Sample two pandas dataframes the same way

Question

I'm doing a machine learning computations having two dataframes - one for factors and other one for target values. I have to split both into training and testing parts. It seems to me that I've found the way but I'm looking for more elegant solution. Here is my code:

import pandas as pd
import numpy as np
import random

df_source = pd.DataFrame(np.random.randn(5,2),index = range(0,10,2), columns=list('AB'))
df_target = pd.DataFrame(np.random.randn(5,2),index = range(0,10,2), columns=list('CD'))

rows = np.asarray(random.sample(range(0, len(df_source)), 2))

df_source_train = df_source.iloc[rows]
df_source_test = df_source[~df_source.index.isin(df_source_train.index)]
df_target_train = df_target.iloc[rows]
df_target_test = df_target[~df_target.index.isin(df_target_train.index)]

print('rows')
print(rows)
print('source')
print(df_source)
print('source train')
print(df_source_train)
print('source_test')
print(df_source_test)

---- edited - solution by unutbu (midified) ---

np.random.seed(2013)
percentile = .6
rows = np.random.binomial(1, percentile, size=len(df_source)).astype(bool)

df_source_train = df_source[rows]
df_source_test = df_source[~rows]
df_target_train = df_target[rows]
df_target_test = df_target[~rows]

Answer 1

Below you can find my solution, which doesn't involve any extra variables.

1. Use .sample method to get sample of your data
2. Use .index method on sample, to get indexes
3. Apply slice()ing by index for second dataframe

E.g. Let's say you have X and Y and you want to get 10 pieces sample on each. And it should be same samples, of course

X_sample = X.sample(10)
y_sample = y[X_sample.index]

Answer 2

I like the Alexander answer but I will add an index reset before sampling. The full code:

# index reset
X.reset_index(inplace=True, drop=True)
y.reset_index(inplace=True, drop=True)
# sampling
X_sample = X.sample(10)
y_sample = y[X_sample.index]

Reset of the index is used to not have problem with matching.