Pandas: if last non-zero value < 0: replace with 0

Question

I have a dataframe:

df = pd.DataFrame({'A': [1,2,-2,0,0], 'B': [0, 0, 0, 3, -2], 'C' : [0, 0, -2, 4, 0], 'D': [0, -3, 2, 1, -2]} ) 

Out: 
   A  B  C  D
0  1  0  0  0
1  2  0  0 -3
2 -2  0 -2  2
3  0  3  4  1
4  0 -2  0 -2

For each column , if the last non-zero value < 0: repace it with 0.

expected result:

df_end = pd.DataFrame({'A': [1,2,0,0,0], 'B': [0, 0, 0, 3, 0], 'C' : [0, 0, -2, 4, 0], 'D': [0, -3, 2, 1, 0]} ) 
df_end

Out: 
   A  B  C  D
0  1  0  0  0
1  2  0  0 -3
2  0  0 -2  2
3  0  3  4  1
4  0  0  0  0

I have solved the opposite problem (if first non-zero value < 0, replace with 0) with:

df.where(df.gt(0).cummax(),0)

I now need it to look at the table from the bottom.

#EDIT As was pointed out in the comments, df.where(df.gt(0).cummax(),0) will eliminate all - values until the first positive. In my original Dataframe Positive and negative values always alternate, so df.where(df.gt(0).cummax(),0) works. I made a bad example dataframe.

Answer 1

Let's try iloc[::-1] to revert df :

df[~df.iloc[::-1].gt(0).cummax()] = 0

Output:

   A  B  C  D
0  1  0  0  0
1  2  0  0 -3
2  0  0 -2  2
3  0  3  4  1
4  0  0  0  0

---

Update : As @Ben.T commented, in the case you have several negative endings, the above would turn them all into zero. This would fix that:

df = pd.DataFrame({'A': [1,2,-2,-1,0], 'B': [0, 0, 0, 3, -2], 'C' : [0, 0, -2, 4, 0], 'D': [0, -3, 2, 1, -2]} ) 

s = df.iloc[::-1]
df[s.lt(0).cumsum().eq(1) & (~s.gt(0).cummax())] = 0

Output:

   A  B  C  D
0  1  0  0  0
1  2  0  0 -3
2 -2  0 -2  2
3  0  3  4  1
4  0  0  0  0

Answer 2

This will only replace the last negative value to 0

s=np.sign(df).iloc[::-1].eq(-1).idxmax()
df.values[s,df.columns.get_indexer(s.index)]=0
df
   A  B  C  D
0  1  0  0  0
1  2  0  0 -3
2  0  0  0  2
3  0  3  4  1
4  0  0  0  0