def pairs_plot(st1,st2,length):
adapter1 = stocks[st1][:length]
adapter2 = stocks[st2][:length]
dates =[tomilli(x) for x in adapter1['Date'][:]]
#plot the opening price of two stocks
p.figure()
p.plot(dates,adapter1['Open'][:],width=500, height=300,title=st1+' vs. '+ st2)
p.plot(dates,adapter2['Open'][:],width=500, height=300)
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)')
p.figure()
soccer_normed = soccer/float(soccer.max())
US_normed = US_Total_2009/float(US_Total_2009.max())+.2
p.hold('on')
p.plot(months,US_normed,width=500, height=300,color='red',title='Normalized Birthrates per Month<br/>Red: USA (2009 shifted .2), Blue: NBA')
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)')
p.figure()
nfl_normed = nfl/float(nfl.max())
# Shift by 0.2, to visually separate from the NFL plot
US_normed = US_Total_2009/float(US_Total_2009.max())+.2
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())
#plotting Candian Only Players NHL Births per Month
p.plot(months,canadaNHL,color='red')
print '\nCanada Birth Dates By Month'
for c in CanadaCount:
print c, CanadaCount[c]
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)')
mlb_shifted = np.concatenate((mlb[7:],mlb[:7]),axis=0)
p.figure()
plot_shift = p.plot(months,mlb_shifted,title='Totals Births per MLB Shifted')
p.figure()
mlb_normed = mlb/float(mlb.max())
from webplot import p
p.use_doc('webplot example')
import numpy as np
import datetime
import time
x = np.arange(100) / 6.0
y = np.sin(x)
z = np.cos(x)
data_source = p.make_source(idx=range(100), x=x, y=y, z=z)
p.plot(x, y, 'orange')
p.figure()
p.plot('x', 'y', color='blue', data_source=data_source, title='sincos')
p.plot('x', 'z', color='green')
p.figure()
p.plot('x', 'y', data_source=data_source)
p.figure()
p.plot('x', 'z', data_source=data_source)
p.figure()
p.table(data_source, ['x', 'y', 'z'])
p.scatter('x', 'y', data_source=data_source)
p.figure()
p.scatter('x', 'z', data_source=data_source)
p.figure()
p.hold(False)
p.scatter('x', 'y', 'orange', data_source=data_source)
p.scatter('x', 'z', 'red', data_source=data_source)
p.plot('x', 'z', 'yellow', data_source=data_source)
p.plot('x', 'y', 'black', data_source=data_source)
print "click on the plots tab to see results"
