Filter Rows Based One Column' Value And Calculate Percentage Of Sum In Pandas

Given a small dataset as follows: value input 0 3 0 1 4 1 2 3 -1 3 2 1 4 3 -1 5 5 0 6 1 0 7 1 1 8

Solution 1:

You can sum not matched rows by missing values to Series s by Series.where and divide only rows not matched mask filtered by DataFrame.loc, last round by Series.round:

mask = df['input'] != -1
df.loc[mask, 'pct'] = (df.loc[mask, 'value'] / df['value'].where(mask).sum()).round(2)

print (df)
   value  input   pct
0      3      0  0.18
1      4      1  0.24
2      3     -1   NaN
3      2      1  0.12
4      3     -1   NaN
5      5      0  0.29
6      1      0  0.06
7      1      1  0.06
8      1      1  0.06

EDIT: If need replace missing values to 0 is possible use second argument in where for set values to 0, this Series is possible also sum for same output like replace to missing values:

s = df['value'].where(df['input'] != -1, 0)
df['pct'] = (s / s.sum()).round(2)

print (df)
   value  input   pct
0      3      0  0.18
1      4      1  0.24
2      3     -1  0.00
3      2      1  0.12
4      3     -1  0.00
5      5      0  0.29
6      1      0  0.06
7      1      1  0.06
8      1      1  0.06