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from subprocess import check_output

import IPython

import matplotlib #collection of functions for scientific and publication-ready visualization

import numpy as np #foundational package for scientific computing

import pandas as pd #collection of functions for data processing and analysis modeled after R dataframes with SQL like features

import scipy as sp #collection of functions for scientific computing and advance mathematics

import sklearn #collection of machine learning algorithms

import sys #access to system parameters https://docs.python.org/3/library/sys.html

import warnings

import sys
print('Python version: {}'.format(sys.version))
import pandas as pd
print('pandas version: {}'.format(pd.__version__))
import matplotlib
print('matplotlib version: {}'.format(matplotlib.__version__))
import numpy as np
print('NumPy version: {}'.format(np.__version__))
import scipy as sp
print('SciPy version: {}'.format(sp.__version__))
import IPython
from IPython import display
print('IPython version: {}'.format(IPython.__version__))
import sklearn
print('scikit-learn version: {}'.format(sklearn.__version__))
import timeit
import random
import time
import warnings
warnings.filterwarnings('ignore')
print('-' * 25)
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))