Predicting the next occurrence based on binary I have no experience in statistics or machine learning. I have a True/False binary array describing occupation of open public spaces 

+---------------------+
|  index   |  Value   |
+---------------------+
| 0        |  True    |
| 1        |  True    |
| 2        |  False   |
| 3        |  False   |
| 4        |  False   |
| 5        |  True    |
| 6        |  False   |
| 7        |  False   |
| 8        |  True    |
| ...      |  ...     |
+---------------------+


Without getting into dependent variables and domain specific heuristics, is there (or maybe more than one) a simple method to do infer the next False in python?

Ideally in pure python or using packages written in pure python.

My question is somewhat similar to this one, but I have more of a time series (i think).
Do you have more features ?
If not, you can try to find a filling pattern, for example, probably some places are filled first because, for example, are closer to the entrance, then another group etc.
Try to plot as a time dependent problem. I cannot tell how much bigger the error will be.
If you have at least the x, y of the place and the timestep of the value, then you have a pattern.