pandas dataframe fails to assign value to slice subset

Question

I'm trying to change all values in the slice except the first one but it does not work... what am i doing wrong ?

print(test)
test.loc[(test.col_1==-5)&(test.index>'2018-07-17 13:00:00')&(test.index<'2018-07-17 14:00:00'),['col_1']][1:]=-1
print(test)

provides the below output

17/07/2018 13:51:00 -5
17/07/2018 13:52:00 -1
17/07/2018 13:53:00 -5
17/07/2018 13:54:00 -5
17/07/2018 13:55:00 -5
17/07/2018 13:56:00 -5
17/07/2018 13:57:00 -5
17/07/2018 13:58:00 -5
17/07/2018 13:59:00 -5

17/07/2018 13:51:00 -5
17/07/2018 13:52:00 -1
17/07/2018 13:53:00 -5
17/07/2018 13:54:00 -5
17/07/2018 13:55:00 -5
17/07/2018 13:56:00 -5
17/07/2018 13:57:00 -5
17/07/2018 13:58:00 -5
17/07/2018 13:59:00 -5

whereas i was expecting the 2nd output to be

17/07/2018 13:51:00 -5
17/07/2018 13:52:00 -1
17/07/2018 13:53:00 -1
17/07/2018 13:54:00 -1
17/07/2018 13:55:00 -1
17/07/2018 13:56:00 -1
17/07/2018 13:57:00 -1
17/07/2018 13:58:00 -1
17/07/2018 13:59:00 -1

Answer 1

You can use numpy.where and use indexing [1:] to exclude the first time the criterion is True . Here's a minimal example:

df = pd.DataFrame([[1, -5], [2, -5], [3, -1], [4, -5], [5, -5], [6, -1]],
                  columns=['col1', 'col2'])

df.iloc[np.where(df['col1'].between(2, 5))[0][1:], 1] = -1

print(df)

   col1  col2
0     1    -5
1     2    -5
2     3    -1
3     4    -1
4     5    -1
5     6    -1

Answer 2

There is problem join boolean indexing (filtering) with selecting, one possible solution is add new condiction:

test.index = pd.to_datetime(test.index)
mask = (test.col_1==-5)&(test.index>'2018-07-17 13:00:00')&(test.index<'2018-07-17 14:00:00')

m1 = np.arange(len(test)) > 1
test.loc[mask & m1, 'col_1']=-1

print (test)
                     col_1
2018-07-17 13:51:00     -5
2018-07-17 13:52:00     -1
2018-07-17 13:53:00     -1
2018-07-17 13:54:00     -1
2018-07-17 13:55:00     -1
2018-07-17 13:56:00     -1
2018-07-17 13:57:00     -1
2018-07-17 13:58:00     -1
2018-07-17 13:59:00     -1