大 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

从熊猫文档中，我收集到了这些独特价值指数使得某些操作有效，并且偶尔会容忍非唯一指数。

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

来自外部，它看起来不像任何方式利用非独特的指数。例如，以下 ix 查询速度足够慢，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内容 - 这只是一个调用 ix 的示例唯一索引显得慢得多）

(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)

有没有办法让pandas在非唯一和/或排序索引上使用更快的查找方法，如二元搜索？

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index是唯一的时，pandas使用哈希表将键映射到值O（1）。当索引是非唯一且已排序时，pandas使用二进制搜索O（logN），当索引是随机排序时，pandas需要检查索引O（N）中的所有键。

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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