.. _misc-rec_groupby_demo:

misc example code: rec_groupby_demo.py
======================================

[`source code <rec_groupby_demo.py>`_]

::

    import numpy as np
    import matplotlib.mlab as mlab
    import matplotlib.cbook as cbook
    
    datafile = cbook.get_sample_data('aapl.csv', asfileobj=False)
    print 'loading', datafile
    r = mlab.csv2rec(datafile)
    r.sort()
    
    def daily_return(prices):
        'an array of daily returns from price array'
        g = np.zeros_like(prices)
        g[1:] = (prices[1:]-prices[:-1])/prices[:-1]
        return g
    
    def volume_code(volume):
        'code the continuous volume data categorically'
        ind = np.searchsorted([1e5,1e6, 5e6,10e6, 1e7], volume)
        return ind
    
    # a list of (dtype_name, summary_function, output_dtype_name).
    # rec_summarize will call on each function on the indicated recarray
    # attribute, and the result assigned to output name in the return
    # record array.
    summaryfuncs = (
        ('date', lambda x: [thisdate.year for thisdate in x], 'years'),
        ('date', lambda x: [thisdate.month for thisdate in x], 'months'),
        ('date', lambda x: [thisdate.weekday() for thisdate in x], 'weekday'),
        ('adj_close', daily_return, 'dreturn'),
        ('volume', volume_code, 'volcode'),
        )
    
    rsum = mlab.rec_summarize(r, summaryfuncs)
    
    # stats is a list of (dtype_name, function, output_dtype_name).
    # rec_groupby will summarize the attribute identified by the
    # dtype_name over the groups in the groupby list, and assign the
    # result to the output_dtype_name
    stats = (
        ('dreturn', len, 'rcnt'),
        ('dreturn', np.mean, 'rmean'),
        ('dreturn', np.median, 'rmedian'),
        ('dreturn', np.std, 'rsigma'),
        )
    
    # you can summarize over a single variable, like years or months
    print 'summary by years'
    ry = mlab.rec_groupby(rsum, ('years',), stats)
    print mlab. rec2txt(ry)
    
    print 'summary by months'
    rm = mlab.rec_groupby(rsum, ('months',), stats)
    print mlab.rec2txt(rm)
    
    # or over multiple variables like years and months
    print 'summary by year and month'
    rym = mlab.rec_groupby(rsum, ('years','months'), stats)
    print mlab.rec2txt(rym)
    
    print 'summary by volume'
    rv = mlab.rec_groupby(rsum, ('volcode',), stats)
    print mlab.rec2txt(rv)
    

Keywords: python, matplotlib, pylab, example, codex (see :ref:`how-to-search-examples`)