pandas :对DataFrame进行采样 [英] Pandas: Sampling a DataFrame

问题描述

我正在尝试使用Pandas读取一个相当大的CSV文件，并将其分成两个随机的块，其中一个占数据的10％，另一个占90％.

I'm trying to read a fairly large CSV file with Pandas and split it up into two random chunks, one of which being 10% of the data and the other being 90%.

这是我目前的尝试:

rows = data.index
row_count = len(rows)
random.shuffle(list(rows))

data.reindex(rows)

training_data = data[row_count // 10:]
testing_data = data[:row_count // 10]

由于某些原因，当我尝试在SVM分类器中使用这些结果DataFrame对象之一时，sklearn引发此错误:

For some reason, sklearn throws this error when I try to use one of these resulting DataFrame objects inside of a SVM classifier:

IndexError: each subindex must be either a slice, an integer, Ellipsis, or newaxis

我认为我做错了.有更好的方法吗?

I think I'm doing it wrong. Is there a better way to do this?

推荐答案

您使用的是哪个版本的熊猫?对我来说，您的代码工作正常(我在git master上使用).

What version of pandas are you using? For me your code works fine (i`m on git master).

另一种方法可能是:

In [117]: import pandas

In [118]: import random

In [119]: df = pandas.DataFrame(np.random.randn(100, 4), columns=list('ABCD'))

In [120]: rows = random.sample(df.index, 10)

In [121]: df_10 = df.ix[rows]

In [122]: df_90 = df.drop(rows)

较新的版本(从0.16.1开始)直接支持此功能: http://pandas.pydata.org/pandas-docs /stable/generation/pandas.DataFrame.sample.html

Newer version (from 0.16.1 on) supports this directly: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sample.html

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