Python 在列中设置日期时间值：类型转换不正确

我正在尝试将pandas数据帧的列中的值设置为另一个pandas数据帧中的列的值。我遇到了类型（

pandas.tslib.Timestamp

）转换不正确的问题

我有一个数据框

指示器
：

                           0
                   Timestamp
0  2016-02-12 13:45:00-05:00
1  2016-02-16 13:45:00-05:00
2  2016-02-17 13:45:00-05:00
3  2016-02-18 13:45:00-05:00
4  2016-02-19 13:45:00-05:00
5  2016-02-22 13:45:00-05:00
6  2016-02-24 13:45:00-05:00
7  2016-02-25 13:45:00-05:00
8  2016-02-26 13:45:00-05:00
9  2016-02-29 13:45:00-05:00
10 2016-03-01 13:45:00-05:00
11 2016-03-02 13:45:00-05:00
12 2016-03-03 13:45:00-05:00

另一个数据帧
发出信号
：

        Signal Timestamp
0    0     NaN       NaN
     1     NaN       NaN
     2     NaN       NaN
     3     NaN       NaN
     4     NaN       NaN
     5     NaN       NaN
     6     NaN       NaN
     7     NaN       NaN
     8     NaN       NaN
     9     NaN       NaN
     10    NaN       NaN
     11    NaN       NaN
     12    NaN       NaN

signals.info（）
：

产生

        Signal            Timestamp
0    0     NaN  1455302700000000000
     1     NaN  1455648300000000000
     2     NaN  1455734700000000000
     3     NaN  1455821100000000000
     4     NaN  1455907500000000000
     5     NaN  1456166700000000000
     6     NaN  1456339500000000000
     7     NaN  1456425900000000000
     8     NaN  1456512300000000000
     9     NaN  1456771500000000000
     10    NaN  1456857900000000000
     11    NaN  1456944300000000000
     12    NaN  1457030700000000000

我怎样才能正确地转换它呢？
我最终还是这样做了

signals['Timestamp'][0] = indicators[0]['Timestamp'].set_index('Timestamp').index

你的python、numpy和pandas版本是什么？python是
3.4.4
，pandas是
0.17.1
，numpy是
1.11.0b2
好的，
signals.info（）
显示了什么？在过去使用JSON和pandas时，我发现它们可以变化1000个数量级。JSON->PANDAS（1456174020000/1000）/86400+25569+（-5/24）给出了一个13位数字，表示一个带有时间戳的日期的10。您可以将其反转？如果您（1）将
信号['TimeStamp']
转换为手前的日期时间，或者（2）删除手前的
信号['TimeStamp']
列，该怎么办？我想问题在于，您正在将日期时间转换为字符串，而pandas正在执行类似于将日期时间转换为整数然后转换为字符串的操作。
        Signal            Timestamp
0    0     NaN  1455302700000000000
     1     NaN  1455648300000000000
     2     NaN  1455734700000000000
     3     NaN  1455821100000000000
     4     NaN  1455907500000000000
     5     NaN  1456166700000000000
     6     NaN  1456339500000000000
     7     NaN  1456425900000000000
     8     NaN  1456512300000000000
     9     NaN  1456771500000000000
     10    NaN  1456857900000000000
     11    NaN  1456944300000000000
     12    NaN  1457030700000000000

signals['Timestamp'][0] = indicators[0]['Timestamp'].set_index('Timestamp').index