Ask HN: What do you use for basic data analysis, visualization, and graphing?

[transitivebs]

I often find myself with some JSON data that I want to visualize. I usually end up converting to CSV, uploading to a Google Sheet, and manually creating charts. BUT this is really time consuming and I find Google Sheets charts pretty difficult and painful for quickly exploring different views of the original data.

So what do you use for this type of thing?

I know python has lots of good utils for data wrangling & graphing, but I'd prefer a solution which is: no-code, gives me a bunch of common graph views I can quickly choose between, and that "just works" 99% of the time.

Thanks!

[n8henrie]

Consider adding "no code" to the title somewhere to save a click for the hundreds of people that are planning to cheerfully suggest jupyter + pandas / matplotlib / Altair / seaborn / R / etc etc.

[transitivebs]

Good call; I realized that after the fact and unfortunately can't edit the title anymore.

[wanderingmind]

You can use superset[0]. Its a Flask app that can connect to databases, read csv, json and create good plots

[0] https://superset.apache.org/

[transitivebs]

Looks very promising.

I think I'm looking for the AI-powered equivalent of this that's one level of abstraction higher. Apache projects are obviously super high quality, but I want to offload the cognitive load of thinking about the graph specifics to an ML algo that "just works" for the majority of use cases (and is tweakable after the fact).

[flat-pluto]

I know you said wanted a no-code solution but in case you don't get a satisfactory answer try this out.

Earlier today there was a Show HN post[1] which showed how to visualize a Pandas dataframe (can come from CSV, JSON whatever). I tried it for basic tasks and it is pretty good. It's minimal code (<5 lines) - just reading the json and calling pygwalker in a Google Colab environment[2] or something. Something like this:

    import pandas as pd
    import pygwalker as pyg
    df = pd.read_json('{filename}.json')
    gwalker = pyg.walk(df)

Should be decent for most basic use-cases.

[1] - https://news.ycombinator.com/item?id=34869244

[2] - https://colab.research.google.com/

[ghiculescu]

If you can connect it to the data source, Metabase is exactly what you want. https://www.metabase.com/

[epgui]

Personally I find Metabase lacks flexibility, but then again I think that’s just the predicament you’re stuck with in no-code/low-code.

[ReDeiPirati]

This! As long as you can connect the data source you can go quite forward with this tool. It's probably the one that have the quickest learning curve.

[djbusby]

I love metabase. I'd also suggest to take a look at Apache Superset.

[transitivebs]

Metabase looks amazing. Thanks!!

[drewcoo]

The no code/low code poster child is Excel.

https://www.zdnet.com/article/office-excel-why-its-microsoft...

And there are many would-be Excels that do parts of what it does better.

Even if those contenders don't have a flight simulator yet.

https://excelunusual.com/a-first-flight-simulator-model-in-e...

[MilStdJunkie]

Not sure if this is "no code" exactly, but I use these from inside of Asciidoc every day, and I haven't seen them mentioned yet particularly. Asciidoc directives inside of a graph block are processed before the graph, so you can get conditional graphs in the output, or use the include directive to fetch outside graphics.

== Vega and Vega-Lite

Site:: https://vega.github.io/vega/

Sandbox:: https://vega.github.io/editor/#/examples/vega/airport-connec...

== PlantUML

Sandbox:: https://plantuml-editor.kkeisuke.dev/

Language Specs:: https://plantuml.com/sitemap-language-specification

[rvrst]

Marple[0] is a pretty awesome tool for quick visualization and browsing through data.

[0] https://www.marpledata.com/

[transitivebs]

Looks very promising.

They seem to imply it's only for time-series data, but I like their marketing & UX so far. So many of the projects people link to are probably awesome, but if you don't nail the UX / DX, people bounce really quickly.

Thanks!

[employee42]

Can't believe someone hasn't suggested Grafana[0] yet. It sounds perfect for your needs (although there is some coding required to make the queries).

[0] https://grafana.com/

[aeontech]

Datasette seems like it might be a good fit?

https://datasette.io/

[cldellow]

OP said they "find Google Sheets charts pretty difficult and painful for quickly exploring different views of the original data"

And that they want "a bunch of common graph views I can quickly choose between"

That doesn't sound like Datasette to me, although I'd be happy to be wrong -- how would you recommend someone achieve this in Datasette?

[transitivebs]

Checking it out now; thanks :)

[kungfufrog]

+1 for Datasette, love it!

[__mharrison__]

I use pandas. I'm pretty biased but I generally prefer to create things programmatically rather than drag and drop tooling. Especially if I need to do it in the future.

(I just made a course covering visualization w/ Pandas, Seaborn, Excel, Tableau, and a few others. My takeaway is that unless your data is good, you will need some preprocessing. Also, making good visualizations and tweaking them is difficult with code and no-code tooling. You need to figure out how to do the 20% of things (if you are even able to) in both code/no-code tools.)

[gooseyman]

What is the name of your new course? I’m struggling to determine if it’s effective pandas or one of the others.

[b_mc2]

If I'm not trying to build a very specific graph or chart, and just exploring data I usually use either Rawgraphs or Sqliteviz. Rawgraphs is nice if you just want to swap visualizations out with smaller data as is, sqliteviz seems to handle much larger datasets and let's you use SQL if you want to change the resultset. Both seem to keep data local too and I know sqliteviz works offline, rawgraphs might too.

https://www.rawgraphs.io/

https://sqliteviz.com/

[didgetmaster]

Here is a tool that will quickly load data from CSV, Json, or JsonLines formatted files using 'drag-and-drop' techniques. You can query it, analyze it, and display the information using a variety of charts.

https://www.youtube.com/watch?v=EHwBspzBDbs

Available for free download at https://www.Didgets.com

[ellisv]

I use R and ggplot2 for most plotting/visualization but I’d recommend something like Apache Superset if you want no-code (although setup is still required).

[Gatsky]

Well, there is this chap on twitter who makes amazing charts to do with equities and macroeconomics (eg https://twitter.com/TimmerFidelity/status/161865104862910873...). Turns out he just uses Excel. Maybe that's not the answer you are looking for.

[transitivebs]

Still proves how far you can go when you master your tools. Great example && thanks.

[shubhamjain]

Shameless plug: This is exactly the problem the I am trying to solve with my app - TextQuery [1]. Creating even a basic graph means dealing with multiple tools. I wanted to create a simple app where you can import all common types of data, run SQL over it, and visualize it quickly.

[1]: https://textquery.app/

[transitivebs]

Awesome; glad I helped validate the pain point && always happy to try and help fellow indie hackers.

btw adding a screenshot to the home page of the goal UX would help 1000x even if it's just a design mockup.

For my use case, I'd want to drag & drop a JSON file.

Thanks!

[iamcreasy]

You can write Trino rest client for a JSON endpoint that will allow you to query the endpoint using SQL and the result table can be push to any number of application for visualization.

[acomjean]

Rstudio is a pretty nice user interface to the R language. datasets can be browsed like spreadsheet tables. ggplot2 is a great graphing tool. Used in science a lot by non programmers.

there is an online book too thats pretty decent: R for data science

https://r4ds.had.co.nz/

[ies7]

Since you're already using Google sheet, fastest nocode dashboard available should be Google Data Studio.

[transitivebs]

Ahhh; didn't know about this. I guess it's called Looker Studio now? Will give it a try. Thanks!

[msadowski]

In the robotics world many people use PlotJuggler (https://plotjuggler.io/) that, among other things, sports csv files.

[geophph]

I might suggest pandas + plotly express. Not no code, and dependent on your data structure, but if you can form it into a tidy data frame plotly express will let you easily customize into different chart types and styles from there