issue_comments: 156915727

Yes, I'm afraid this is a known issue. Grouped aggregations are currently implemented with a loop in pure Python, which, of course, is pretty slow.

I've done some exploratory work to rewrite them in Numba, which shows some encouraging preliminary results:

``` from numba import guvectorize, jit import pandas as pd import numpy as np

@guvectorize(['(float64[:], int64[:], float64[:])'], '(x),(x),(y)', nopython=True) def _grouped_mean(values, int_labels, target): count = np.zeros(len(target), np.int64) for i in range(len(values)): val = values[i] if not np.isnan(val): lab = int_labels[i] target[lab] += val count[lab] += 1 target /= count

def move_axis_to_end(array, axis): array = np.asarray(array) return np.rollaxis(array, axis, start=array.ndim)

def grouped_mean(values, by, axis=-1): int_labels, uniques = pd.factorize(by, sort=True) values = move_axis_to_end(values, axis) target = np.zeros(values.shape[:-1] + uniques.shape) _grouped_mean(values, int_labels, target) return target, uniques

values = np.random.RandomState(0).rand(int(1e6)) values[::50] = np.nan by = np.random.randint(50, size=int(1e6)) df = pd.DataFrame({'x': values, 'y': by})

np.testing.assert_allclose(grouped_mean(values, by)[0], df.groupby('y')['x'].mean())

%timeit grouped_mean(values, by) # 100 loops, best of 3: 15.3 ms per loop %timeit df.groupby('y').mean() # 10 loops, best of 3: 21.4 ms per loop ```

Unfortunately, I'm unlikely to have time to work on this in the near future. If you or anyone else is interested in taking the lead on this, it would be greatly appreciated!

Note that we can't reuse the routines from pandas because they are only designed for 1D or at most 2D data.