from?matplotlib.finance?import?quotes_historical_yahoo_ochl from?datetime?import?date import?pandas?as?pd today?=?date.today() start?=?(today.year-2,?today.month,?today.dat) quotes?=?quotes_historical_yahoo_ochl('MSFT',?start,?today) attributes?=?['date','open','close','high','low','volume'] quotesdf?=?pd.DataFrame(quotes,?columns?=?attributes) list1?=?[] for?i?in?range(0,?len(quotes)): ????x?=?date.fromordinal(int(quotes[i][0])) ????y?=?date.strftime(x,?'%Y/%m/%d') list1.append(y) quotesdf.index?=?list1 quotesdf?=?quotesdf.drop(['date'],?axis?=?1?) quotesdf.ix['14/01/30':'14/02/10',['open',?'close']] quotesdf['14/01/01':'15/01/01'].sort('close',?ascending=False)[0:5] list1?=?[] tmpdf?=?quotesdf['15/01/01':'15/12/31'] for?i?in?range(0,?len(tmpdf)): ????list1.append(int(tmpdf.index[i][3:5])) tmpdf['month']?=?list1 print?tmpdf[?tmpdf.close?>?tmpdf.open]['month'].value_counts()