numpy.fromiter using a custom generator and data type

gistfile1.py

import numpy
# Imagine these come from a db cursor or something
coordinates = [(1,2,3), (4,5,6), (7,8,9)]
def my_gen(some_tuple):
    for x, y, z in some_tuple:
        yield x, y, z

a = numpy.fromiter(my_gen(coordinates), dtype=[('x', 'l'), ('y', 'l'), ('z', 'l')])

"""

This creates a numpy record array, which looks like:

array([(1, 2, 3), (4, 5, 6), (7, 8, 9)], 
      dtype=[('x', '<i8'), ('y', '<i8'), ('z', '<i8')])

>>> a['x']
array([1, 4, 7])


.... Some timings:

python -m timeit -n1 -r100 "a = []" "for i in xrange(1000000):" "    a.append(i)"        
1 loops, best of 100: 93.1 msec per loop

python -m timeit -n1 -r100 -s "import numpy" "a = numpy.fromiter(xrange(1000000), dtype=numpy.int64)"
1 loops, best of 100: 74.3 msec per loop

python -m timeit -n1 -r100 -s "import numpy" "def gen():" "    for i in xrange(1000000):" "        yield i"  "l = []" "for i in gen():" "    l.append(i)"
1 loops, best of 100: 247 msec per loop

python -m timeit -n1 -r100 -s "import numpy" "def gen():" "    for i in xrange(1000000):" "        yield i"  "a = numpy.fromiter(gen(), dtype=numpy.int64)"
1 loops, best of 100: 123 msec per loop

"""