<jupyter_start><jupyter_text>______ LOOKUP______ Gets Player by Part of Name returns Player ID'active_swsport_codesearch_player_allsearch_player_all.col_in=player_id**PARTIAL NAME SEARCH**http://lookup-service-prod.mlb.com/json/named.search_player_all.bam?sport_code='mlb'&active_sw='Y'&name_part='young%'&search_player_all.col_in=player_id**PARTIAL NAME SEARCH excluding the results player id**http://lookup-service-prod.mlb.com/json/named.search_player_all.bam?sport_code='mlb'&active_sw='Y'&name_part='young%'&search_player_all.col_ex=player_id**DIVISION STANDINGS/TEAM INFO**```def get_division_standings(league, division)-> pd:    standings = pd.DataFrame( statsapi.standings_data(league)[division]['teams'])            standings = standings[['name', 'div_rank', 'gb', 'w', 'l',  'elim_num', 'wc_rank', 'wc_gb', 'wc_elim_num']]     sorting  in pandas    reverse_standings = standings.sort_values('l')    print(reverse_standings)     df.describe()       df.info()     print stastistical summary of table in pandas     print(standings.describe())    return standings    return revserse_standingsget_division_standings(103,201)pprint(get_division_standings(103,201))      AL EASTpprint(get_division_standings(103,202))      AL CENTRALpprint(get_division_standings(103,200))      AL WESTpprint(get_division_standings(104,203))      NL WESTpprint(get_division_standings(104,204))      NL EASTpprint(get_division_standings(104,205))      NL CENTRAL```<jupyter_code># USING LEAGUE LEADERS
rookie_hr_leaders = statsapi.league_leaders('homeRuns', season=2022, playerPool = 'rookies', limit=15)
rookie_hr_leaders = rookie_hr_leaders.split('\n') 
print(type(rookie_hr_leaders))
print(rookie_hr_leaders)
# DICT RETURN FUNCTIONS<jupyter_output><empty_output>
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