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我有一个熊猫数据框（dfnew），其中一列（时间戳）是datetime64[ns]类型。现在我想看看在特定时间范围内有多少观察，比如说 10:00:00 到 12:00:00。

    dfnew['timestamp'] = dfnew['timestamp'].astype('datetime64[ns]')
    dfnew['timestamp]
0    2013-12-19 09:03:21.223000
1    2013-12-19 11:34:23.037000
2    2013-12-19 11:34:23.050000
3    2013-12-19 11:34:23.067000
4    2013-12-19 11:34:23.067000
5    2013-12-19 11:34:23.067000
6    2013-12-19 11:34:23.067000
7    2013-12-19 11:34:23.067000
8    2013-12-19 11:34:23.067000
9    2013-12-19 11:34:23.080000
10   2013-12-19 11:34:23.080000
11   2013-12-19 11:34:23.080000
12   2013-12-19 11:34:23.080000
13   2013-12-19 11:34:23.080000
14   2013-12-19 11:34:23.080000
15   2013-12-19 11:34:23.097000
16   2013-12-19 11:34:23.097000
17   2013-12-19 11:34:23.097000
18   2013-12-19 11:34:23.097000
19   2013-12-19 11:34:23.097000
Name: timestamp

    dfnew['Time']=dfnew['timestamp'].map(Timestamp.time)
    t1 = datetime.time(10, 0, 0)
    t2 = datetime.time(12, 0, 0)
    print len(dfnew[t1<dfnew["Time"]<t2])

这会产生错误TypeError: can't compare datetime.time to Series。 我是熊猫数据框的新手。我想我在这里犯了一个非常愚蠢的错误。感谢任何帮助。

4

1 回答 1

2

您可以使用 DatetimeIndexindexer_between_time方法，因此使用它的一个技巧是将 Series / 列传递给 DatetimeIndex 构造函数：

from datetime import time

# s is your datetime64 column

In [11]: pd.DatetimeIndex(s).indexer_between_time(time(10), time(12))
Out[11]: 
array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])

这将获得 10 到 12（包括 *）之间的时间位置，因此使用 iloc 进行过滤：

In [12]: s.iloc[pd.DatetimeIndex(s).indexer_between_time(time(10), time(12))]
Out[12]: 
1    2013-12-19 11:34:23.037000
2    2013-12-19 11:34:23.050000
3    2013-12-19 11:34:23.067000
4    2013-12-19 11:34:23.067000
5    2013-12-19 11:34:23.067000
6    2013-12-19 11:34:23.067000
7    2013-12-19 11:34:23.067000
8    2013-12-19 11:34:23.067000
9    2013-12-19 11:34:23.080000
10   2013-12-19 11:34:23.080000
11   2013-12-19 11:34:23.080000
12   2013-12-19 11:34:23.080000
13   2013-12-19 11:34:23.080000
14   2013-12-19 11:34:23.080000
15   2013-12-19 11:34:23.097000
16   2013-12-19 11:34:23.097000
17   2013-12-19 11:34:23.097000
18   2013-12-19 11:34:23.097000
19   2013-12-19 11:34:23.097000
Name: timestamp, dtype: datetime64[ns]

*include_start和include_end是可选的布尔参数indexer_between_time。

于 2014-01-21T07:12:01.830 回答