Question

在以下数据中，日期和时间位于不同的列中，我将它们组合在一起以获得完整的日期时间，因此结果列的类型为“datetime64 [ns]”。但是，有时候会有空白日期和时间的记录，在这种情况下，结果列的类型为“对象”，本质上是一个字符串对象。当所有记录都存在且不存在时，我该如何处理？

示例数据

 <plugin>
    <groupId>org.apache.maven.plugins</groupId>
    <artifactId>maven-failsafe-plugin</artifactId>
    <version>2.19.1</version>
    <configuration>
      <systemPropertyVariables>
        <browser>firefox, chrome</browser>
      </systemPropertyVariables>
      <parallel>tests</parallel>
    </configuration>
  </plugin>

以下是我现在的代码：

CARD,IN Date,IN Time,OUT Date,OUT Time
100001,30-04-2015,14:19:18,01-05-2015,00:10:56
100002,30-04-2015,11:27:52,,
100003,30-04-2015,17:59:47,01-05-2015,04:51:52
100004,30-04-2015,16:15:25,,
100005,30-04-2015,10:25:13,01-05-2015,01:25:13
100006,30-04-2015,16:59:10,,
100007,30-04-2015,13:22:06,,
100008,30-04-2015,09:15:29,,
100009,30-04-2015,17:01:10,01-05-2015,01:51:01
100010,30-04-2015,13:13:30,01-05-2015,01:37:28
100011,30-04-2015,09:37:28,01-05-2015,00:37:28
100012,30-04-2015,18:55:44,01-05-2015,03:22:22
100013,30-04-2015,14:28:16,01-05-2015,01:27:18
100014,30-04-2015,09:02:13,01-05-2015,00:02:13
100015,30-04-2015,09:04:10,01-05-2015,00:04:10
100016,30-04-2015,18:51:56,01-05-2015,09:51:56
100017,30-04-2015,09:12:51,01-05-2015,00:12:51
100018,30-04-2015,10:40:31,01-05-2015,01:40:31
100019,30-04-2015,10:35:56,01-05-2015,01:35:56
100020,30-04-2015,17:50:03,01-05-2015,03:54:54
100021,30-04-2015,17:00:16,01-05-2015,02:45:35
100022,30-04-2015,11:18:41,01-05-2015,01:15:52

Answer 1

我认为你可以试试str.contains：

data.ix[(data.OUT.str.contains('nan')), 'OUT'] = np.nan

但最好的是使用to_datetime参数errors='coerce'：

data['OUT'] = pd.to_datetime(data['OUT'], errors='coerce')
print data
      CARD    IN Date                  IN   OUT Date                 OUT
0   100001 2015-04-30 2015-04-30 14:19:18 2015-01-05 2015-01-05 00:10:56
1   100002 2015-04-30 2015-04-30 11:27:52        NaT                 NaT
2   100003 2015-04-30 2015-04-30 17:59:47 2015-01-05 2015-01-05 04:51:52
3   100004 2015-04-30 2015-04-30 16:15:25        NaT                 NaT
4   100005 2015-04-30 2015-04-30 10:25:13 2015-01-05 2015-01-05 01:25:13
5   100006 2015-04-30 2015-04-30 16:59:10        NaT                 NaT
6   100007 2015-04-30 2015-04-30 13:22:06        NaT                 NaT
7   100008 2015-04-30 2015-04-30 09:15:29        NaT                 NaT
8   100009 2015-04-30 2015-04-30 17:01:10 2015-01-05 2015-01-05 01:51:01
9   100010 2015-04-30 2015-04-30 13:13:30 2015-01-05 2015-01-05 01:37:28
10  100011 2015-04-30 2015-04-30 09:37:28 2015-01-05 2015-01-05 00:37:28
11  100012 2015-04-30 2015-04-30 18:55:44 2015-01-05 2015-01-05 03:22:22
12  100013 2015-04-30 2015-04-30 14:28:16 2015-01-05 2015-01-05 01:27:18
13  100014 2015-04-30 2015-04-30 09:02:13 2015-01-05 2015-01-05 00:02:13
14  100015 2015-04-30 2015-04-30 09:04:10 2015-01-05 2015-01-05 00:04:10
15  100016 2015-04-30 2015-04-30 18:51:56 2015-01-05 2015-01-05 09:51:56
16  100017 2015-04-30 2015-04-30 09:12:51 2015-01-05 2015-01-05 00:12:51
17  100018 2015-04-30 2015-04-30 10:40:31 2015-01-05 2015-01-05 01:40:31
18  100019 2015-04-30 2015-04-30 10:35:56 2015-01-05 2015-01-05 01:35:56
19  100020 2015-04-30 2015-04-30 17:50:03 2015-01-05 2015-01-05 03:54:54
20  100021 2015-04-30 2015-04-30 17:00:16 2015-01-05 2015-01-05 02:45:35
21  100022 2015-04-30 2015-04-30 11:18:41 2015-01-05 2015-01-05 01:15:52

在python pandas中清理日期和时间记录

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