用pandas和numpy解析冒号分隔的稀疏数据 [英] Parsing colon separated sparse data with pandas and numpy

问题描述

我想用pandas/numpy中的col_index:value格式解析数据文件.例如:

I would like to parse data file with the format col_index:value in pandas/numpy. For example:

0:23 3:41 1:31 2:65

0:23 3:41 1:31 2:65

对应于此矩阵:

[[23 0 0 41] [0 31 65 0]]

[[23 0 0 41] [0 31 65 0]]

这似乎是在文件中表示稀疏数据的一种非常普遍的方法，但是我找不到一种简便的方法来解析此数据，而不必在调用read_csv之后进行某种迭代.

It seems like a pretty common way to represent sparse data in a file, but I can't find an easy way to parse this without having to do some sort of iteration after calling read_csv.

推荐答案

我最近发现这实际上是svm-light格式，您可以使用svm loader这样的数据读取数据集:

I found out recently that this is in fact svm-light format and you may be able to read a dataset like this using an svm loader like:

http://scikit-learn.org/stable/modules/Generated/sklearn.datasets.load_svmlight_file.html

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