我有一个dtype datetime64 的数据帧
df:
time timestamp
18053.401736 2019-06-06 09:38:30+00:00
18053.418252 2019-06-06 10:02:17+00:00
18053.424514 2019-06-06 10:11:18+00:00
18053.454132 2019-06-06 10:53:57+00:00
Name: timestamp, dtype: datetime64[ns, UTC]
和一系列dtype timedelta64
ss:
ref_time
0 days 09:00:00
1 0 days 09:00:01
2 0 days 09:00:02
3 0 days 09:00:03
4 0 days 09:00:04
...
21596 0 days 14:59:56
21597 0 days 14:59:57
21598 0 days 14:59:58
21599 0 days 14:59:59
21600 0 days 15:00:00
Name: timeonly, Length: 21601, dtype: timedelta64[ns]
我想将两者合并，以便输出df只有在时间戳和系列一致的地方才有值：
Desired output:
time timestamp ref_time
Nan Nan 09:00:00
... ... ...
Nan Nan 09:38:29
18053.401736 2019-06-06 09:38:30+00:00 09:38:30
Nan Nan 09:38:31
... ... ...
18053.418252 2019-06-06 10:02:17+00:00 10:02:17
Nan Nan 10:02:18
Nan Nan 10:02:19
... ... ...
18053.424514 2019-06-06 10:11:18+00:00 10:11:18
... ... ...
18053.454132 2019-06-06 10:53:57+00:00 10:53:57
然而，如果我将"timestamp"转换为一个时间，我只会得到一个对象dtype，并且无法将其与ss合并。
dframe['timestamp'].dtype # --> datetime64[ns, UTC]
df['timeonly'] = df['timestamp'].dt.time
df['timeonly'].dtype # --> object
df_date.merge(timeax, how='outer', on=['timeonly'])
# ValueError: You are trying to merge on object and timedelta64[ns] columns. If you wish to proceed you should use pd.concat
但是按照建议使用concat并不能给我所需的输出。如何合并/连接DataFrame和Series？Pandas 1.1.5版
通过减去日期部分将时间戳转换为时间增量，然后合并：
df1 = pd.DataFrame([pd.Timestamp('2019-06-06 09:38:30+00:00'),pd.Timestamp('2019-06-06 10:02:17+00:00')], columns=['timestamp'])
df2 = pd.DataFrame([pd.Timedelta('09:38:30')], columns=['ref_time'])
timestamp
0 2019-06-06 09:38:30+00:00
1 2019-06-06 10:02:17+00:00
timestamp datetime64[ns, UTC]
dtype: object
ref_time
0 09:38:30
ref_time timedelta64[ns]
dtype: object
df1['merge_key'] = df1['timestamp'].dt.tz_localize(None) - pd.to_datetime(df1['timestamp'].dt.date)
df_merged = df1.merge(df2, left_on = 'merge_key', right_on = 'ref_time')
提供：
timestamp merge_key ref_time
0 2019-06-06 09:38:30+00:00 09:38:30 09:38:30
这里的主要挑战是将所有内容转换为兼容的日期类型。使用您稍微修改过的示例作为输入
from io import StringIO
df = pd.read_csv(StringIO(
"""
time,timestamp
18053.401736,2019-06-06 09:38:30+00:00
18053.418252,2019-06-06 10:02:17+00:00
18053.424514,2019-06-06 10:11:18+00:00
18053.454132,2019-06-06 10:53:57+00:00
"""))
df['timestamp'] = pd.to_datetime(df['timestamp'])
from datetime import timedelta
sdf = pd.read_csv(StringIO(
"""
ref_time
0 days 09:00:00
0 days 09:00:01
0 days 09:00:02
0 days 09:00:03
0 days 09:00:04
0 days 09:38:30
0 days 10:02:17
0 days 14:59:56
0 days 14:59:57
0 days 14:59:58
0 days 14:59:59
0 days 15:00:00
"""))
sdf['ref_time'] = pd.to_timedelta(sdf['ref_time'])
这里的数据类型与您的问题一样，哪个是重要的
首先，我们计算出base_date，因为我们需要将时间增量转换为日期时间等。注意，我们通过round('1d')将其设置为相关日期的午夜
base_date = df['timestamp'].iloc[0].round('1d').to_pydatetime()
base_date
输出
datetime.datetime(2019, 6, 6, 0, 0, tzinfo=<UTC>)
接下来，我们将sdf的时间增量添加到base_date:
sdf['ref_dt'] = sdf['ref_time'] + base_date
现在sdf['ref_dt']和df['timestamp']在同一个"单元"中，属于同一类型，所以我们可以合并
sdf.merge(df, left_on = 'ref_dt', right_on = 'timestamp', how = 'left')
输出
ref_time ref_dt time timestamp
-- --------------- ------------------------- ------- -------------------------
0 0 days 09:00:00 2019-06-06 09:00:00+00:00 nan NaT
1 0 days 09:00:01 2019-06-06 09:00:01+00:00 nan NaT
2 0 days 09:00:02 2019-06-06 09:00:02+00:00 nan NaT
3 0 days 09:00:03 2019-06-06 09:00:03+00:00 nan NaT
4 0 days 09:00:04 2019-06-06 09:00:04+00:00 nan NaT
5 0 days 09:38:30 2019-06-06 09:38:30+00:00 18053.4 2019-06-06 09:38:30+00:00
6 0 days 10:02:17 2019-06-06 10:02:17+00:00 18053.4 2019-06-06 10:02:17+00:00
7 0 days 14:59:56 2019-06-06 14:59:56+00:00 nan NaT
8 0 days 14:59:57 2019-06-06 14:59:57+00:00 nan NaT
9 0 days 14:59:58 2019-06-06 14:59:58+00:00 nan NaT
10 0 days 14:59:59 2019-06-06 14:59:59+00:00 nan NaT
11 0 days 15:00:00 2019-06-06 15:00:00+00:00 nan NaT
我们看到合并发生在需要的地方