# 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"))

# Any results you write to the current directory are saved as output.

train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/sample_submission_zero.csv')
transactions = pd.read_csv('../input/transactions.csv',nrows=1000)
user_logs = pd.read_csv('../input/user_logs.csv',nrows=1000)
members = pd.read_csv('../input/members.csv')

print(train.shape)
print(test.shape)
print(transactions.shape)
print(user_logs.shape)
print(members.shape)