Random row selection in Pandas dataframe

Is there a way to select random rows from a DataFrame in Pandas.

In R, using the car package, there is a useful function some(x, n) which is similar to head but selects, in this example, 10 rows at random from x.

I have also looked at the slicing documentation and there seems to be nothing equivalent.

Update

Now using version 20. There is a sample method.

df.sample(n)

With pandas version 0.16.1 and up, there is now a DataFrame.sample method built-in:

import pandas

df = pandas.DataFrame(pandas.np.random.random(100))

# Randomly sample 70% of your dataframe
df_percent = df.sample(frac=0.7)

# Randomly sample 7 elements from your dataframe
df_elements = df.sample(n=7)

For either approach above, you can get the rest of the rows by doing:

df_rest = df.loc[~df.index.isin(df_percent.index)]

Per Pedram's comment, if you would like to get reproducible samples, pass the random_state parameter.

df_percent = df.sample(frac=0.7, random_state=42)

Something like this?

import random

def some(x, n):
    return x.ix[random.sample(x.index, n)]

Note: As of Pandas v0.20.0, ix has been deprecated in favour of loc for label based indexing.

sample

As of v0.20.0, you can use pd.DataFrame.sample, which can be used to return a random sample of a fixed number rows, or a percentage of rows:

df = df.sample(n=k)     # k rows
df = df.sample(frac=k)  # int(len(df.index) * k) rows

For reproducibility, you can specify an integer random_state, equivalent to using np.ramdom.seed. So, instead of setting, for example, np.random.seed = 0, you can:

df = df.sample(n=k, random_state=0)

The best way to do this is with the sample function from the random module,

import numpy as np
import pandas as pd
from random import sample

# given data frame df

# create random index
rindex =  np.array(sample(xrange(len(df)), 10))

# get 10 random rows from df
dfr = df.ix[rindex]

Below line will randomly select n number of rows out of the total existing row numbers from the dataframe df without replacement.

df = df.take(np.random.permutation(len(df))[:n])

Actually this will give you repeated indices np.random.random_integers(0, len(df), N) where N is a large number.