Pandas panelnd 与具有分层索引的数据框 [英] Pandas panelnd vs dataframe with hierarchical index

问题描述

我想知道何时以及为什么我应该更喜欢面板(nd)而不是具有分层索引的数据框，反之亦然.在我非常简短的经验中，我会说前者更便于切片，而后者用于数学运算.我的特别需要是通过方便的切片和元素操作以交互方式操作 3-5 维面板.

I was wondering when and why I should prefer a panel(nd) over a dataframe with hierarchical index, and vice versa. In my very brief experience, I would say that the former is more convenient for slicing, while the latter for mathematical operations. My particular need would be to interactively manipulate 3-5 dimensional panels with convenient slicing and element-wise operations.

谢谢，

贾科莫

推荐答案

通常坚持使用多索引框架，因为它们得到更全面的支持.

Generally stick with a multi-indexed frame as they are more fully supported.

A panelnd 就像一个广义的 n-dim Panel，主要适用于单一类型的数据.它确实像面板一样工作，但有一些怪癖和缺失的功能(这就是为什么它是实验性的).

A panelnd is like a generalized n-dim Panel, good mainly for single-dtyped data. It does work like a Panel, but has some quirks and missing features (its why its experimental).

他们的是将一些操作应用于n-dim的多个slab的方法(特别是通过0.13.1中的新apply，参见此处.

Their are ways to apply some operations to multiple slabs of a n-dim (esp. via new apply in 0.13.1, see here.

一旦我达到 3 维以上，我主要是保存"数据并切片以在 2 维中工作，然后在需要时重新组装它.对于这些更高亮度的对象(例如，通过 HDFStore)，存储也很方便，这也是它们最初被创建的原因.

Once I get to more than 3 dimensions, I mainly 'hold' the data and slice to work it in 2 dimensions, then reassemble it if needed. Storage can also be convient for these higher dim objects (e.g. via HDFStore), and was the reason they were created in the first place.

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