summing time column in mysql

summing time column in mysql

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summing time column in mysql
Tag : mysql , By : Gazza
Date : November 25 2020, 07:06 PM

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Summing a comma separated column in MySQL 4 (not 5)

Tag : mysql , By : Edwin
Date : March 29 2020, 07:55 AM
wish help you to fix your issue To do this kind of non trivial string manipulations, you need to use stored procedures, which, for MySQL, only appeared 6 years ago, in version 5.0.
MySQL 4 is now very old, the latest version from branch 4.1 was 4.1.25, in 2008. It is not supported anymore. Most Linux distributions don't provide it anymore. It's really time to upgrade.
  WHILE INSTR(s, ",") > 0 DO
    SET res = res + SUBSTRING_INDEX(s, ",", 1);
    SET s = MID(s, INSTR(s, ",") + 1);
  RETURN res + s;
END //
mysql> SELECT SUM_OF_LIST("5,2.1") AS Result;
| Result |
|    7.1 |

MYSQL query for summing occurence of some column

Tag : mysql , By : scotta01
Date : March 29 2020, 07:55 AM
help you fix your problem Let's say I have such MYSQL table for logins:
SELECT COUNT(*) AS 'login_times', name 
FROM logins 
GROUP BY name 
ORDER BY login_times DESC

summing up column values in mysql

Tag : mysql , By : phatfish
Date : March 29 2020, 07:55 AM
wish help you to fix your issue I have a table "fruits" with the following data
SELECT count(*) as total,
       sum(a = 'apple') as applecount,
       sum(a = 'orange') as orangecount,
       sum(a not in ('orange', 'apple')) as others
FROM fruits

Calculating time difference and summing based on another column

Tag : sql , By : Michael T.
Date : March 29 2020, 07:55 AM
I wish did fix the issue. I used the following query which resolved my issue,

summing a time-series column upon it self with a condition

Tag : python , By : paolodm
Date : March 29 2020, 07:55 AM
I wish this help you Here's an approach that works for a simple example of one machine that varies between on and off during the course of one day. It works regardless of whether the machine is in on or off state in the first row.
df = pd.DataFrame({'ID': [12, 12, 12, 12, 12],
                   'on/off': [0,0,1,0,1],
                   'Time': ['2019-01-01 00:00:00', '2019-01-01 00:00:05', '2019-01-01 00:00:10','2019-01-01 00:00:15','2019-01-01 00:00:20']

    ID  on/off  Time
0   12  0       2019-01-01 00:00:00
1   12  0       2019-01-01 00:00:05
2   12  1       2019-01-01 00:00:10
3   12  0       2019-01-01 00:00:15
4   12  1       2019-01-01 00:00:20
s = df[df['on/off'].shift(1) != df['on/off']].index
df = df.loc[s]
df['time shift'] = df['Time'].shift(1)
    ID  on/off  Time                 time shift
0   12  0       2019-01-01 00:00:00  NaT
2   12  1       2019-01-01 00:00:10  2019-01-01 00:00:00
3   12  0       2019-01-01 00:00:15  2019-01-01 00:00:10
4   12  1       2019-01-01 00:00:20  2019-01-01 00:00:15
r = df[df['on/off'] == 1].index
df = df.loc[r]
    ID  on/off  Time                 time shift
2   12  1       2019-01-01 00:00:10  2019-01-01 00:00:00
4   12  1       2019-01-01 00:00:20  2019-01-01 00:00:15
(df['Time'] - df['time shift']).sum()
Timedelta('0 days 00:00:15')
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