import numpy as np
import pandas as pd
df = pd.DataFrame({
'cond': ['A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'B', 'B','B', 'B', 'B', 'B', 'B','B','B'],
'Array': ['S', 'S', 'TT', 'TT','S', 'S', 'TT', 'TT','S', 'S', 'TT', 'TT','S', 'S', 'TT', 'TT','SS','TT'],
'Area': [3.0, 2.0, 2.88, 1.33, 2.44, 1.25, 1.53, 1.0, 0.156, 2.0, 2.4, 6.3, 6.9, 9.78, 10.2, 3.0, 16.0, 19.0]
})
print(df)
I am trying to make an indicator column that indicates if the area being a certain size has already happened. So for example, if the cond. is A, then I want to indicate the first time that the area is <=1.5 (and for all data points after), and if the cond. is B, then to indicate the first time the area >10 (and for all points after). The final result should look like:
cond Array Area Indicator
0 A S 3.000 0
1 A S 2.000 0
2 A TT 2.880 0
3 A TT 1.330 1
4 A S 2.440 1
5 A S 1.250 1
6 A TT 1.530 1
7 A TT 1.000 1
8 A S 0.156 1
9 B S 2.000 0
10 B TT 2.400 0
11 B TT 6.300 0
12 B S 6.900 0
13 B S 9.780 0
14 B TT 10.200 1
15 B TT 3.000 1
16 B SS 16.000 1
17 B TT 19.000 1
A lot of the other examples I looked at were to indicate if the area for A was <=1.5, or indicate the first time it happens, but not to indicate the first time it happens AND indicate all the datapoints after. The idea is that once my condition hits a certain area, it enters a different "phase" and I'm trying to indicate when "A" enters and stays in that phase (and the equivalent for B).
CodePudding user response：
You can make write the conditions, and then group by cond and use cumsum clip:
mask = (df['cond'].eq('A') & df['Area'].lt(1.5)) | (df['cond'].eq('B') & df['Area'].gt(10))
df['Indicator'] = mask.groupby(df['cond']).cumsum().clip(0, 1)
Output:
>>> df
cond Array Area Indicator
0 A S 3.000 0
1 A S 2.000 0
2 A TT 2.880 0
3 A TT 1.330 1
4 A S 2.440 1
5 A S 1.250 1
6 A TT 1.530 1
7 A TT 1.000 1
8 A S 0.156 1
9 B S 2.000 0
10 B TT 2.400 0
11 B TT 6.300 0
12 B S 6.900 0
13 B S 9.780 0
14 B TT 10.200 1
15 B TT 3.000 1
16 B SS 16.000 1
17 B TT 19.000 1
CodePudding user response：
You could create a boolean Series by comparing the Area values with the cutoff points for each cond. To create the boolean Series, we first have to map the cutoff points to the conds; and since B requires greater than check and A requires less than check; we have to reverse the sign for B to get both conditional checks in the same direction.
Then use groupby.cummax to get the desired indicators:
mapping = {'A':1.5, 'B':-10}
area = df['Area'].mask(df['cond'].eq('B'), -df['Area'])
df['Indicator'] = df['cond'].map(mapping).ge(area).groupby(df['cond']).cummax().astype(int)
Output:
cond Array Area Indicator
0 A S 3.000 0
1 A S 2.000 0
2 A TT 2.880 0
3 A TT 1.330 1
4 A S 2.440 1
5 A S 1.250 1
6 A TT 1.530 1
7 A TT 1.000 1
8 A S 0.156 1
9 B S 2.000 0
10 B TT 2.400 0
11 B TT 6.300 0
12 B S 6.900 0
13 B S 9.780 0
14 B TT 10.200 1
15 B TT 3.000 1
16 B SS 16.000 1
17 B TT 19.000 1
CodePudding user response：
Use expanding to find if any of the previous values match your condition:
condA = df["cond"].eq("A")&df["Area"].expanding().apply(lambda x: x.lt(1.5).any())
condB = df["cond"].eq("B")&df["Area"].expanding().apply(lambda x: x.gt(10).any())
df["Indicator"] = (condA|condB).astype(int)
>>> df
cond Array Area Indicator
0 A S 3.000 0
1 A S 2.000 0
2 A TT 2.880 0
3 A TT 1.330 1
4 A S 2.440 1
5 A S 1.250 1
6 A TT 1.530 1
7 A TT 1.000 1
8 A S 0.156 1
9 B S 2.000 0
10 B TT 2.400 0
11 B TT 6.300 0
12 B S 6.900 0
13 B S 9.780 0
14 B TT 10.200 1
15 B TT 3.000 1
16 B SS 16.000 1
17 B TT 19.000 1