Find first true value in a row of Pandas dataframe
pandas, python
Solution
One way would be to use `cumsum` to help find the first:
In [123]: (b1 & b2 & (b2.cumsum(axis=1) == 1)).any(axis=1)
Out[123]:
0 True
1 True
2 False
3 False
dtype: bool
This works because `b2.cumsum(axis=1)` gives us the cumulative number of Trues seen, and cases where that number is 1 and b2 itself is True must be the first one.
In [124]: b2.cumsum(axis=1)
Out[124]:
0 1 2 3 4
0 1 1 2 2 2
1 0 0 1 2 3
2 1 2 2 2 2
3 1 2 3 3 3
Problem
I have two dataframes of boolean values. The first one looks like this: ``` b1=pd.DataFrame([[ True, False, False, False, False], [False, False, True, False, False], [False, True, False, False, False], [False, False, False, False, False]]) b1 Out[88]: 0 1 2 3 4 0 True False False False False 1 False False True False False 2 False True False False False 3 False False False False False ``` If I am just interested in whether each row has any True value I can use the `any` method: ``` b1.any(1) Out[89]: 0 True 1 True 2 True 3 False dtype: bool ``` However, I want to have an added constraint based on a second dataframe that looks like the following: ``` b2 = pd.DataFrame([[ True, False, True, False, False], [False, False, True, True, True], [ True, True, False, False, False], [ True, True, True, False, False]]) b2 Out[91]: 0 1 2 3 4 0 True False True False False 1 False False True True True 2 True True False False False 3 True True True False False ``` I want to identify rows that have a True value in the first dataframe ONLY if it is the first True value in a row of the second dataframe. For example, this would exclude row 2 because although it has a True value in the first dataframe, it is the 2nd true value in the second dataframe. In contrast, rows 1 and 2 have a true value in dataframe 1 that is also the first true value in dataframe 2. The output should be the following: ``` 0 True 1 True 2 False 3 False dtype: bool ```