from webplot import p p.use_doc('uefa') from collections import Counter import numpy as np from wakaridata.sportstats import SportStats sports = SportStats() uefa_data = sports[sports.keys()[4]][:] dob_months = [int(dob.split('-')[1]) for dob in uefa_data['DOB']] mCount = Counter(dob_months) for c in mCount: print c, mCount[c] months = mCount.keys() soccer = np.array(mCount.values()) p.plot(months,soccer,width=500, height=300,title='Number of Births per Month for UEFA 2012') #http://www.cdc.gov/nchs/data/nvsr/nvsr60/nvsr60_01_tables.pdf#I02 #birth rates per month across USA 2009 US_Total_2009 = np.array([337980,316641,347803,337272,345257,346971,368450,359554\ ,361922,347625,320195,340995],dtype='float64') p.figure() p.plot(months,US_Total_2009,width=500, height=300,title='Totals Births per Month 2009 (USA)')
from webplot import p p.use_doc('nhl_analysis') from collections import Counter import numpy as np from wakaridata.sportstats import SportStats sports = SportStats() nfl_data = sports[sports.keys()[2]][:] dob_months = [int(dob.split('-')[1]) for dob in nfl_data['DOB']] mCount = Counter(dob_months) for c in mCount: print c, mCount[c] months = mCount.keys() nfl = np.array(mCount.values()) p.plot(months,nfl,title='Number of Births per Month for NFL') #http://www.cdc.gov/nchs/data/nvsr/nvsr60/nvsr60_01_tables.pdf#I02 #birth rates per month across USA 2009 US_Total_2009 = np.array([337980,316641,347803,337272,345257,346971, 368450,359554,361922,347625,320195,340995]) p.figure() p.plot(months,US_Total_2009,title='Totals Births per Month 2009 (USA)')
from webplot import p p.use_doc('mlb_analysis') from collections import Counter import numpy as np from wakaridata.sportstats import SportStats sports = SportStats() mlb_data = sports[sports.keys()[0]][:] dob_months = [int(dob.split('-')[1]) for dob in mlb_data['DOB']] mCount = Counter(dob_months) for c in mCount: print c, mCount[c] months = mCount.keys() mlb = np.array(mCount.values()) p.plot(months,mlb,title='Number of Births per Month for MLB') #http://www.cdc.gov/nchs/data/nvsr/nvsr60/nvsr60_01_tables.pdf#I02 #birth rates per month across USA 2009 US_Total_2009 = np.array([337980,316641,347803,337272,345257,346971,368450,359554 \ ,361922,347625,320195,340995],dtype='float64') p.figure() p.plot(months,US_Total_2009,title='Totals Births per Month 2009 (USA)')
from webplot import p from collections import Counter import numpy as np from wakaridata.sportstats import SportStats sports = SportStats() nhl_data = sports[sports.keys()[1]][:] dob_months = [int(dob.split('-')[1]) for dob in nhl_data['DOB']] mCount = Counter(dob_months) print 'All Hockey Players Birth Dates By Month' for c in mCount: print c, mCount[c] months = mCount.keys() NHL = np.array(mCount.values()) #plotting NHL Births per Month p.plot(months,NHL,title='Number of Births per Month for NHL<br/>Blue: Total NHL, Red: NHL (Canada)') canada = nhl_data[nhl_data['Nationality']=='Canada'] CanadaMonths = [int(dob.split('-')[1]) for dob in canada['DOB']] CanadaCount = Counter(CanadaMonths) canadaNHL = np.array(CanadaCount.values())
from webplot import p p.use_doc('nba_analysis') from collections import Counter import numpy as np from wakaridata.sportstats import SportStats sports = SportStats() nba_data = sports[sports.keys()[3]][:] months = [int(dob.split('-')[1]) for dob in nba_data['DOB']] mCount = Counter(months) for c in mCount: print c, mCount[c] months = mCount.keys() nba = np.array(mCount.values()) p.plot(months,nba,title='Number of Births per Month for nba') #http://www.cdc.gov/nchs/data/nvsr/nvsr60/nvsr60_01_tables.pdf#I02 #birth rates per month across USA 2009 US_Total_2009 = np.array([337980,316641,347803,337272,345257,346971,368450,359554\ ,361922,347625,320195,340995],dtype='float64') p.figure() p.plot(months,US_Total_2009,title='Totals Births per Month 2009 (USA)')