pandas 多索引数据帧的 lexsort_depth 究竟是什么？
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What exactly is the lexsort_depth of a multi-index Dataframe?
提问by Amelio Vazquez-Reina
What exactly is the lexsort_depthof a multi-index dataframe? Why does it have to be sorted for indexing?
lexsort_depth多索引数据框究竟是什么？为什么必须对索引进行排序？
For example, I have noticed that, after manually building a multi-index dataframe dfwith columns organized in three levels, if I try to do:
例如，我注意到，在手动构建一个多索引数据框后df，列组织成三个级别，如果我尝试这样做：
idx = pd.IndexSlice
df[idx['foo', 'bar']]
I get:
我得到：
KeyError: 'Key length (2) was greater than MultiIndex lexsort depth (0)'
and at this point, df.columns.lexsort_depthis 0
在这一点上，df.columns.lexsort_depth是0
However,if I do, as recommended hereand here:
df = df.sortlevel(0,axis=1)
then the cross-section indexing works. Why? What exactly is lexsort_depth, and why sorting with sortlevelfixes this type of indexing?
然后横截面索引起作用。为什么？究竟是什么lexsort_depth，为什么用排序来sortlevel修复这种类型的索引？
采纳答案by Jeff
lexsort_depthis the number of levels of a multi-index that are sorted lexically. That is, in an a-b-c-1-2-3 order (normal sort order).
lexsort_depth是按词法排序的多索引的级别数。即，按 abc-1-2-3 顺序（正常排序顺序）。
So element indexing willwork if a multi-index is not sorted, but the lookups may be quite a bit slower (in 0.15.2, this will show a PerformanceWarningfor doing these kinds of lookups, see here
因此，如果未对多索引进行排序，则元素索引将起作用，但查找可能会慢一些（在 0.15.2 中，这将显示PerformanceWarning用于执行此类查找，请参见此处
The reason that sorting in general a good idea is that pandas is able to use hash-based indexing to figure out where the location is in a particular level independently for the level. ; then you can use these indexers to find the final locations.
排序通常是一个好主意的原因是，pandas 能够使用基于哈希的索引来独立地找出特定级别中的位置。; 然后您可以使用这些索引器来查找最终位置。
Pandas takes advantage of np.searchsortedto find these locations when its sorted. If its not sorted, then you have to fallback to some different (slower) methods.
Pandas 会np.searchsorted在排序时利用这些位置。如果它没有排序，那么你必须回退到一些不同的（较慢的）方法。
hereis the code that does this.
