDate_AtoB ADate_BDate_BtoC ADate_BDate_CtoD \
0 2015-01-01 00:02:00 0.0 0.0 0.0
1 2015-01-01 00:03:00 2.0 2.0 2.0
2 2015-01-01 00:02:00 1.0 1.0 1.0
3 2015-01-01 00:03:00 -1.0 -1.0 -1.0
4 2015-01-01 00:04:00 -4.0 -4.0 -4.0
ADate_CDate_AtoB ADate_CDate_BtoC ADate_CDate_CtoD ... \
0 4.0 4.0 4.0 ...
1 2.0 2.0 2.0 ...
2 0.0 0.0 0.0 ...
3 1.0 1.0 1.0 ...
4 -3.0 -3.0 -3.0 ...
BDate_DDate_CtoD CDate_BDate_AtoB CDate_BDate_BtoC CDate_BDate_CtoD \
0 2.0 -4.0 -4.0 -4.0
1 0.0 0.0 0.0 0.0
2 1.0 1.0 1.0 1.0
3 3.0 -2.0 -2.0 -2.0
4 4.0 -1.0 -1.0 -1.0
CDate_CDate_AtoB CDate_CDate_BtoC CDate_CDate_CtoD CDate_DDate_AtoB \
0 0.0 0.0 0.0 -2.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 2.0
3 0.0 0.0 0.0 1.0
4 0.0 0.0 0.0 3.0
CDate_DDate_BtoC CDate_DDate_CtoD
0 -2.0 -2.0
1 0.0 0.0
2 2.0 2.0
3 1.0 1.0
4 3.0 3.0
[5 rows x 31 columns]