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)')