在python中，是否有一个函数来增加日期和时间，同时声明时间范围？

Question

我们正在用python进行一个项目，在这个项目中，数据存储在MySQL数据库中。在数据库中，我们有两个表，其中表1（原始数据）从2个不同来源（LPN1，RisingHF）接收数据，表2（气象站）从一个来源存储数据。你知道吗

我们要做的是减去值，例如：

For 2019-07-26 15:00:00 to 2019-07-26 15:20:00
subtract latest data received from weather station with LPN1
subtract latest data received from weather station with RisingHF

For 2019-07-26 15:20:00 to 2019-07-26 15:40:00
subtract latest data received from weather station with LPN1
subtract latest data received from weather station with RisingHF

For 2019-07-26 15:40:00 to 2019-07-26 16:00:00
subtract latest data received from weather station with LPN1
subtract latest data received from weather station with RisingHF

等等。。。。。你知道吗

我们想也许我们可以增加时间和日期，例如：

import pandas as pd
from pandas.io import sql
from sqlalchemy import create_engine

list_lpn = []
list_rising = []

engine = create_engine("mysql://xxx:xxx@localhost/xxx")

conn = engine.connect()
    for x in range (24):
        for y in range (0, 20, 40):
            a = pd.read.sql("SELECT MAX(timestamp) FROM Weather_Station WHERE timestamp > 2019-07-26 x:y:00 AND timestamp < 2019-07-26 x:y+20:00, conn) 
            b = pd.read.sql("SELECT MAX(timestamp) FROM Raw_Data WHERE topic = 'lpn1' AND timestamp > 2019-07-26 x:y:00 AND timestamp < 2019-07-26 x:y+20:00, conn) 
            c = pd.read.sql("SELECT MAX(timestamp) FROM Raw_Data WHERE topic = 'RisingHF' AND timestamp > 2019-07-26 x:y:00 AND timestamp < 2019-07-26 x:y+20:00, conn) 

            list_lpn = a - b
            list_rising = a - c

这段代码还没有实现，但这是我们刚刚考虑过的，也许这就是如何做到的，但也许有更有效的方法来做到这一点？你知道吗

表1

  id | timestamp           | topic       | temperature | humidity | wind_speed | battery | secured | encrypted | correct |
+----+---------------------+-------------+-------------+----------+------------+---------+---------+-----------+---------+
| 16 | 2019-07-26 15:09:25 | lpn1        | 30.40       | 41.81    | NULL       | 3.13    |    NULL |      NULL |    NULL |
| 17 | 2019-07-26 15:11:30 | rhf1s001id1 | 29.61       | 43.32    | NULL       | 3.08    |    NULL |      NULL |    NULL |
| 18 | 2019-07-26 15:24:26 | lpn1        | 29.71       | 40.75    | NULL       | 3.13    |    NULL |      NULL |    NULL |
| 19 | 2019-07-26 15:25:25 | rhf1s001id1 | 29.60       | 42.34    | NULL       | 3.14    |    NULL |      NULL |    NULL |
| 20 | 2019-07-26 15:39:19 | rhf1s001id1 | 29.18       | 42.83    | NULL       | 3.15    |    NULL |      NULL |    NULL |
| 21 | 2019-07-26 15:39:26 | lpn1        | 29.46       | 40.89    | NULL       | 3.13    |    NULL |      NULL |    NULL |
| 22 | 2019-07-26 15:54:27 | lpn1        | 29.63       | 41.60    | NULL       | 3.14    |    NULL |      NULL |    NULL |
| 23 | 2019-07-26 15:55:38 | rhf1s001id1 | 28.73       | 44.29    | NULL       | 3.17    |    NULL |      NULL |    NULL |

表2

  id   | timestamp           | temperature1m | temperature1h | humidity1m | humidity1h |
+------+---------------------+---------------+---------------+------------+------------+
|    1 | 2019-07-26 15:00:13 |          26.9 |          28.3 |         44 |         48 |
|    2 | 2019-07-26 15:10:13 |            28 |          28.3 |         41 |         48 |
|    3 | 2019-07-26 15:20:13 |          27.8 |          28.3 |         39 |         48 |
|    4 | 2019-07-26 15:30:13 |            27 |          28.3 |         40 |         48 |
|    5 | 2019-07-26 15:40:13 |          26.8 |          28.3 |         42 |         48 |
|    6 | 2019-07-26 15:50:13 |            27 |          28.3 |         42 |         48 |
+------+---------------------+---------------+---------------+------------+------------+