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I don't know where this myth originated from but I have seen this in multiple places. Even if you think about it 2d numpy arrays can't have different type for d
by YetAnotherNick 3y ago
I don't know where this myth originated from but I have seen this in multiple places. Even if you think about it 2d numpy arrays can't have different type for different columns.
- theLiminator 3y agoActually numpy has something called a structured array that is pretty much what what you described.
- billyjmc 3y agoWell, if you use structured arrays or record arrays, you can do this (more or less). https://numpy.org/doc/stable/user/basics.rec.html https://numpy.org/doc/stable/user/basics.rec.html
- deleted 3y ago[deleted]
- FrustratedMonky 3y ago"myth originated" It's in the documentation. I just learned pandas recently, and I would have said this same thing. Not because I read through the numpy code, but because I read the documentation. Is it wrong? Can't a user pick up a new tool and trust some documentation without reading through 100's of libraries built on libraries. When was last time someone traced out every dependency, so they can confidently say something is "Myth".
- YetAnotherNick 3y agoWhere is it written in pandas documentation? Pandas dataframe is stored in list of 1d numpy arrays, not a single 2d array.
- ayhanfuat 3y agoThe columns with common dtypes are grouped together in something called "blocks" and inside those blocks are 2D numpy arrays. It is probably not in the documentation because it is seen as implementation detail but you can see the block manager's structure in this article (https://dkharazi.github.io/blog/blockmanager/ https://dkharazi.github.io/blog/blockmanager/) or in this talk (https://thomasjpfan.github.io/scipy-2020-lightning-talk-pandas-blockmanager https://thomasjpfan.github.io/scipy-2020-lightning-talk-pand...).