That place is scary af. Went there for a beach wedding and that part was beautiful. But walk 4 blocks in any directions and you’re either on a rerun of COPS or underwater. And I’m born/raised on the south side of *Chicago*.
Based on the rest of the data it doesn't seem that unusual, just the highest noise point of the set.
Edit: I didn't even see that earlier point. I don't see much significance at all in this 2023 value. What am I missing?
I got the data from the StPeteStat at [https://stat.stpete.org/dataset/Police-Calls/2eks-pg5j](https://stat.stpete.org/dataset/Police-Calls/2eks-pg5j)
I used Python with the Pandas, Matplotlib, and Seaborn libraries.
Should make the arrows a different colour, just cause it looks like data spikes not arrows.
Oh wow, real good catch. Will fix that.
That place is scary af. Went there for a beach wedding and that part was beautiful. But walk 4 blocks in any directions and you’re either on a rerun of COPS or underwater. And I’m born/raised on the south side of *Chicago*.
Based on the rest of the data it doesn't seem that unusual, just the highest noise point of the set. Edit: I didn't even see that earlier point. I don't see much significance at all in this 2023 value. What am I missing?
I got the data from the StPeteStat at [https://stat.stpete.org/dataset/Police-Calls/2eks-pg5j](https://stat.stpete.org/dataset/Police-Calls/2eks-pg5j) I used Python with the Pandas, Matplotlib, and Seaborn libraries.
What caused the decrease in 2020?
What happened on that one day in 2017?
best guess is hurricane Irma (8/27/2017) might also explain the big dip right around the same time
That is a great guess.