30 Days of Python
github.com
github.com
If the intention of coding in Python is to get stuff done quickly, it seems a bit silly to handcuff yourself by avoiding one of the biggest time-savers.
I strongly believe there's a balance to be struck between getting started quickly and understanding how your code is run. Especially if it's an interpreted language like Python.
If you do understand opening a terminal in your project's repository and executing `python3 ./mypythonfile` then this should be prioritized. Even for noobs.
So many of my tools over the years have stuck with their simple command line initiation rather than wrapping into GUIs or other alternatives. Being able quickly write “python [whatever you are doing]” is awesome.
I want them to realise that code is just text files, same with html
x = 1
print(x)
y = 2
print(x, y)
Some developers seem to think that using a debugger is a sign of weakness, or that it leads you to write bad code. I see it differently: the debugger is one of the best tools not only for debugging, but also to help you understand a complex codebase that you are jumping into.
Even the concept of the "print line" debugging into a lot file seems kind of foreign to them sometimes.
They instead rely on running the code, changing something, running it again, etc.
I am a big fan of reading through the code. That is almost always enough for me to find the issue and point it out to them assuming I understand the input and output context well enough. But if that doesn't work, add a debug point and fire it up. Not using a debugger seems like a massive waste of time in a situation like this. So much so that I have started asking "did you debug this" or "have you watched this happen in a debugger" before I will help out much. If they say "no" because they don't know how, I will teach them to do so.
Never met this term before - IIUC, SQL tracing is MS-specific? If I may ask, in what kind of situations would you use it?
Basically, it is just a journal of all queries (really ANYTHING going on in the db but for developers it is useful to limit to queries) and typically filtered by database and by keyword (e.g. table or stored procedure name).
You can set up a trace to track long-running queries on a production server (DBA might do this) or on a dev server a developer might use it to capture SQL run from their code (e.g. a complex stored procedure with a lot of input params) so you don't have to try to assemble the SQL using a debugger, for example.
A good example might be "I am doing everything right in code, but the data set is coming back empty" so you profile it and realize the SQL is being called with an empty string and not a null, or whatever. When reviewing the code you thought "it will be a null". That kind of thing.
Totally agree.
In the first week of programming, students should be allowed only use the debugger (no direct runs) in every course/collage around the world. I saw firsthand how many courses/collages students struggle with logic because debugging was never introduced, and they were unaware of how to step through each line.
Another mistake that new programmers make is writing 30 lines of code and then run and lost why bugs occurring. As a rule of thumb, they should debug/run the program every 7 new lines of code.
I was a print-line debugger for years.
Really? I have never encountered that in the last 18 years in any of the 8 or 9 places I've worked.
I've worked with many know-it-alls who both knew it all and didn't. With arrogant and humble developers. No one ever said that.
https://news.ycombinator.com/item?id=29387515
(See the last reply in that thread.)
https://news.ycombinator.com/item?id=19829435
https://www.javacodegeeks.com/2013/05/debuggers-are-for-lose...
I should consider myself lucky
I wish it generated a project with unit tests and told you which command to run (i.e. not an exercism command) in order to run those tests. That way you've got a more similar experience to what would happen if you cloned a project in that language and wanted to contribute.
Unfamiliar languages are much easier to deal with than unfamiliar tool chains IMO
The only times that I ever use Linux are in docker containers.
I've spent years writing java, python, php, and only occasionally need to use a Linux shell. When I do, Google and stack overflow are good enough to get the job done.
Sure, maybe in some domains it's incredibly useful, but to be a decent developer? Not at all.
My first language in uni was Java and we did start with the 'text editor + compile from cli' approach and 99% of people who were new to it literally don't get what or why they're typing obscure commands into the terminal.
Does that mean they have the same skill set as you, or can solve the same problems in the same ways? No. But that doesn't mean they aren't good developers.
*Where X is I some tech I myself have learnt.
Of course if you don't focus at all on the "first principles" operational stuff, when the existing system breaks, your debugging will take a long time. But that doesn't discount that loads of people are able to get a lot of work done without having a great grasp on the incidental complexity that comes with trying to get a virtual environment up or command line things.
Also Python has very good Windows support, it's not particularly "*nix affine".
If the purpose is simply to expose learners to the concept then perhaps the advanced sections are justified, but then there’s no reason we need to spend a full day on each of the data structures. One or two days is enough.
Further, the author goes into extensive detail about strings (which is great) but then says basically nothing about the more advanced topics.
This challenge may take more than100 days, follow your own pace.
Whoa, took a dark turn there
I wish the learning guides early on centered on builing an "engineered" application. Covering the approaches to structring the application, user input processing, error handling, logging, dependencies, packaging etc. Such that the learners won't be left alone with a simple knowledge of syntax and basic operations.
Well, likely there are such guides already.
That said, at some point it starts to make sense to read open source repositories to pick up useful patterns.
Is having to explain namespaces, public vs private, static and so on just to get the most basic Java possible program off the ground on mostly the same level of complexity? Or do you just glance over those initially, like a complete beginner would for type casting an input in Python?
It might be a good idea to introduce virtual environments earlier, as they help keep things from getting too snarled up dependency-wise.
In terms of modules, pathlib is very useful, if you're going to be collecting data from web scraping or similar then automating the process of creating directories and transferring files to them. Subprocess is another good one, for coordinating multiple activities in one script.
There’s a rather deep rabbit hole really about figuring out what the best beginner curriculum should look like, because if the diversity of the applicability of Python. They might be doing to a data science role, a sysadmin role, an infosec role, an educational role, you just never know. I think it’d be prohibitively difficult to unify these tracks, and might even play a role in the perceived difficulty for a lot of people coming to computer work from zero programming experience.
Someday, I’d like to see some sort of AI platform that can gauge a users intuition about the concepts on the whiteboard and tailor lessons to be impressed upon that user the best way. I’d teach a 30yo with ten years of Excel munging experience different than I’d teach a 20yo with no CS education at all besides video games. That could be automated to great benefit, imo.
However is is probably easier to get started with for someone who is already learning Python, because its data structures closely resemble Python data structures, the query language can be expressed directly in Python object literals. This makes MongoDB a very gentle introduction the basic usage of databases for storing data (as opposed to e.g. dumping JSON to a file).
Starting with Mongo might make it easier to graduate to relational databases & SQL later.
Arguably TinyDB might be a better choice than MongoDB, but at least Mongo does get used in industry once in a while.
So true.
I really like that 1 day is devoted to:
* PyPI
* virtual environments
Those feel super useful in all the roles you've listed.
I'm also not convinced that MongoDB is the most practical choice. On the other hand, it might be more intuitive to learn on 1 day than the relational database modelling. (Especially if we assume that the student has already learned about various data structures during week 1.)
That's an intriguing idea!!