pandas 中的多列分解 [英] multi-column factorize in pandas

问题描述

pandas factorize函数将系列中的每个唯一值分配给基于0的顺序索引，并计算每个系列条目所属的索引.

The pandas factorize function assigns each unique value in a series to a sequential, 0-based index, and calculates which index each series entry belongs to.

我想在多列上完成pandas.factorize的等效功能:

I'd like to accomplish the equivalent of pandas.factorize on multiple columns:

import pandas as pd
df = pd.DataFrame({'x': [1, 1, 2, 2, 1, 1], 'y':[1, 2, 2, 2, 2, 1]})
pd.factorize(df)[0] # would like [0, 1, 2, 2, 1, 0]

也就是说，我要确定数据帧的几列中的每个值的唯一元组，为每个值分配一个顺序索引，并计算数据帧中的每一行属于哪个索引.

That is, I want to determine each unique tuple of values in several columns of a data frame, assign a sequential index to each, and compute which index each row in the data frame belongs to.

Factorize仅适用于单列.熊猫中有多列等效功能吗?

Factorize only works on single columns. Is there a multi-column equivalent function in pandas?

推荐答案

您需要先创建一个元组的ndarray，pandas.lib.fast_zip可以在cython循环中非常快速地完成此操作.

You need to create a ndarray of tuple first, pandas.lib.fast_zip can do this very fast in cython loop.

import pandas as pd
df = pd.DataFrame({'x': [1, 1, 2, 2, 1, 1], 'y':[1, 2, 2, 2, 2, 1]})
print pd.factorize(pd.lib.fast_zip([df.x, df.y]))[0]

输出为:

[0 1 2 2 1 0]

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