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What inferences and premises can be made from this chart in our analysis of the event timeline information?
- On the first of month, from 13 to 16, a spree of 4 thefts take place in very quick succession.
- On the third of month, from 20 to 23, a spree of 4 vandalisms take place in very quick succession.
- On the tenth of month, from 22 to 24, a spree of 5 burglaries take place, in quick succession, where one stand out taking place one hour before.
- On the eleventh of month, from 8 to 10, two burglaries take place in quick succession.
- On the fifteenth of month, from 9 to 14, there are 5 1-hour intervalled assaults take place.
- On the nineteenth of month, from 10 to 15, there are 5 1-hour intervalled thefts take place.
- On the twentieth of month, from 19 to 22, there are 4 1-hour intervalled vandalisms take place.
- On the twenty-fifth of month, from 15 to 18, there are 4 1-hour robberies take palce.
- On the twenty-seventh of month, from 17 to 21, there are 5 ~1-hour intervalled assaults take place, where two stand out taking place in quick succession.
We can also start seeing more patterns, such as the amount of people involved in the burglaries, thefts, and assaults. Maybe there is one person who becomes absent? Maybe this person is performing worse off than the others? Many inferences can be drawn based on these premises we have identified.
You need to parse the timestamp information into one column following timedate format: YYYY-MM-DD HH:MM:SS
Easy peasy! Just copy the code into a Jupyter Notebook cell and run!
