How to add flag column in each group of pandas groupby object - python-3.x

Question

I have df with three columns X, Y, Z. i want to apply groupby function to group data based on X . and then i want to insert flag column in each group . condition for flag column is if Column Z 30% values are greater than 1.5 then add flag column value 1 for group . if Column Z 30% values are not greater than 1.5 then add flag column value 0 for group .
here is my example df:
df = pd.DataFrame({'X':['1', '1', '1' ,'1', '1', '2','2','2','2','2','2','3','3','3'],'Y':["34","45","33","45","44", "66",'67','23','34','10','11','13','12','14'],'Z':["1.2","1.3","1.6","1.7","1.8", "0",'0','0','1.8','1.2','1.3','1.6','1.7','1.8']})
X Y Z
0 1 34 1.2
1 1 45 1.3
2 1 33 1.6
3 1 45 1.7
4 1 44 1.8
5 2 66 0
6 2 67 0
7 2 23 0
8 2 34 1.8
9 2 10 1.2
10 2 11 1.3
11 3 13 1.6
12 3 12 1.7
13 3 14 1.8
desired results:
df_result= pd.DataFrame({'X':['1', '1', '1' ,'1', '1', '2','2','2','2','2','2','3','3','3'],'Y':["34","45","33","45","44", "66",'67','23','34','10','11','13','12','14'],'Z':["1.2","1.3","1.6","1.7","1.8", "0",'0','0','1.8','1.2','1.3','1.6','1.7','1.8'],'flag':["1","1","1","1","1", "0",'0','0','0','0','0','1','1','1']})
print(df_result)
X Y Z flag
0 1 34 1.2 1
1 1 45 1.3 1
2 1 33 1.6 1
3 1 45 1.7 1
4 1 44 1.8 1
5 2 66 0 0
6 2 67 0 0
7 2 23 0 0
8 2 34 1.8 0
9 2 10 1.2 0
10 2 11 1.3 0
11 3 13 1.6 1
12 3 12 1.7 1
13 3 14 1.8 1

Answer 1

Use GroupBy.transform with lambda function and converting boolean to integers by Series.astype:
df["Z"]= df["Z"].astype(float)
f = lambda x: (x > 1.5).sum() > len(x) *.3
#if necessary convert 30% to integer by ceil
#f = lambda x: (x > 1.5).sum() > np.ceil(len(x) *.3)
df['flag'] = df.groupby("X")["Z"].transform(f).astype(int)
print (df)
X Y Z flag
0 1 34 1.2 1
1 1 45 1.3 1
2 1 33 1.6 1
3 1 45 1.7 1
4 1 44 1.8 1
5 2 66 0.0 0
6 2 67 0.0 0
7 2 23 0.0 0
8 2 34 1.8 0
9 2 10 1.2 0
10 2 11 1.3 0
11 3 13 1.6 1
12 3 12 1.7 1
13 3 14 1.8 1

Answer 2

Try this.please let me know if there is any issue.
import pandas as pd
import math
df = pd.DataFrame({'X':['1', '1', '1' ,'1', '1', '2','2','2','2','2','2','3','3','3'],'Y':["34","45","33","45","44", "66",'67','23','34','10','11','13','12','14'],'Z':["1.2","1.3","1.6","1.7","1.8", "0",'0','0','1.8','1.2','1.3','1.6','1.7','1.8']})
df["Z"]= pd.to_numeric(df["Z"])
def func(x):
p = math.ceil(x.shape[0]*3/10)
if sum(x>1.5) > p:
return 1
else:
return 0
t = df.groupby("X")["Z"].apply(lambda x: func(x)).reset_index(name="flag")
df["flag"] = df["X"].apply(lambda x: t[t["X"]==x]["flag"].values[0])
output
X Y Z flag
1 34 1.2 1
1 45 1.3 1
1 33 1.6 1
1 45 1.7 1
1 44 1.8 1
2 66 0.0 0
2 67 0.0 0
2 23 0.0 0
2 34 1.8 0
2 10 1.2 0
2 11 1.3 0
3 13 1.6 1
3 12 1.7 1
3 14 1.8 1