df = pandas.read_parquet(‘foo.parquet’)
  df.to_csv(‘foo.csv’)
  df.to_json(‘foo.json’)

FWIW, in my experience at a "data analytics platform" company, it's reasonably popular for data-heavy workflows since Parquet is well-defined, and file sizes (especially as the amount of data grows) are a fraction of their CSV equivalents.

> Is it a limitation of the format itself?

I don't think so. In other languages, you can generally read/write Parquet files without a ton of dependencies (e.g. https://github.com/xitongsys/parquet-go).

This is the opposite of my experience.

> To read a parquet file in Python, you need Apache Arrow and Pandas.

Or DuckDB.

    import duckdb
    df = duckdb.query("select * from 'a.parquet'")

    vd a.parquet

Do you consider Pandas a "data science" stack? To me, it's just a library like any other that makes it easy to work with tabular data. Even for CSV, there is csvreader (usually not a good idea to deal with CSV by hand). Outputting to CSV is literally a one liner in Pandas or DuckDB.

   import pandas as pd

   # output to CSV
   pd.read_parquet("a.parquet").to_csv("a.csv") 

   # output to JSON (choose from any number of orientations)
   pd.read_parquet("a.parquet").to_json(orient="table")

   # read rows
   for row in pd.read_parquet("a.parquet").itertuples():
       print(row)