@yera_ee Each way has its advantages.
With dataclasses, you get nice attribute access, error checking, a name for the aggregate data, and a more restrictive equality test. All good things.
Dicts are at the core of the language and are interoperable with many other tools: json, **kw, …
With dataclasses, you get nice attribute access, error checking, a name for the aggregate data, and a more restrictive equality test. All good things.
Dicts are at the core of the language and are interoperable with many other tools: json, **kw, …
@yera_ee Dicts have a rich assortment of methods and operators.
People learn to use dicts on their first day.
Many existing tools accept or return dicts.
pprint() knows how to handle dicts.
Dicts are super fast.
JSON.
Dicts underlie many other tools.
People learn to use dicts on their first day.
Many existing tools accept or return dicts.
pprint() knows how to handle dicts.
Dicts are super fast.
JSON.
Dicts underlie many other tools.
@yera_ee Embrace dataclasses but don't develop an aversion to dicts.
Python is a very dict centric language.
Mentally rejecting dicts would be like developing an allergy to the language itself. It leads to fighting the language rather than working in harmony with it.
Python is a very dict centric language.
Mentally rejecting dicts would be like developing an allergy to the language itself. It leads to fighting the language rather than working in harmony with it.
@yera_ee Only in Python does this happen.
In Lisp, you don't hear about people feeling icky about using lists.
In Matlab, people aren't averse to using matrices.
In Pandas, people don't feel bad about using data frames.
In Lisp, you don't hear about people feeling icky about using lists.
In Matlab, people aren't averse to using matrices.
In Pandas, people don't feel bad about using data frames.
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