Biopython VS Pandas

Usearch will do all-against-all comparisons, cluster sequences, and produce alignments for each cluster. You can set the clustering threshold (proportion of residues identical). The alignments are in fasta format, which is pretty standard. If all you want is basic similarity it might be easiest to just write something that calculates normalized Hamming distances (typically called p-distances in the molecular evolution literature) between pairs of sequences. I suspect the biopython fasta reader (you can install biopython from https://biopython.org/) will be good enough.

Yes, the computing tool for biological computation.

Beside that their core lectures to non-computer scientists are public (survey), workshops by software carpentry move around the globe. Maybe your intent to seed hands-on knowledge is in similar tune before heading for biopython, bioperl, bioawk. It doesn't hurt to tap into resources initially written for non-labrats either, e.g. about regular expressions by programming historian.

Well, what language do you want to learn? What is your background so far? Assuming it is more on the side of biology, software carpentry's Python may eventually lead to biopython? Though there equally is a chance for AWK (Hack the planet's text! and bioawk...

I think you mind find something of a community around BioPython, which might be helpful. Just looking at the capabilities will probably be instructive as well.

Python is the main programming language I use nowadays. In particular numpy and pandas are of course extremely useful. I also use biopython package - a collection of software tools for biological computation written in Python by an international group of researchers and developers.

Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.

there is pull request to integrate in both pydantic extra types and into pandas cose [1]

[1]: https://github.com/pandas-dev/pandas/issues/53999

Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.

pandas: A library for data analysis in Python

Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.

Pandas, a powerful data manipulation library in Python, has become an essential tool for data scientists and analysts. One of its key functions is read_csv(), which allows users to read data from CSV (Comma-Separated Values) files into a Pandas DataFrame. In this tutorial, brought to you by CodesWithPankaj.com, we will explore the intricacies of read_csv() with clear examples to help you harness its full potential.

So, what I'd like to do is write a documentation package in Python to recreate what I've lost. I plan to build upon the fantastic python-docx and docxtpl packages, and I'll probably rely on pandas from much of the tabular stuff. Here are the features I intend to include:

Weirdly most of my time spent with data analysis was in the C layers in pandas.

Explore here.