Tag Archives: Visualization
Recent advances in data acquisition in the NBA allow us to gain insight into the sport and it's players, and, we can use this data to play with Python's visualization abilities. In this post data from the current season is used to see just how dominant the Golden State Warriors are; Fivethirtyeight's ELO rankings help to make an interactive graph (not usable on a mobile device); shot charts from stats.nba.com help to build a 3D visualization of a player's shots; and, we will look for any changes in Stephen Curry's play that may explain his explosive 2015-2016 season.
When I decided to move to Israel the most frequent question, asked overtly or discretely, was "Is it safe?" The undertone was always that Israel is not and I may be a bit crazy for going. This post will examine how safe Israel is using the USA as the default comparison. There are so many ways to measure safety. Feeling safe is much different than actually being safe. What it means to be safe is not clear. However, there are some basic metrics we will explore here.
The first thing that comes to mind is homicide. The following graph comes from the world bank. The USA is, at all times, at least twice as dangerous than Israel for homicides.
Most good questions start over a round of beers and this is no different. During game 5 of the 2014-2015 NBA finals my friends and I got into a typical sports argument; who is the best and why? I had been arguing that Lebron James' performance in these finals was historically good but my friends disagreed. Forcefully. As this discussion flowed and ebbed we got into rating styles of professional basketball itself, and eventually, what makes a good NBA draft. Let's take a look using data from basketball-reference.com.
Nothing says romance like analyzing WhatsApp text messages using Python. In an effort to understand my relationship with my girlfriend I present, with lovely graphs, our texting habits.
Using Python and some of its standard packages like Numpy, Scipy, Matplotlib, and Pandas, I parsed data from our WhatsApp conversations. The data began on August 12, 2014, at 10:47pm local time in Amsterdam. The last entry used is from May 18, 22:45 Mountain time. There are a total of 27192 texts. The first table gives some statistics regarding the data.