My target on daily basis is 250. For any given date, if the cum-daily_result has reached 250 then subsequent rows should have only 250 as expected results

In below table column 'ID' to 'cum_daily_result' are the input in data frame. The expected output is computed manually in column 'expected_daily_result'

I tried the below code, but not giving the expected result,

if df['cum_daily_result'][-1] >= 250:
    expected_daily_result = df['cum_daily_result'][-1]
else:
    expected_daily_result  = df['cum_daily_result']

CodePudding user response：

Consider using .clip(...)

    expected_daily_result  = df.cum_daily_result.clip(upper=250)

CodePudding user response：

make simple example text not image

example:

data = [['a', 250, 250], ['a', 250, 500], ['a', -1290, -790],
        ['b', -1392, -1392], ['b', 250, -1142], ['b', 250, -892], ['b', 2238, 1346],
        ['b', 250, 1596], ['c', 2477, 2477], ['c', -3813, -1336], ['c', 250, -1086]]
df = pd.DataFrame(data, columns=['col1', 'col2', 'col3'])

output(df):

    col1    col2    col3
0   a       250     250
1   a       250     500
2   a       -1290   -790
3   b       -1392   -1392
4   b       250     -1142
5   b       250     -892
6   b       2238    1346
7   b       250     1596
8   c       2477    2477
9   c       -3813   -1336
10  c       250     -1086

use groupby

idx = df[df['col3'] >= 250].groupby('col1').head(1).index
df.loc[idx, 'col4'] = 1
df['col4'] = df.groupby('col1')['col4'].bfill() * df['col3']
df['col4'] = df.groupby('col1')['col4'].ffill().astype('int')

output(df)

    col1    col2    col3    col4
0   a       250     250     250
1   a       250     500     250
2   a       -1290   -790    250
3   b       -1392   -1392   -1392
4   b       250     -1142   -1142
5   b       250     -892    -892
6   b       2238    1346    1346
7   b       250     1596    1346
8   c       2477    2477    2477
9   c       -3813   -1336   2477
10  c       250     -1086   2477