Replace all values for every subgroups with subgroup min value with panda apply() function (without-CodePudding

I have a panda dataframe df:

DIFF_HOURS  DIFF_TEMP
0   0.0     0.050886
1   1.0     0.660698
2   2.0     1.656014
3   3.0     2.543857
4   4.0     3.071813
...     ...     ...
627647  68.0    -1.708911
627648  69.0    -1.225022
627649  70.0    -2.040668
627650  71.0    -2.738665

For data visualization, I do various boxplot with x=DIFF_HOURS and y=DIFF_TEMP.

I want to have subgroups of 6 hours:

Group 1: 0, 1, 2, 3,4,6
Group 2: 7,8,9,10,11,12
...
Group n: 66,67,68,69,70,71,72

And replace all values of each subgroup by min subgroup value:

Group 1: 0,0,0,0,0,0
Group 2: 7,7,7,7,7,7
...
Group n: 66,66,66,66,66,66

I don't want to use a loop. Is it possible to use panda apply() function please?

CodePudding user response：

Try this:

df.groupby(df['DIFF_HOURS'].mod(6).eq(0).cumsum())['DIFF_HOURS'].apply(lambda x: [x.min()]*6)

CodePudding user response：

My solution whick works fine and fast:

every_hours = 6
max_periode = 72

for i in range(0, max_periode, every_hours):
      df.loc[(df['DIFF_HOURS'] > i) & (df['DIFF_HOURS'] <= (i 6)), 'DIFF_HOURS'] = i