fill each row of a dataframe with zero starting from the column in which the values decreases with r-CodePudding

I would like to fill each row of a dataframe with zero starting from the column in which the value decreases with respect to the value in the previous column

I have the following Dataframe:

ActualDf = pd.DataFrame(np.array([[2, 3, 2, 3, 4, 5, 0, 0, 0, 0, 0], [1, 1, 1, 2, 2, 3, 1, 1, 0, 0, 0], [2, 2, 2, 3, 3, 5, 3, 3, 0, 0, 0], [2, 3, 2, 3, 3, 3, 4, 1, 2, 0, 0], [3, 3, 1, 2, 2, 5, 0, 0, 0, 0, 0], [1, 2, 3, 1, 2, 3, 0, 0, 0, 0, 0]]),
columns=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10']

)

And I would like to obtain this one

DesiredDf = pd.DataFrame(np.array([[2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 2, 2, 3, 0, 0, 0, 0, 0], [2, 2, 2, 3, 3, 5, 0, 0, 0, 0, 0], [2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0]]),
columns=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10'])

How is it possible to da that in an efficient way?

CodePudding user response：

Here is an approach similar using a mask:

DesiredDf =ActualDf.mask(ActualDf.diff(axis=1).lt(0).cummax(axis=1), 0)

output:

   0  1  2  3  4  5  6  7  8  9  10
0  2  3  0  0  0  0  0  0  0  0   0
1  1  1  1  2  2  3  0  0  0  0   0
2  2  2  2  3  3  5  0  0  0  0   0
3  2  3  0  0  0  0  0  0  0  0   0
4  3  3  0  0  0  0  0  0  0  0   0
5  1  2  3  0  0  0  0  0  0  0   0

CodePudding user response：

You could use diff and lt to find where the difference is negative, and then use cumsum and gt to flag all cells after the first negative value.

ActualDf[ActualDf.diff(axis=1).lt(0).cumsum(1).gt(0)] = 0