Structure Of Python Numpy Arrays

I would like to ask a completely new question regarding this code. The code in the link above returns a numpy array for open and close: open = np.array([q.open for q in quotes]).a

Solution 1:

quotes is a list which contains stock information per symbol:

In[43]: len(quotes)
Out[43]: 61In[44]: len(symbols)
Out[44]: 61In[45]: symbolsOut[45]: 
array(['COP', 'AXP', 'RTN', 'BA', 'AAPL', 'PEP', 'NAV', 'GSK', 'MSFT',
       'KMB', 'R', 'SAP', 'GS', 'CL', 'WAG', 'WMT', 'GE', 'SNE', 'PFE',
       'AMZN', 'MAR', 'NVS', 'KO', 'MMM', 'CMCSA', 'SNY', 'IBM', 'CVX',
       'WFC', 'DD', 'CVS', 'TOT', 'CAT', 'CAJ', 'BAC', 'AIG', 'TWX', 'HD',
       'TXN', 'KFT', 'VLO', 'NWS', 'F', 'CVC', 'TM', 'PG', 'LMT', 'K',
       'HMC', 'GD', 'HPQ', 'DELL', 'MTU', 'XRX', 'YHOO', 'XOM', 'JPM',
       'MCD', 'CSCO', 'NOC', 'UN'], 
      dtype='|S17')

For example the first element in quotes is for the 'COP' symbol and contains an array of values by date:

In [49]: symbols[0]
Out[49]: 'COP'In [50]: quotes[0].openOut[50]: 
array([ 13.81001419,  14.01678947,  14.01500099, ...,  56.77238579,
        56.82699428,  56.89080408])

In [51]: quotes[0].dateOut[51]: 
array([2003-01-02, 2003-01-03, 2003-01-06, ..., 2007-12-27, 2007-12-28,
       2007-12-31], dtype=object)