Efficient way to create a DataFrame iteratively
I am working on a financial simulation ([financial_life](https://github.com/MartinPyka/financial_life)), in which I would like to collect my timeseries data in a DataFrame during simulation. However, while looking through several posts on StackOverflow and experimenting with timeit, I noticed that appending data to a Dataframe (e.g. via append or via loc-expansion) is far more inefficient than the append-function for a simple list and then transforming everything to a dataframe, if requested. The latter one is, what I am doing now.

So I was wondering, whether there is any performant way of creating a DataFrame in an iterative manner?