find next working day in a pandas column
<p>I have a pandas df that represents the open days of a shop looking like:</p>

<pre><code>         Dates  Open
0   2016-01-01     0
1   2016-01-02     0
2   2016-01-03     0
3   2016-01-04     1
4   2016-01-05     1
5   2016-01-06     1
6   2016-01-07     1
7   2016-01-08     1
8   2016-01-09     0
9   2016-01-10     0
10  2016-01-11     1
11  2016-01-12     1
12  2016-01-13     1
13  2016-01-14     1
14  2016-01-15     1
15  2016-01-16     0
16  2016-01-17     0
17  2016-01-18     1
18  2016-01-19     1
19  2016-01-20     1
20  2016-01-21     1
21  2016-01-22     1
22  2016-01-23     0
23  2016-01-24     0
24  2016-01-25     1
25  2016-01-26     1
26  2016-01-27     1
27  2016-01-28     1
28  2016-01-29     1
</code></pre>

<p>this can be recreated by:</p>

<pre><code>Dates =['2016-01-01',
 '2016-01-02',
 '2016-01-03',
 '2016-01-04',
 '2016-01-05',
 '2016-01-06',
 '2016-01-07',
 '2016-01-08',
 '2016-01-09',
 '2016-01-10',
 '2016-01-11',
 '2016-01-12',
 '2016-01-13',
 '2016-01-14',
 '2016-01-15',
 '2016-01-16',
 '2016-01-17',
 '2016-01-18',
 '2016-01-19',
 '2016-01-20',
 '2016-01-21',
 '2016-01-22',
 '2016-01-23',
 '2016-01-24',
 '2016-01-25',
 '2016-01-26',
 '2016-01-27',
 '2016-01-28',
 '2016-01-29']

Open = [0,0,0,1,1,1,1,1,0,0,1,1,1,1,1,0,0,1,1,1,1,1,0,0,1,1,1,1,1]


df = DataFrame({'Dates':Dates, 'Open':Open})
</code></pre>

<p>the column open represents the days in which the shop is open for deliveries. I want to create a new column with the next open day for each date in the leftmost column. I cannot use predefined workingdays functions but I have to use the Open column to determine whether or not the shop is open. The desired outcome would be:</p>

<pre><code>         Dates  Open     Desired
0   2016-01-01     0  2016-01-04
1   2016-01-02     0  2016-01-04
2   2016-01-03     0  2016-01-04
3   2016-01-04     1  2016-01-05
4   2016-01-05     1  2016-01-06
5   2016-01-06     1  2016-01-07
6   2016-01-07     1  2016-01-08
7   2016-01-08     1  2016-01-11
8   2016-01-09     0  2016-01-11
9   2016-01-10     0  2016-01-11
10  2016-01-11     1  2016-01-12
11  2016-01-12     1  2016-01-13
12  2016-01-13     1  2016-01-14
13  2016-01-14     1  2016-01-15
14  2016-01-15     1  2016-01-18
15  2016-01-16     0  2016-01-18
16  2016-01-17     0  2016-01-18
17  2016-01-18     1  2016-01-19
18  2016-01-19     1  2016-01-20
19  2016-01-20     1  2016-01-21
20  2016-01-21     1  2016-01-22
21  2016-01-22     1  2016-01-25
22  2016-01-23     0  2016-01-25
23  2016-01-24     0  2016-01-25
24  2016-01-25     1  2016-01-26
25  2016-01-26     1  2016-01-27
26  2016-01-27     1  2016-01-28
27  2016-01-28     1  2016-01-29
28  2016-01-29     1  
</code></pre>