Python neural network in 11 lines – the name is a bit misleading but nice DIY initiative.
Python or R – The bottom line for me is that both are tools and you should adjust the tool you use to the task. I personally feel more comfortable on Python and really glad that ideas, prototypes and thoughts I had can move to production using almost the same code and does not require additional work.
Python, Ruby and Golang web framework comparison – I think that for most of the people and projects this comparison is not relevant as either they are experts \ using a specific language or the language is limited by the project environment. But for the cases it is relevant – switching between language, learning a language and POCing it is nice to have a comparison with frameworks you know, feel comfort with.
Pandas and Apache Spark data frames differences – Both Pandas and Apache Spark are very common tools for data scientist using Python. It is therefore very important to know the differences between the data frames in both tools as one may assume it might function the same.
Anti patterns – at first glance I thought it was a very weird idea but then going over several examples and it was actually interesting and enlightening. Some things I do automatically and haven’t thought of them for a long time, some matters of style, etc. Interesting although I was skeptic in the beginning 🙂