Convert the -ve values into 1 and reduce those values from +ve values of same variable in pandas

Question

I have a dataframe

df_in = pd.DataFrame([["A",-2],["B",23],["A",-4],["A",14],["B",12],["A",34],["B",-4],["C",-1],["A",-5],["B",21],["C",4],["B",-6]], columns=['var', 'val'])

var val
A   -2
B   23
A   -4
A   14
B   12
A   34
B   -4
C   -1
A   -5
B   21
C    4
B   -6

I want to convert all -ve values to 1. find the difference between the value and 1, sum it at var level. Divide the sum by no. of +ve value of var and reduce that value from the + ve values of var. Ex: var A has 3 -ve value and 2 +ve values. Covert all -ve values to 1 and find the difference like diff between -2 and 1 is 3, -4 and 1 is 5 -5 and 1 is 6. Sum 3+5+6=14. A has 2 +values. So divide 14 by 2 which is 7. Now reduce 7 from +ve values(23,34) of A. Similarly repeat for other var also either by doing groupby.

Expected Output is:

df_out = pd.DataFrame([["A",1],["B",19],["A",1],["A",7],["B",8],["A",27],["B",1],["C",1],["A",1],["B",17],["C",2],["B",1]], columns=['var', 'val'])

var val
A   1
B   19
A   1
A   7
B   8
A   27
B   1
C   1
A   1
B   17
C   2
B   1

How to do it?

Answer 1

Use:

#Trues for positive
m = df_in['val'].gt(0)
#substract 1 form right side and aggregate negative values
neg = df_in.loc[~m, 'val'].rsub(1).groupby(df_in['var']).sum()
#count positive values
pos = df_in.loc[m, 'var'].value_counts()
#divide both
diff = neg.div(pos)
#mapping difference with subtract `val` for positive else set 1
df_in['val'] = np.where(m, df_in['val'].sub(df_in['var'].map(diff)), 1)
print (df_in)
   var   val
0    A   1.0
1    B  19.0
2    A   1.0
3    A   7.0
4    B   8.0
5    A  27.0
6    B   1.0
7    C   1.0
8    A   1.0
9    B  17.0
10   C   2.0
11   B   1.0