def group_indices(self):
if self._group_indices is None:
axis = self._group_axis
self._group_indices = tseries.groupby_indices(axis,
self.grouper)
return self._group_indices
def bench_groupby():
N = 200
arr = np.arange(10000).astype(object)
values = np.random.randn(10000)
keys = arr // 10
d = dict(zip(arr, keys))
f = lambda: groupby_nocython(arr, d.get)
print 'no cython: %.2f ms per iteration' % (_timeit(f, n=N) * 1000)
f = lambda: tseries.arrmap(arr, d.get)
timing = _timeit(f, n=N) * 1000
print 'arrmap: %.2f ms per iteration' % timing
f = lambda: isnull(tseries.arrmap(arr, d.get))
print 'isnull: %.2f ms per iteration' % (_timeit(f, n=N) * 1000 - timing)
f = lambda: tseries.groupby(arr, d.get)
print 'groupby: %.2f ms per iteration' % (_timeit(f, n=N) * 1000)
f = lambda: tseries.groupby_indices(arr, d.get)
print 'groupby_inds: %.2f ms per iteration' % (_timeit(f, n=N) * 1000)
def _test():
groups = tseries.groupby_indices(arr, d.get)
result = {}
for k, v in groups.iteritems():
result[k] = np.mean(values.take(v))
return result
print 'test: %.2f ms per iteration' % (_timeit(_test, n=N) * 1000)
def bench_groupby():
N = 200
arr = np.arange(10000).astype(object)
values = np.random.randn(10000)
keys = arr // 10
d = dict(zip(arr, keys))
f = lambda: groupby_nocython(arr, d.get)
print 'no cython: %.2f ms per iteration' % (_timeit(f, n=N) * 1000)
f = lambda: tseries.arrmap(arr, d.get)
timing = _timeit(f, n=N) * 1000
print 'arrmap: %.2f ms per iteration' % timing
f = lambda: isnull(tseries.arrmap(arr, d.get))
print 'isnull: %.2f ms per iteration' % (_timeit(f, n=N) * 1000 - timing)
f = lambda: tseries.groupby(arr, d.get)
print 'groupby: %.2f ms per iteration' % (_timeit(f, n=N) * 1000)
f = lambda: tseries.groupby_indices(arr, d.get)
print 'groupby_inds: %.2f ms per iteration' % (_timeit(f, n=N) * 1000)
def _test():
groups = tseries.groupby_indices(arr, d.get)
result = {}
for k, v in groups.iteritems():
result[k] = np.mean(values.take(v))
return result
print 'test: %.2f ms per iteration' % (_timeit(_test, n=N) * 1000)
def _test():
groups = tseries.groupby_indices(arr, d.get)
result = {}
for k, v in groups.iteritems():
result[k] = np.mean(values.take(v))
return result
def _test():
groups = tseries.groupby_indices(arr, d.get)
result = {}
for k, v in groups.iteritems():
result[k] = np.mean(values.take(v))
return result