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"