我有以下熊猫数据框
eui BatV TempC_DS TempC_SHT \
0 58A0CB0000101DB6 NaN NaN NaN
1 58A0CB0000101DB6 NaN NaN NaN
2 58A0CB0000101DB6 NaN NaN NaN
3 58A0CB0000101DB6 NaN NaN NaN
4 58A0CB0000101DB6 NaN NaN NaN
.. ... ... ... ...
245 58A0CB0000101DB6 NaN NaN NaN
246 58A0CB0000101DB6 NaN NaN NaN
247 58A0CB0000101DB6 NaN NaN NaN
248 58A0CB0000101DB6 NaN NaN NaN
249 58A0CB0000101DB6 NaN NaN NaN
EventEnqueuedUtcTime id \
0 2021-02-24T10:34:13.8060000Z beeae3f6-8e1c-4eab-a4e3-72a7ccef383d
1 2021-02-24T10:34:34.1070000Z f1e5d54a-0eba-4ae7-8ab9-cb3ba4c74b24
2 2021-02-24T10:39:22.0980000Z fc3dc5b5-3529-4c5e-a1db-d13a1d849fcf
3 2021-02-24T10:44:21.7910000Z 5bb9fa04-20da-4862-9eaf-203f3bb6b1e5
4 2021-02-24T10:49:22.8080000Z 20e59b34-357a-48cf-bcc5-0e857bb52f54
.. ... ...
245 2021-02-25T07:50:08.5040000Z 8eca61b9-a1b3-4cf1-adf5-5bc90208c37e
246 2021-02-25T07:55:08.0550000Z b43e0f32-b5ad-4c8f-ac02-0fea62c4f959
247 2021-02-25T08:00:08.7940000Z 85516c14-bf8d-4d62-9ddf-6289e5eb3071
248 2021-02-25T08:05:08.2260000Z 0d13773c-81fd-4038-bbe9-6def2262b4e3
249 2021-02-25T08:10:09.2350000Z 16b29ea2-5bf5-489f-bfc5-34f301a4587d
_rid \
0 AqMcAKHcB0mACgAAAAAAAA==
1 AqMcAKHcB0mBCgAAAAAAAA==
2 AqMcAKHcB0mCCgAAAAAAAA==
3 AqMcAKHcB0mECgAAAAAAAA==
4 AqMcAKHcB0mGCgAAAAAAAA==
.. ...
245 AqMcAKHcB0nzCwAAAAAAAA==
246 AqMcAKHcB0n0CwAAAAAAAA==
247 AqMcAKHcB0n2CwAAAAAAAA==
248 AqMcAKHcB0n3CwAAAAAAAA==
249 AqMcAKHcB0n5CwAAAAAAAA==
_self \
0 dbs/AqMcAA==/colls/AqMcAKHcB0k=/docs/AqMcAKHcB...
1 dbs/AqMcAA==/colls/AqMcAKHcB0k=/docs/AqMcAKHcB...
2 dbs/AqMcAA==/colls/AqMcAKHcB0k=/docs/AqMcAKHcB...
3 dbs/AqMcAA==/colls/AqMcAKHcB0k=/docs/AqMcAKHcB...
4 dbs/AqMcAA==/colls/AqMcAKHcB0k=/docs/AqMcAKHcB...
.. ...
245 dbs/AqMcAA==/colls/AqMcAKHcB0k=/docs/AqMcAKHcB...
246 dbs/AqMcAA==/colls/AqMcAKHcB0k=/docs/AqMcAKHcB...
247 dbs/AqMcAA==/colls/AqMcAKHcB0k=/docs/AqMcAKHcB...
248 dbs/AqMcAA==/colls/AqMcAKHcB0k=/docs/AqMcAKHcB...
249 dbs/AqMcAA==/colls/AqMcAKHcB0k=/docs/AqMcAKHcB...
_etag _attachments _ts \
0 "ef029dec-0000-0d00-0000-60362ba60000" attachments/ 1614162854
1 "ef02befa-0000-0d00-0000-60362bba0000" attachments/ 1614162874
2 "f002c9c1-0000-0d00-0000-60362cda0000" attachments/ 1614163162
3 "f102f18c-0000-0d00-0000-60362e070000" attachments/ 1614163463
4 "f2021852-0000-0d00-0000-60362f330000" attachments/ 1614163763
.. ... ... ...
245 "4103b1ac-0000-0d00-0000-603757ad0000" attachments/ 1614239661
246 "42039845-0000-0d00-0000-6037590a0000" attachments/ 1614240010
247 "4203ded9-0000-0d00-0000-60375a3a0000" attachments/ 1614240314
248 "43034b51-0000-0d00-0000-60375b640000" attachments/ 1614240612
249 "4303b5b0-0000-0d00-0000-60375c620000" attachments/ 1614240866
DecibelValue
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
.. ...
245 59.0
246 51.0
247 68.0
248 48.0
249 55.0
[250 rows x 12 columns]
我想使用具有日期 + 时间的 EventEnqueuedUtcTime。
我只想在数据框中保留所有大于 25/02/2021 上午 8 点的行。
但我不知道如何像这样过滤
答案 0 :(得分:2)
通过 to_datetime
将列转换为日期时间,然后使用 boolean indexing
:
df['EventEnqueuedUtcTime'] = pd.to_datetime(df['EventEnqueuedUtcTime'])
df = df[df['EventEnqueuedUtcTime'] > '2021-02-25 08:20:00']