Replace values in a column that come after a specific value-CodePudding

I would like to replace values in a column, but only to the values seen after an specific value

for example, I have the following dataset:

In [108]: df=pd.DataFrame([[12,13,14,15,16,17],[4,10,5,6,1,3],[1, 3,5,4,9,1],[2, 4, 1,8,3,4], [4, 2, 6,7,1,8]], columns=['ID','time,'A', 'B', 'C']) 

In [109]: df
Out[109]: 

   ID  time  A  B  C  
0  12  4     1  2  4
1  13  10    3  4  2
2  14  5     5  1  6
3  15  6     4  8  7
4  16  1     9  3  1
5  17  3     1  4  8

and I want to change for column "A" all the values that come after 5 for a 1, for column "B" all the values that come after 1 for 6, for column "C" change all the values after 7 for a 5. so it will look like this:

   ID  time  A  B  C  
0  12  4     1  2  4
1  13  10    3  4  2
2  14  5     5  1  6
3  15  6     1  6  7
4  16  1     1  6  5
5  17  3     1  6  5

I know that I could use where to get sort of a similar effect, but if I put a condition like df["A"] = np.where(x!=5,1,x), but obviously this will change the values before 5 as well. I can't think of anything else at the moment.

Thanks for the help.

CodePudding user response：

Use DataFrame.mask with shifted valeus by DataFrame.shift, compared by dictioanry and for next Trues is used DataFrame.cummax:

df=pd.DataFrame([[12,13,14,15,16,17],[4,10,5,6,1,3],
                 [1, 3,5,4,9,1],[2, 4, 1,8,3,4], [4, 2, 6,7,1,8]], 
                index=['ID','time','A', 'B', 'C']).T


after = {'A':5, 'B':1, 'C': 7}
new = {'A':1, 'B':6, 'C': 5}

cols = list(after.keys())
s = pd.Series(new)
df[cols] = df[cols].mask(df[cols].shift().eq(after).cummax(), s, axis=1)
print (df)
   ID  time  A  B  C
0  12     4  1  2  4
1  13    10  3  4  2
2  14     5  5  1  6
3  15     6  1  6  7
4  16     1  1  6  5
5  17     3  1  6  5