I have a dataframe,you can have it by running this code:
import numpy as np
import pandas as pd
from io import StringIO
df4s = """
LowerAge age 1 2 3 4
0 2 3 o.234 o.234 o.234 o.234
1 3 4 o.234 o.234 o.234 o.234
2 4 2 o.234 o.234 o.234 o.234
3 5 3 o.234 o.234 o.234 o.234
"""
df4 = pd.read_csv(StringIO(df4s.strip()), sep='\s+')
df4
The ouput is:
LowerAge age 1 2 3 4
0 2 3 o.234 o.234 o.234 o.234
1 3 4 o.234 o.234 o.234 o.234
2 4 2 o.234 o.234 o.234 o.234
3 5 3 o.234 o.234 o.234 o.234
Now the logic is like this: for each row ,if LowerAge-1 < age,then df4[str(LowerAge-1)] =1,or it will stay the same, for example:
In the first row,LowerAge-1 equals 1 and it is less than age，then value of column ‘1’(because LowerAge-1 equals 1) will equal 1,
in the second row, LowerAge-1 equals 2 and it is less than age， then value of column ‘2’ will equal 1.
The ideal output should be:
LowerAge age 1 2 3 4
0 2 3 1 o.234 o.234 o.234
1 3 4 o.234 1 o.234 o.234
2 4 2 o.234 o.234 o.234 o.234
3 5 3 o.234 o.234 o.234 o.234
My code is:
index_age = df4['LowerAge']-1
mask=index_age < df4['age']
df4.loc[mask, index_age.astype(str)]=1
my output:
LowerAge age 1 2 3 4
0 2 3 1 1 1 1
1 3 4 1 1 1 1
2 4 2 o.234 o.234 o.234 o.234
3 5 3 o.234 o.234 o.234 o.234
If I want to stick to use mask to do it,what should I do ,Any friend can hlep?
>Solution :
If your case you can do slice then crosstab and update
s = (df.LowerAge-1)
s = s[s<df.age]
df.update(pd.crosstab(s.index,s.astype(str)).where(lambda x : x==1))
df
Out[454]:
LowerAge age 1 2 3 4
0 2 3 1.0 o.234 o.234 o.234
1 3 4 o.234 1.0 o.234 o.234
2 4 2 o.234 o.234 o.234 o.234
3 5 3 o.234 o.234 o.234 o.234