Pandas: Sampling a DataFrame


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)


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?

8/30/2012 6:12:46 AM

Accepted Answer

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:

6/19/2015 6:17:16 AM

I have found that np.random.choice() new in NumPy 1.7.0 works quite well for this.

For example you can pass the index values from a DataFrame and and the integer 10 to select 10 random uniformly sampled rows.

rows = np.random.choice(df.index.values, 10)
sampled_df = df.ix[rows]

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