grouping pandas rows above and below based on specified value - pandas

Question

Is there a simple way to group pandas dataframe according to a given value. In the code below,the value is 1.5, I want to find the last row with value equal to or greater 1.5 and add a new column 'Group' such that all the rows above that last row are in one group and rows below it are in another group.
import pandas as pd
import numpy as np
fnd_val = 1.5
A = [1,2,3,4,5,6,7,8,9]
B = [-1.1306,-0.5694,-0.7241,1.8211,1.5555,0.0416,1.9236,0.1944,-0.0204]
df = pd.DataFrame({'Class':A, 'Value':B})
df_srtd = df.sort_values(by = "Class", ascending)
>>>df_srtd
Class Value
0 1 -1.1306
1 2 -0.5694
2 3 -0.7241
3 4 1.8211
4 5 1.5555
5 6 0.0416
6 7 1.9236
7 8 0.19440
8 9 -0.0204
#Desired output
Class Value Group
0 1 -1.1306 2
1 2 -0.5694 2
2 3 -0.7241 2
3 4 1.8211 2
4 5 1.5555 2
5 6 0.0416 2
6 7 1.9236 2
7 8 0.19440 1
8 9 -0.0204 1
#or if sorting is reversed like below
df_srtd = df.sort_values(by = "Class", ascending=False)
>>>df_srtd
Class Value
8 9 -0.0204
7 8 0.19440
6 7 1.9236
5 6 0.0416
4 5 1.5555
3 4 1.8211
2 3 -0.7241
1 2 -0.5694
0 1 -1.1306
#Desired output
Class Value Group
8 9 -0.0204 2
7 8 0.19440 2
6 7 1.9236 2
5 6 0.0416 2
4 5 1.5555 2
3 4 1.8211 2
2 3 -0.7241 1
1 2 -0.5694 1
0 1 -1.1306 1
My approach (using reversely sorted data):
import pandas as pd
import numpy as np
A = [1,2,3,4,5,6,7,8,9]
B = [-1.1306,-0.5694,-0.7241,1.8211,1.5555,0.0416,1.9236,0.1944,-0.0204]
df = pd.DataFrame({'Class':A, 'Value':B})
df_srtd = df.sort_values(by = "Class", ascending=False)
df_srtd['val_nxt'] = df_srtd['Value'].shift(-1)
fnd_val = 1.5
conditions = [
(df_srtd['Value'] >= fnd_val),
(df_srtd['Value'] < fnd_val)
& (df_srtd['Value'] < df_srtd['val_nxt']),
(df_srtd['Value'] < fnd_val)
]
choices = [ '2', '2', '1']
df_srtd['Group'] = np.select(conditions, choices, default='-99')
print(df_srtd)
Result obtained:
Class Value val_nxt Group
8 9 -0.0204 0.19440 2
7 8 0.19440 1.9236 2
6 7 1.9236 0.0416 2
5 6 0.0416 1.5555 2
4 5 1.5555 1.8211 2
3 4 1.8211 -0.7241 2 #All after this should be grouped 1
2 3 -0.7241 -0.5694 2 #This one should have been 1 but is grouped 2
1 2 -0.5694 -1.1306 1
0 1 -1.1306 NaN 1
As seen in above result the row with class 3 is put in group 2 instead of 1. I tested by adding more conditions but nothing worked.

Answer 1

Try this
df_srtd['Group'] = df_srtd.Value.ge(fnd_val)[::-1].cummax() + 1
Out[321]:
Class Value Group
0 1 -1.1306 2
1 2 -0.5694 2
2 3 -0.7241 2
3 4 1.8211 2
4 5 1.5555 2
5 6 0.0416 2
6 7 1.9236 2
7 8 0.1944 1
8 9 -0.0204 1
It is the same on reversed of Class
Sample `df_srtd_rev`
Class Value
8 9 -0.0204
7 8 0.1944
6 7 1.9236
5 6 0.0416
4 5 1.5555
3 4 1.8211
2 3 -0.7241
1 2 -0.5694
0 1 -1.1306
df_srtd_rev['Group'] = df_srtd_rev.Value.ge(fnd_val)[::-1].cummax() + 1
Out[326]:
Class Value Group
8 9 -0.0204 2
7 8 0.1944 2
6 7 1.9236 2
5 6 0.0416 2
4 5 1.5555 2
3 4 1.8211 2
2 3 -0.7241 1
1 2 -0.5694 1
0 1 -1.1306 1