Data Visualization

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.

Last Friday, the Cakti set aside regular client projects for our quarterly ShipIt Day, a chance for personal development and independent projects. People work individually or in groups to flex their creativity, tackle interesting problems, or expand their personal knowledge. This quarter’s ShipIt Day was all about open source contributions, frontend fixes, and learning new (or revisiting old) programming languages. Read about the various ShipIt Day projects for Q2 of 2016 below.

We’re starting a new, monthly series on the Caktus blog highlighting the articles and posts shared by Cakti that drew the most attention on Twitter. These roundups will include everything from Django how-tos to explorations of the tech industry, to innovations for social good.