In this tutorial we will learn how to use Pandas sample to randomly select rows and columns from a Pandas dataframe. There are some reasons for randomly sample our data; for instance, we may have a very large dataset and want to build our models on a smaller sample of the data. Other examples are when carrying out bootstrapping or cross-validation. Here we will learn how to; select rows at random, set a random seed, sample by group, using weights, and conditions, among other useful things.
In this short post I will show you a quick fix for the error “unsupported operand type(s) for +: ‘float’ and ‘NoneType’” with Pyvttbl. In earlier posts I have showed how to carry out ANOVA using Pyvttbl (among other packages. See posts 1, 2, 3, and 4 for ANOVA using pyvttbl).
However, Pyvttbl is not compatible with Python versions greater 1.11 (e.g., 1.12.0, that I am running). This may, of course, be due to that Pyvttbl have not been updated in quite some time.
My solution to this problem involves setting up a Python virtual environment (the set up of the virtual environment it is based on the Hitchikers Guide to Python). You will learn how to set up the virtual environment in Linux and Windows.
In this post we will learn how to reverse Pandas dataframe. We start by changing the first column with the last column and continue with reversing the order completely. After we have learned how to swap columns in the dataframe and reverse the order by the columns, we continue by reversing the order of the rows. That is, pandas dataframe can be reversed such that the last column becomes the first or such that the last row becomes the first.