Overwrite a value in a pandas dataframe column based on a calculation function applied to it

Question

From the following DataFrame:

worktime = 1440
person = [11,22,33,44,55]
begin_date = '2019-10-01'
shift= [1,2,3,1,2]
pause = [90,0,85,70,0]
occu = [60,0,40,20,0]
time_u = [50,40,80,20,0]
time_a = [84.5,0.0,10.5,47.7,0.0]
time_p = 0
time_q = [35.9,69.1,0.0,0.0,84.4]

df = pd.DataFrame({'date':pd.date_range(begin_date, periods=len(person)),'person':person,'shift':shift,'worktime':worktime,'pause':pause,'occu':occu, 'time_u':time_u,'time_a':time_a,'time_p ':time_p,'time_q':time_q,})

Output:

    date      person  shift worktime pause  occu    time_u  time_a  time_p  time_q 
0   2019-10-01  11      1   1440       90   60        50     84.5      0     35.9
1   2019-10-02  22      2   1440        0    0        40      0.0      0     69.1
2   2019-10-03  33      3   1440       85   40        80     10.5      0      0.0
3   2019-10-04  44      1   1440       70   20        20     47.7      0      0.0
4   2019-10-05  55      2   1440        0    0         0      0.0      0     84.4

I am looking for a suitable function that takes the already contained value of the columns and uses it in a calculation and then overwrites it with the result of the calculation.

It concerns the columns time_u, time_a, time_p and time_q and should be applied according to the following principle:

time_u = worktime - pause - occu - (existing value of time_u)
time_a = (new value of time_u) - time_a
time_p = (new value of time_a) - time_p
time_q = (new value of time_p)- time_q

Is there a possible function that could be used here?

Using this formula manually, the output would look like this:

    date      person  shift worktime pause  occu    time_u  time_a  time_p  time_q 
0   2019-10-01  11      1   1440       90   60       1240    1155.5  1155.5  1119.6
1   2019-10-02  22      2   1440        0    0       1400    1400    1400    1330.9
2   2019-10-03  33      3   1440       85   40       1235    1224.5  1224.5  1224.5
3   2019-10-04  44      1   1440       70   20       1330    1282.3  1282.3  1282.3
4   2019-10-05  55      2   1440        0    0       1440    1440    1440    1355.6

Unfortunately, this task is way beyond my skill level, so any help in setting up the appropriate function would be greatly appreciated.

Many thanks in advance

Try something like this: stackoverflow.com/questions/45393123/… — FAB
– FAB, Commented Nov 22, 2022 at 23:53

user19077881 · Accepted Answer · 2022-11-23 00:38:17Z

0

You can simply apply the relationships you have supplied sequentially. Or are you looking for something else? By the way, you put an extra space at the end of 'time_p'

df['time_u'] = df['worktime'] - df['pause'] - df['occu'] - df['time_u']
df['time_a'] = df['time_u'] - df['time_a']
df['time_p'] = df['time_a'] - df['time_p']
df['time_q'] = df['time_p'] - df['time_q']

edited Nov 23, 2022 at 0:38

answered Nov 23, 2022 at 0:21

user19077881

5,5792 gold badges8 silver badges22 bronze badges

Sign up to request clarification or add additional context in comments.

Collectives™ on Stack Overflow

Overwrite a value in a pandas dataframe column based on a calculation function applied to it

1 Answer 1

Comments

Your Answer

Linked

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

Comments

Your Answer

Sign up or log in

Post as a guest

Linked

Related