Random row selection in Pandas dataframe

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.

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])