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Comment by unixhero | Hacker News Reader

What makes Pandas so bad and what makes Dplyr so great?

I have used Pandas a lot for data analysis and for data integration duct tape scenarios. For me it has been a low bar for achieving a lot.

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If you use Pandas daily, maybe get used to it and can ignore the issues, but for anyone using Pandas occasionally, it's every time a huge pain trying to figure out how to use it. The API is not intuitive and the documentation is very verbose and unclear. And stackoverflow top answers are often the "old way" of doing something when yet another way of doing the same thing has been added to the API.

For some people pandas seems to click. Good for you. I always struggle with google and the manual to get even simple things done.

I can never figure out if I am gonna get a series or a data frame out of an operation. It seems to edit rows when I think it’ll edit columns and I constantly have to explicitly reset the index not to get into problems.

I think dplyr is easy to read and write. It does get longer than other alternatives, but the readability is imho so good at it doesn’t feel verbose.

it's just so bloated and verbose. many ways to do the same things, annoying defaults (how is column not the default axis to drop?), indices are beyond frustrating (have never met anyone who doesn't just reset them after a groupby), inconvenient to do custom aggregations, very slow, not opinionated enough

then there are the inherent python issues like dates and times, poor support for nonstandard evaluation, handling mixed data types and nulls

I could never use Pandas without SO and the documentation and I use it for almost 10 years.

I have no idea what is the intention of the developers most of the time.

Aha, so you're productive right?

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