Themes, yes. What we’ve all been waiting for in this post after 14 posts of mostly troubleshooting haha. To review some of the plots we created with datashader: Figure 1: NYC Crime Density Figure 2: NYC Crime Density (Top 3% Most Dense Regions Highlighted) Figure 3: NYC Crime Density by Offense Level Figure 4: NYC […]
Read More NYPD Crime #15 – TakeawaysReview This is a direct continuation of last post, so I won’t review too much. Last time, we mapped NYPD complaint / offense density geographically using datashader. We explored the ideas of color gradients, color gradient floors, histogram equalization, and looking at top x% of densities. Here, we will look at categorical mapping, which allows […]
Read More NYPD Crime #14 – Data Exploration (Part VIIII – Lat Long Visualization By Category)Review Last post, we looked at Loading a parquet Installing datashader Using datashader We ran into enough issues to make a post out of those items. Parquet On the parquet side, I became familiar with the parquet file format and the partitioned nature of data storage within distributed computing platforms (in this case, I used […]
Read More NYPD Crime #13 – Data Exploration (Part VIII – Datashader Deep Dive)Intro In our last post, we looked at some time based fields. Summer months are big for incidents, so are the 1st, 10th, 15th, and 20th of the month. Why? We’re not sure yet, maybe with some more exploration, we’ll get to the bottom of that. Let’s load up our data from S3 again. In [1]: […]
Read More NYPD Crime #11 – Data Exploration (Part VI – Complaint Type & Location Exploration)Intro At this point, we’ve gotten a feel for the data, but we really still don’t know what’s in it. After 9 posts, we’re finally ready to actually explore haha… Oh, the miraculous world of data science… Similar to the All-NBA prediction project I was doing, I really have no objective here. I came in […]
Read More NYPD Crime #10 – Data Exploration (Part V – Date-Time Exploration)Getting the Data Getting the data was quite simple. The Edmonton Open Data portal has a Property Assessment page. I’ve downloaded the dataset into the data folder of this project. Loading the Data Let’s fire it up into pandas and check out a few data points! In [1]: # Enable plots in the notebook %matplotlib inline […]
Read More Edmonton Property Assessment #2 – Exploring Property Assessment DatasetIntroduction Hello all. This must be getting tiring if ANYBODY has actually made it this far into this series of posts at this point. How perfect… Larry David is even at A BASKETBALL GAME. I wanted to create one final post to summarize my objective and findings. I’ve been all over the place since I […]
Read More All-NBA Predict #32 – Predicting 2016 – 2017 All-NBA Teams (End-To-End)Data Exploration So, I have a crapton of data. I didn’t really take too much of a scientific approach last time when analyzing team stats, I had some ideas in my head that I wanted to execute against, and essentially one thing just lead to another. Eventually I came to a point where I was […]
Read More All-NBA Predict #10 – Exploration of Historical NBA Players (Part II, Scatterplot Matrices)
