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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def symbols_bollinger(symbols='sp5002012', start=datetime.datetime(2008, 1, 1), end=datetime.datetime(2009, 12, 31), price_type='adjusted_close', cleaner=clean_dataframe, window=20, sigma=1.):
 """Calculate the Bolinger for a list or set of symbols Example: GOOG AAPL IBM MSFT 2010-12-23 16:00:00 1.298178 1.185009 1.177220 1.237684 2010-12-27 16:00:00 1.073603 1.371298 0.590403 0.932911 2010-12-28 16:00:00 0.745548 1.436278 0.863406 0.812844 2010-12-29 16:00:00 0.874885 1.464894 2.096242 0.752602 2010-12-30 16:00:00 0.634661 0.793493 1.959324 0.498395 """

    symbols = normalize_symbols(symbols)
    prices = price_dataframe(symbols, start=start, end=end, price_type=price_type, cleaner=cleaner)
    return frame_bollinger(prices, window=window, sigma=sigma, plot=False)