If this is hypothetical (and not explicitly about how you have used Python) then you can say that Python can be used for pretty much everything, although it has it's strong points and weaker points.
You could use python to create a pipeline (snakemake), make plots (matplotlib, seaborn), interface with databases (sqlite), perform processing of fasta/fastq (biopython) and create websites (django).
But even more important than knowing what you can is knowing the limitations. While you have a lot of statistical modules in Python - usually R is the more appropriate language here, definitely for differential expression analysis. But there is nothing that you can do in R that you cannot do in Python. For making production ready code for which speed is an issue then Python is often not your best guess, rather something compiled such as C or Java, or more recent languages such as Go or Rust. It's often 1) having a language you are comfortable in and 2) using the right tool for the job at hand.
I really dislike R, but I do differential expression analysis in R.