text_plain_output_1.png
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

allteams_2010 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2009-2010.csv')
allteams_2011 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2010-2011.csv')
allteams_2012 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2011-2012.csv')
allteams_2013 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2012-2013.csv')
allteams_2014 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2013-2014.csv')
allteams_2015 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2014-2015.csv')
allteams_2016 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2015-2016.csv')
allteams_2017 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2016-2017.csv')
allteams_2018 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2017-2018.csv')
allteams_2019 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2018-2019.csv')
allteams_2021 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2020-2021.csv')

allteams_list = [allteams_2010, allteams_2011, allteams_2012, allteams_2013, allteams_2014, allteams_2015, allteams_2016, allteams_2017, allteams_2018, allteams_2019, allteams_2021]

teamstats_2010 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2010 Team Stats.csv', skiprows=[0])
teamstats_2011 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2011 Team Stats.csv', skiprows=[0])
teamstats_2012 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2012 Team Stats.csv', skiprows=[0])
teamstats_2013 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2013 Team Stats.csv', skiprows=[0])
teamstats_2014 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2014 Team Stats.csv', skiprows=[0])
teamstats_2015 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2015 Team Stats.csv', skiprows=[0])
teamstats_2016 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2016 Team Stats.csv', skiprows=[0])
teamstats_2017 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2017 Team Stats.csv', skiprows=[0])
teamstats_2018 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2018 Team Stats.csv', skiprows=[0])
teamstats_2019 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2019 Team Stats.csv', skiprows=[0])
teamstats_2021 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2021 Team Stats.csv', skiprows=[0])

teamstats_list = [teamstats_2010, teamstats_2011, teamstats_2012, teamstats_2013, teamstats_2014, teamstats_2015, teamstats_2016, teamstats_2017, teamstats_2018, teamstats_2019, teamstats_2021]

i = 0
for df in teamstats_list:
    df.School = df.School.str.rstrip()
    year_val = 2010 + i
    years = [year_val] * df.shape[0]
    df['YEAR'] = years
    i += 1

year_val = 2021
years = [year_val] * df.shape[0]
teamstats_2021['YEAR'] = [year_val] * teamstats_2021.shape[0]

teamstats_2010_2021 = pd.concat(teamstats_list)

i = 0
for df in allteams_list:
    df[['W', 'L']] = df['W-L'].str.split('-', expand=True)
    df.drop(['W-L'], inplace=True, axis=1)
    year_val = 2010 + i
    years = [year_val] * df.shape[0]
    df['YEAR'] = years
    i += 1

allteams_2010_2021 = pd.concat(allteams_list)

schoolname_list1 = teamstats_2010_2021.School.unique()
schoolname_list1.sort()

schoolname_list2 = allteams_2010_2021.TEAM.unique()
schoolname_list2.sort()

different_names_list = []
for i in range(len(schoolname_list1)):
    if schoolname_list1[i] not in schoolname_list2:
        different_names_list.append(schoolname_list1[i])

corrected_name_list = ['UAB', 'Albany', 'Bowling Green', 'BYU', 'CSU Bakersfield', 'CSU Fullerton', 'Long Beach State', 'CSU Northridge', 'Centenary', 'Central Connecticut', 'UCF', 'The Citadel', 'Charleston', 'UConn', "Hawai'i", 'UIC', 'LSU', 'UL Monroe', 'Loyola Chicago', 'UMBC', 'UMass', 'UMass Lowell', 'McNeese', 'Miami', 'Ole Miss', 'UM Kansas City', 'Morgan St', 'UNLV', 'Nicholls', 'Norfolk St', 'NC State', 'UNC Asheville', 'UNC Greensboro', 'UNC Wilmington', 'Prairie View A&M', 'Purdue Fort Wayne', 'St. Francis (PA)', "Saint Mary's", 'San José St', 'Seattle U', 'SE Louisiana', 'USC', 'SMU', 'Southern Miss', 'St. Francis (BKN)', "St. John's", 'Tarleton', 'UT Martin', 'Texas A&M-CC', 'TCU', 'UT Arlington', 'UTEP', 'UT Rio Grande Valley', 'UTSA', 'UC Davis', 'UC Irvine', 'UC Riverside', 'UC San Diego', 'UC Santa Barbara', 'California', 'VCU', 'Winston Salem']
for i in range(len(corrected_name_list)):
    print(different_names_list[i], 'vs. ', corrected_name_list[i])