python - Pandas: Sampling a DataFrame

Question

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%.

Here's my current attempt:

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]

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

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

Incidentally, this wouldn't randomly shuffle correctly anyway - the problem is random.shuffle(list(rows)). shuffle alters the data it operates on, but when you call list(rows), you make a copy of rows that gets altered and then thrown away - the underlying pandas Series, rows, is unchanged. One solution is to call rows = list(rows), then random.shuffle(rows) and data.reindex(rows) after that. — spencer nelson

Wouter Overmeire Wouter Overmeire · Accepted Answer · 2012-08-30T07:36:18

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

Another approach could be:

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)

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

python - Pandas: Sampling a DataFrame

5 Answers