[mgradowski]

Also, you will sleep better at night knowing that your column dtypes are safe from harm, exactly as you stored them. Moving from CSV (or god forbid, .xlsx) has been such a quality of life improvement.

One thing I miss though is how easy it is to inspect .csv and .xlsx. I kinda solved it using [1], but it only works on Windows. More portable recommendations welcome!

[1] https://github.com/mukunku/ParquetViewer

[ZeroGravitas]

I really like Visidata for "exploring" csv-type data.

Its a vi(m) inspired tool.

It also handles xls(x), sqlitedb and a bunch of other random things, and it appears to support parquet via pandas:

https://www.visidata.org/docs/loading/

[mgradowski]

Nice one!

[speedgoose]

I used to use Zeppelin, some kind of Jupyter Notebook for Spark (that supports Parquet). But it may be better alternatives.

https://zeppelin.apache.org/

[cb321]

The "real" format being binary with debugging tools is absolutely the best way to go. For example, you can use `nio print` (or even just `nio p`) in the Nim multi-program https://github.com/c-blake/nio to get "text debugging output" of binary files.