Lately, I have kept myself busy reading the pandas documentation. I am always happy when I find something very useful that I didn’t know before. One of the things that I’ve lately discovered is piping.

Histograms are a great tool for determining a variables’ distribution. As I explained in last post, the process of constructing a histogram can roughly be equated to:

Histograms and kernel densities are ubiquitous in data analysis. At a exploratory stage, we want to know about the variables’ distribution. You can quickly check some descriptive statistics like the mean, variance, percentile and kurtosis. Or, to have a clear picture, you can plot histograms or kernel densities.

Part of time series analysis deals with pinning down the stochastic process that generated the data. If we know how this process looks like, we will be better able to predict its future values.

I had an amazing time in my first time ever Pydata Berlin 2016. Got to know awesome people and very talented researchers. As always, with these events it is always difficult to choose which talk you want to attend to. There were hours where all three talks running in parallel were really interesting.