You can use DataFrameGroupBy.sample method as follwing:

sample_df = df.groupby("target").sample(n=5000, random_state=1)

Here's a rather long winded answer that has a lot of flexibility and uses some random data I generated. I also added a field to the dataframe to denote whether that row had been used.

Generating Data

import pandas as pd
from string import ascii_lowercase
import random

random.seed(44)

files = [ascii_lowercase[i] for i in range(4)]
value_1 = random.sample(range(1, 10), 8)

files_df = files*len(value_1)
value_1_df = value_1*len(files)
value_1_df.sort()
value_2_df = random.sample(range(100, 200), len(files_df))

df = pd.DataFrame({'file' : files_df,
                 'value_1': value_1_df,
                 'value_2': value_2_df,
                  'used': 0})

Randomly Selecting Files

len_to_run = 3 #change to run for however long you'd like
batch_to_pull = 4
updated_files = df.loc[df.used==0,'file'].unique()

for i in range(len_to_run): #not needed if you only want to run once
    file_to_pull = ''.join(random.sample(updated_files, 1))
    print 'file ' + file_to_pull
    for j in range(batch_to_pull): #pulling 4 values
        updated_value_1 = df.loc[(df.used==0) & (df.file==file_to_pull),'value_1'].unique()
        value_1_to_pull = random.sample(updated_value_1,1)
        print 'value_1 ' + str(value_1_to_pull)
        df.loc[(df.file == file_to_pull) & (df.value_1==value_1_to_pull),'used']=1

file a
value_1 [1]
value_1 [7]
value_1 [5]
value_1 [4]
file d
value_1 [3]
value_1 [2]
value_1 [1]
value_1 [5]
file d
value_1 [7]
value_1 [4]
value_1 [6]
value_1 [9]

from sklearn.model_selection import StratifiedShuffleSplit

n_splits = 1 
sss = model_selection.StratifiedShuffleSplit(n_splits=n_splits, 
                                                 test_size=0.1,
                                                 random_state=42)
train_idx, test_idx = list(sss.split(X, y))[0]