# Convert data types 
df.Date = df.Date.astype('datetime64')
df.StandardDate = df.StandardDate.astype('datetime64')
df.DateSK = df.DateSK.astype('int')
df.Day = df.Day.astype('int')
df.DOWInMonth = df.DOWInMonth.astype('int')
df.DayOfYear = df.DayOfYear.astype('int')
df.WeekOfYear = df.WeekOfYear.astype('int')
df.WeekOfMonth = df.WeekOfMonth.astype('int')
df.Month = df.Month.astype('int')
df.Quarter = df.Quarter.astype('int')
df.Year = df.Year.astype('int')

print 'Data Types'
print df.dtypes


#From Excel to DataFrame
from pandas import DataFrame, ExcelFile
import pandas as pd
import json


# Path to excel file
# Your path will be different, please modify the path below.
location = r'//covenas/decisionsupport/meinzer/projects/network/Contract Deliverables for Emanio Reports.xls'
Beispiel #2
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						elif strat == 'sell':	
							for k in range(len(df)):	
							#''' Short-only strategy '''
								strat_rsi_bb_crossover_2(k, df.Close, buy_symbol, _hard_stop ,avg_trueRange, valid_trading_period, \
									rsi_eventArray, rsi_event_id, rsi_action, posSize, posPrc, dynamicPNL, realizedPNL)		
					else:		
						for k in range(len(df)):
							#''' Long-Short strategy '''					
							strat_rsi_bb_crossover_3(k, df.Close, buy_symbol, _hard_stop ,avg_trueRange, valid_trading_period, \
								rsi_eventArray, rsi_event_id, rsi_action, posSize, posPrc, dynamicPNL, realizedPNL)
					''' Generate year summary ''' 
					yearSummary = DataFrame(index = np.arange(len(df)), columns=['TradeID', 'Year', 'Symbol', 'Strat', \
						'StartTrading', 'EndTrading', 'Date', 'DateTime', 'Close', 'IsPeriod', 'Action', \
									'PosSize', 'PosDir', 'PosPrc', 'DynamicPNL', 'RealizedPNL'])
					yearSummary.TradeID = tradeID
					yearSummary.Year = year
					yearSummary.Symbol = symbol
					yearSummary.Strat = strat
					#yearSummary.Buy = MRCI.ix[i, 'Buy'] + " " + MRCI.ix[i, 'b_Month'] + str(y_buy)
					#yearSummary.Sell = MRCI.ix[i, 'Sell'] + " " + MRCI.ix[i, 's_Month'] + str(y_sell)
					yearSummary.StartTrading = datetime_toString(start_trading_date)
					yearSummary.EndTrading = datetime_toString(end_trading_date)	
					yearSummary.Date = df.Date
					yearSummary.DateTime = dt
					#yearSummary.DateCode = t
					yearSummary.Close = df.Close
					yearSummary.IsPeriod = seasonal_trading_period
					yearSummary.Action = rsi_action
					yearSummary.PosSize = abs(posSize)
					yearSummary.PosDir = posSize
					yearSummary.PosPrc = posPrc
					dum1 = line[0].split('<td>')[1]
					alldata[colheads[jj]][salescnt] = dum1


				salescnt += 1

			if colheads[jj] == 'Album':
				albumURLs.append(tdata[i+jj+1].split()[1][6:-1])


alldata['Album URL'] = albumURLs


musicinfo = DataFrame(alldata)

musicinfo.Year = musicinfo.Year.astype(float)
musicinfo['Sales (millions)'] = musicinfo['Sales (millions)'].astype(float)

# plt.hist(musicinfo.Year, bins=range(1988,2014))
# # plt.scatter(musicinfo.Year, musicinfo['Sales (millions)'])
# plt.title("Number of Albums on RIAA\nList of Top Hip Hop Albums")
# plt.ylabel('# Albums')
# plt.xlabel("Year")
# plt.minorticks_on()
# plt.show()


#################################################################
## Now, we have our Album URLs.  Let's get the album pages...
#################################################################
fout = open('./albums/albumlist.txt','w')