datasette VS q

Simon Willison's github would be a great place to get started imo -

https://github.com/simonw/datasette

I'm a fan on simonw's datasette/dogsheep ecosystem https://datasette.io/

I use Anki the exact same way. After a lifetime of learning I have accepted that I will never read over anything I write for myself voluntarily - so my two options are:

1. Write an article so good I can publish it and look it over myself later on. I did this last year with https://andrew-quinn.me/fzf/, for example.

2. Create Anki cards out of the material. Use the builtin Card Browser or even https://datasette.io/ on the underlying SQLite database in a pinch to search for my notes any time I have to.

Were you aware of, or tempted by https://datasette.io/ for creating your solution?

Browsing HN, GitHub and the like we get to see a huge variety of software products and code bases.

I often see products and think - if this product X, got together with Y, it would be pretty cool - kind of like if they had a kid together.

Not too literally, but more on the conceptual level - my level of programming is low.

E.g. Just some....

- pocketable.io & datasette (+with some more charting) [https://pocketbase.io, https://datasette.io]

You might like the Datasette project: https://datasette.io/

I don't think they are desperate for contributions but it's a welcoming environment and a fun project to hack on. You'll learn a lot just from reading the source and the incredibly informative PRs. The creator is a really talented developer with a great blog which shows up on the HN front page often.

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.

> uploads of boring raw excel data and receive a nice UI

https://datasette.io/

CSV utilities (still haven't pick a favorite one...): https://github.com/harelba/q https://github.com/BurntSushi/xsv https://github.com/wireservice/csvkit https://github.com/johnkerl/miller

I think they're talking about https://github.com/harelba/q, which is not very fast.

Apparently, there were many who thought the same thing; Tools to execute SQL against CSV were trdsql, q, csvq, TextQL. They were highly functional, hoewver, had many options and no input completion. I found it just a little difficult to use.

Hi, author of q here.

Regarding the error you got, q currently does not autodetect headers, so you'd need to add -H as a flag in order to use the "country" column name. You're absolutely correct on failing-fast here - It's a bug which i'll fix.

In general regarding speed - q supports automatic caching of the CSV files (through the "-C readwrite" flag). Once it's activated, it will write the data into another file (with a .qsql extension), and will use it automatically in further queries in order to speed things considerably.

Effectively, the .qsql files are regular sqlite3 files (with some metadata), and q can be used to query them directly (or any regular sqlite3 file), including the ability to seamlessly join between multiple sqlite3 files.

http://harelba.github.io/q/#auto-caching-examples