here is my dilemma, I've taken a collection of stocks from get_fundamentals and pulled apart the sid and prices, but after I recombine them I have not figured out a way to remove only the sid value to parse into the ordering function. could somebody please provide me with some insight? (also, this is fairly rough code as I haven't tried to streamline it. I welcome any thoughts relating to the code's flow as well. )

Thanks!

def before_trading_start(context, data):

    #get a list of securities sorted by market cap  
    items = get_fundamentals(  
        query(fundamentals.valuation.market_cap,  
              )  
        .filter(fundamentals.valuation.market_cap != None)  
        .filter(fundamentals.company_reference.primary_exchange_id != "OTCPK") # no pink sheets  
#        .filter(fundamentals.asset_classification.morningstar_sector_code == 309) # Energy sector  
        .filter(fundamentals.share_class_reference.security_type == 'ST00000001') # common stock only  
        .filter(~fundamentals.share_class_reference.symbol.contains('_WI')) # drop when-issued  
        .filter(fundamentals.share_class_reference.is_primary_share == True) # remove ancillary classes  
        .filter(fundamentals.share_class_reference.is_depositary_receipt == False) # !ADR/GDR  
        .filter(fundamentals.valuation.market_cap > 1e8)  
        .order_by(fundamentals.valuation.market_cap.desc())  
        .limit(list_length)  
        )  
#    context.stocks = data[context.stocks]  
#    print 'data'  
#    print context.stocks  
    #Pull out the sids from the list of securities returned by get_fundamentals  
    column_labels = [key for key in dict(items.dtypes) if dict(items.dtypes)[key] in ['float64', 'int64']]  
#    print 'column_labels'  
#    print len(column_labels)  
    #pulling out the sid values.  
    num = len(column_labels)  
    sid_hold = []  
    j=0  
    for n in range(num):  
         j = column_labels[n].sid; sid_hold.append(j)

    #transfer the extracted sid values into context.relations  
    context.relations = []  
    context.relations = sid_hold  
    # Updates the universe to include the securities returned by get_fundamentals  
    update_universe(column_labels)  


def handle_data(context, data):  

    # Get historical data for the S&P tracking SPY ETF to stand in for the index  
    exchanges = history(10, "1d", "price")[context.spy]

    # Get historical data for the stocks returned by get_fundamentals  
    prices = history(10, "1d", "open_price")  
     # get correlation between securities from prices and indices from exchanges  
    nop = []  
    j=0  
    for i in range(prices.shape[1]):  
             j = prices.icol(i).corr(exchanges); nop.append(j)  


# Beginning of set up to make data good to use  
    numLF = len(context.relations)  
    numRF = len(nop)  
    left_frame = pd.DataFrame({'key': range(numLF),  
                               'sid': context.relations})  
    right_frame = pd.DataFrame({'key': range(numRF),  
                               'cor': nop})     

    temp = pd.merge(left_frame, right_frame, on='key', how='outer')



    neg_corr = []  
    pos_corr = []  
    temp_sid_neg = []  
    temp_sid_pos = []  
    j = 0  
    y = 0  
    for q in range(len(temp)):  
        if temp.cor[q] < 0:  
            j = temp.cor[q]; neg_corr.append(j); j = temp.sid[q]; temp_sid_neg.append(j)  
        elif temp.cor[q] > 0:  
            y = temp.cor[q]; pos_corr.append(y); y = temp.sid[q]; temp_sid_pos.append(y)  
    lsn = len(temp_sid_neg)  
    lcn = len(neg_corr)  
    lsp = len(temp_sid_pos)  
    lcp = len(pos_corr)


    TempSidNeg = pd.DataFrame({'key': range(lsn),  
                               'sid': temp_sid_neg})  
    TempCorNeg = pd.DataFrame({'key': range(lcn),  
                               'cor': neg_corr})  
    TempSidPos = pd.DataFrame({'key': range(lsp),  
                               'sid': temp_sid_pos})  
    TempCorPos = pd.DataFrame({'key': range(lcp),  
                               'cor': pos_corr})

    neg = pd.merge(TempSidNeg, TempCorNeg, on='key', how='outer')  
    pos = pd.merge(TempSidPos, TempCorPos, on='key', how='outer')  
    #sort list by the lowest to the higest correlation  
    context.holdingneg = neg.sort(['cor'], ascending=[True]).head(NumPositions)  
    context.holdingpos = pos.sort(['cor'], ascending=[False]).head(NumPositions)

#can't get sid values out to parse into order function