Indexing and selecting stock data

I just started with quantopian and I can wrap my head around indexing stock prices. I know how indexing works, [0] = beginning/start while [-1] would reference the last piece of data in the list/df or what ever

please someone shed some light on this, my brain if fried

In this block of code the current stock price is referenced as [-1] and the previous day is as [-2]

def initialize(context):  
    # AAPL, MSFT, and SPY  
    context.securities = [sid(24), sid(5061), sid(8554)]

def handle_data(context, data):  
    price_history = data.history(context.securities, "price", bar_count=2, frequency="1d")  
    for s in context.securities:  
        prev_bar = price_history[s][-2]  
        curr_bar = price_history[s][-1]  
        if curr_bar > prev_bar:  
            order(s, 20)

In this block of code todays New Price is referenced as [-1] while the Old price is referenced as [0]

def initialize(context):  
    # AAPL, MSFT, and SPY  
    context.securities = [sid(24), sid(5061), sid(8554)]

def handle_data(context, data):  
    prices = data.history(context.securities, "price", bar_count=10, frequency="1d")  
    pct_change = (prices.ix[-1] - prices.ix[0]) / prices.ix[0]  
    log.info(pct_change)

then again using the iloc, where I would guess the New Price is [0] and the Old Price is [-1]

def initialize(context):  
    # AAPL, MSFT, and SPY  
    context.securities = [sid(24), sid(5061), sid(8554)]

def handle_data(context, data):  
    price_history = data.history(context.securities, "price", bar_count=10, frequency="1d")  
    pct_change = price_history.iloc[[0, -1]].pct_change()  
    log.info(pct_change)

Open High Low Close Volume Date [0] or [-10] 2016-11-08 110.309998 111.720001 109.699997 111.059998 24054500.0 [1] or [-9] 2016-11-09 109.879997 111.320000 108.050003 110.879997 58430900.0 [2] or [-8] 2016-11-10 111.089996 111.089996 105.830002 107.790001 57123700.0 [3] or [-7] 2016-11-11 107.120003 108.870003 106.550003 108.430000 34094100.0 [4] or [-6] 2016-11-14 107.709999 107.809998 104.080002 105.709999 50872500.0 [5] or [-5] 2016-11-15 106.570000 107.680000 106.160004 107.110001 32216900.0 [6] or [-4] 2016-11-16 106.699997 110.230003 106.599998 109.989998 58702500.0 [7] or [-3] 2016-11-17 109.809998 110.349998 108.830002 109.949997 26964600.0 [8] or [-2] 2016-11-18 109.720001 110.540001 109.660004 110.059998 27404300.0 [9] or [-1] 2016-11-21 110.120003 111.989998 110.010002 111.730003 29119100.0