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ashwal
Some good tips in here, I've find myself reaching for JSON/excel methods often.

Despite using it for years, I still haven't decided if pandas is poorly architected or if the clunkiness (for lack of better of term) is a result of the inherent difficulty of the tasks.

hansvm
I seem to remember the author awhile back writing about how it was their first major project and there were a ton of things they'd learned and would like to change.
bushbaba
I wish I knew about json_normalize sooner. JSON is great, but needing to transform it to a CSV/Excel sheet is a toil. Great to know about this one-liner!!
lettergram
I spent some time working on something called DataProfiler python library

https://github.com/capitalone/DataProfiler

The gist is that you can point to any common dataset and load it directly into pandas.

from dataprofiler import Data

data = Data("your_file.csv") # Auto-Detect & Load: CSV, AVRO, Parquet, JSON, Text, URL

I simply hate dealing with loading data, so it's my go-to.

wenc
I’ve mostly replaced pd.read_csv and pd.read_parquet with duckdb.query(“select * from ‘x.csv’) or duckdb.query(“select * from ‘y/*.parquet’).

It’s much faster because DuckDB is vectorized. The result is a Pandas dataframe.

Querying the Pandas dataframe from DuckDB is faster than querying it with Pandas itself.

pineapplejuice
My tip is to keep a dict of all the fields and the data types you expect them to be, particularly strings. In my company we have IDs that start with zeros, or are a mix of numbers and letters, and get interpreted as numeric types. I'm frequently pulling data out of the DW with the same fields, so I just have to use the dtype= arg point it to my dict and it takes care of that for me.