Data Science

We regularly use tools like PostgreSQL, Pandas, and Jupyter Notebooks to analyze data here at Caktus. Recently, we were reviewing North Carolina traffic stop data for the NC CopWatch project and had the opportunity to use PostgreSQL's window functions, which are helpful when aggregating data.

Lately, there has been a lot of talk about scoring in the NBA because LeBron James surpassed Kareem Abdul-Jabbar with 38,390 career points. I have noticed that there is not much discussion about post-season scoring, so I searched for this dataset on Kaggle (nba_playoffs.csv) which contains the top 25 all-time post-season scoring leaders. Post-season scoring is its own beast. Since teams face one opponent multiple times in a row, they can better concentrate on the opposing team and its individual players, particularly star players. This results in improved defenses across the board. However, the post-season also means players improving their game. What is the result of improved defenses and players alike? Only elite players score consistently and thus, only the NBA's elite are on this list. This post will first examine the dataset using Pandas and then use Seaborn to graph such data.

Above: A view of the busy exhibit hall. Photo copyright © 2019 by Sean Harrison. All rights reserved.

PyCon 2019 attracted 3,393 attendees, including a group of six Cakti. When we weren’t networking with attendees at our booth, we attended some fascinating presentations. Below are some of our favorites. You can watch these talks and more on the PyCon 2019 YouTube channel.