I used to work for a Go shop. We dealt with financial data. I found it so annoying that many of my colleagues would use Go for one-off tasks such as aggregating CSV files, updating the database with some data, or fetching data from the database, and then trying to make a plot. I saw my colleagues again and again implementing basic algorithms such as rolling median, or finding a maximum. Instead of loading data into Pandas and doing a group by, they would create some kind of loopy solution that would use maps.
I totally understand why some people prefer Go in production to Python, but I could never understand why people wouldn't just learn the standard data science tools instead of reinventing the wheel in Go, always debugging their own off-by-one errors. It was difficult for me to trust the results of such analyses, given that I knew how many of the basic functions were written on the fly and probably not even tested.
In the end, I didn't think it was a good use of the company's time. It felt more like an ego thing - thinking and showing that Go is sufficient. It reminds me of how people try to use iPads to code - only to show that they can do it.
I totally understand why some people prefer Go in production to Python, but I could never understand why people wouldn't just learn the standard data science tools instead of reinventing the wheel in Go, always debugging their own off-by-one errors. It was difficult for me to trust the results of such analyses, given that I knew how many of the basic functions were written on the fly and probably not even tested.
In the end, I didn't think it was a good use of the company's time. It felt more like an ego thing - thinking and showing that Go is sufficient. It reminds me of how people try to use iPads to code - only to show that they can do it.