(Original notebooks can be found in this gist) Assume you have data set as follows: ID Date Value x x x where each row contains an ID, a date (given as pd.Datetime) and a value. The objective is to count how many rows occur in each day. import pandas …
I had to (or at least I thought I had to) implement a transformer to be used in a sklearn.pipeline.Pipeline. In a nutshell, I implemented badly the transform method. The original version can be found in this gist. In the following version I fixed it. Furthermore, I left …
TL;DR The function pandas.DataFrame.values is not the inverse of pd.DataFrame(np.array). Introduction An important part of reproducible data science work, is the ability to apply the DAG on the very same dataset. Simplest option is to commit the datasets to a VCS like git. This …
Docker could be a great tool when you want to try out new technologies without taking the risk of breaking your own system. I decided to use Docker when having a quick look into Visdom the new toy from Facebook. I'll outline the steps I took. Start a minimal container …
Recently, I started to use and get to know Docker. One of my central motivations is to utilize this technology for the creation of reproducible research/work. The first minimal working example I came up with contains a notebook which loads the data from a CSV file which is part …