The problem consists in changing sequences of numbers between zeros, to the first value of the specific sequence.
Example:
| Input(df['p']) | Desired Output(df['Do']) |
|---|---|
| 0 | 0 |
| 0 | 0 |
| 0 | 0 |
| 115 | 115 |
| 090 | 115 |
| 0 | 0 |
| -80 | -80 |
| -90 | -80 |
| -70 | -80 |
| 0 | 0 |
I have tried using np.where, and logic to find the numbers that must be changed, though i'm not able to scale this solution. It only works for one row.
Example:
#check a change in value #eliminate the first change from zero #assigning the above p value
df['A'] =np.where(df['p']!=df['p'].shift(1),np.where(df['p'].shift(1)==0,df['p'],df['p'].shift(1)),df['p'])
| Input(df['p']) | Actual Output(df['A']) |
|---|---|
| 0 | 0 |
| 0 | 0 |
| 0 | 0 |
| 115 | 115 |
| 090 | 115 |
| 0 | 0 |
| -80 | -80 |
| -90 | -80 |
| -70 | -90 |
| 0 | 0 |
Something like that should work, but np.where doesn't support iterations between the same column.
#check a change in value #eliminate the first change from zero #assigning the above Do value
#\/ \/
df['Do'] = np.where(df['p']!=df['p'].shift(1),np.where(df['p'].shift(1)==0,df['p'],df['Do'].shift(1)),df['p'])
Tks!
what you want are the rows not equal (ne) to 0 and where the previous row (with shift) is not 0 either. Then mask the values for these rows and ffill to propagate the previous not 0 value
#input
df = pd.DataFrame({'p':[0,0,0,115,90,0,-80,-90,-70,0]})
# get the result
df['res'] = df['p'].mask(df['p'].ne(0) & df['p'].shift().ne(0)).ffill()
print(df)
p res
0 0 0.0
1 0 0.0
2 0 0.0
3 115 115.0
4 90 115.0
5 0 0.0
6 -80 -80.0
7 -90 -80.0
8 -70 -80.0
9 0 0.0
or you can check only once ne to 0 and use the shift after
m = df['p'].ne(0)
df['res'] = df['p'].mask(m & m.shift()).ffill()