Why Python keeps growing, explained

I have been a heavy Python user now about 15 years, but for me now I'm increasingly reaching for modern JavaScript and particularly TypeScript to do the things I would have traditionally done with Python.

ES modules, fat arrow expressions, and all the other nice new syntax and library features have made the language so more pleasant to use. In many ways the ergonomics of TypeScript in particular are far superior to Python now, and I find that really surprising.

I haven't yet found a replacement for Django (particularly the ORM/Admin/Forms combination) it is just so incredibly productive to use. So, I don't think I will be moving off it, but it's certainly not the growth language for me anymore.

Don't forget about FORTRAN

There's also the argument that at a certain scale the time of a developer is simply more expensive than time on a server.

If I write something in C++ that does a task in 1 second and it takes me 2 days to write, and I write the same thing in Python that takes 2 seconds but I can write it in 1 day, the 1 day of extra dev time might just pay for throwing a more high performance server against it and calling it a day. And then I don't even take the fact that a lot of applications are mostly waiting for database queries into consideration, nor maintainability of the code and the fact that high performance servers get cheaper over time.

If you work at some big corp where this would mean thousands of high performance servers that's simply not worth it, but in small/medium sized companies it usually is.

I started in Python, as a Biologist (hooray for Jupyter-lab, coding so visually, in small steps, with output just there is so great when starting to learn Python). I ventured into other languages every now and then. For example I tried to make an Android app in Kotlin that gets info from some API. I expect something like this but with more brackets everywhere:

    import request
   
    data = request.get(https://some.api/get_some_json)
    some_value_I_want = data['dig']['into']['nested_structure']
    

I didn't get it to work at all, I was lines and lines of code into the program when I didn't even get to see any returned values.

Also, say I want to make a nice plot of some tabular data (.tsv), I do:

    import pandas as pd
    import seaborn as sns
     
    data = pd.read_csv('some_file.tsv', sep='\t')
    pd.melt(data, value_vars=['s', 'max_vms'], id_vars=['sample', 'process', 'N'])
    g = sns.FacetGrid(data=data, col='process', row='variable', hue='sample', sharey=False, margin_titles=True)
    g.map(sns.barplot, 'sample', 'value', order=data['sample'].unique())
    

Boom, what a plot (or large grid of plots actually), so much information. Can anyone show me how to do this in some other language (but R)? Idk, maybe I'm just not so smart, I just learned to program at 35 after always being a biologist, but any venture into any language has me thinking: Why does this have to be so complicated?

(Btw, I'm putting 4 spaces in front of the code, why is it not rendered as code?)

When I just started I used Python to read, sort and transform images and output them to a PowerPoint file. One can use CSS like synthax to format the slides. Boom PPT with 120 slides with images and data from some Excel file that convinced a lot of people with a lot of data that fluorescent images next to H&E stains + metadata can be nice. Is it the best way to present such data? Meh. But man was it cool and easy and time saving.

If by "optimistic" you mean "confident we'll get some free wins that are ultimately pretty small for normal apps" then yes :P