#!/usr/bin/env python
# coding: utf-8

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# 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.


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cwur = pd.read_csv('../input/cwurData.csv')
#education_expenditure_supplementary_data = pd.read_csv('../input/education_expenditure_supplementary_data.csv')
educational_attainment_supplementary_data = pd.read_csv('../input/educational_attainment_supplementary_data.csv')
school_and_country_table = pd.read_csv('../input/school_and_country_table.csv')
shanghaiData = pd.read_csv('../input/shanghaiData.csv')
timesData = pd.read_csv('../input/timesData.csv')


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cwur.head(2)


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educational_attainment_supplementary_data.head(2)


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shanghaiData.head(2)


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timesData.head(2)


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