# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in 

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory

from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8"))
directory = '../input/'
train = pd.read_csv(directory + 'train.csv')
test = pd.read_csv(directory + 'test.csv')
numeric_feats = [x for x in train.columns[1:-1] if 'cont' in x]
categorical_feats = [x for x in train.columns[1:-1] if 'cat' in x]
print(train)
# Any results you write to the current directory are saved as output.