Pandas 中非唯一索引的性能影响是什么? [英] What is the performance impact of non-unique indexes in pandas?

问题描述

从 Pandas 文档中，我收集到唯一值索引可以提高某些操作的效率，并且偶尔会容忍非唯一索引.

From the pandas documentation, I've gathered that unique-valued indices make certain operations efficient, and that non-unique indices are occasionally tolerated.

从外面看，似乎没有以任何方式利用非唯一索引.例如，下面的 ix 查询足够慢，它似乎正在扫描整个数据帧

From the outside, it doesn't look like non-unique indices are taken advantage of in any way. For example, the following ix query is slow enough that it seems to be scanning the entire dataframe

In [23]: import numpy as np
In [24]: import pandas as pd
In [25]: x = np.random.randint(0, 10**7, 10**7)
In [26]: df1 = pd.DataFrame({'x':x})
In [27]: df2 = df1.set_index('x', drop=False)
In [28]: %timeit df2.ix[0]
1 loops, best of 3: 402 ms per loop
In [29]: %timeit df1.ix[0]
10000 loops, best of 3: 123 us per loop

(我意识到两个 ix 查询不会返回相同的东西——这只是一个例子，在非唯一索引上调用 ix 显得慢得多)

(I realize the two ix queries don't return the same thing -- it's just an example that calls to ix on a non-unique index appear much slower)

有什么办法可以让 Pandas 使用更快的查找方法，比如对非唯一和/或排序索引进行二分搜索?

Is there any way to coax pandas into using faster lookup methods like binary search on non-unique and/or sorted indices?

推荐答案

当索引唯一时，pandas 使用哈希表将键映射到值 O(1).当索引不唯一且已排序时，pandas 使用二分查找 O(logN)，当索引是随机排序时，pandas 需要检查索引中的所有键 O(N).

When index is unique, pandas use a hashtable to map key to value O(1). When index is non-unique and sorted, pandas use binary search O(logN), when index is random ordered pandas need to check all the keys in the index O(N).

你可以调用sort_index方法:

import numpy as np
import pandas as pd
x = np.random.randint(0, 200, 10**6)
df1 = pd.DataFrame({'x':x})
df2 = df1.set_index('x', drop=False)
df3 = df2.sort_index()
%timeit df1.loc[100]
%timeit df2.loc[100]
%timeit df3.loc[100]

结果:

10000 loops, best of 3: 71.2 µs per loop
10 loops, best of 3: 38.9 ms per loop
10000 loops, best of 3: 134 µs per loop

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