Weighted percentile using numpy

Question

Is there a way to use the numpy.percentile function to compute weighted percentile? Or is anyone aware of an alternative python function to compute weighted percentile?

thanks!

Answer 1

Completely vectorized numpy solution

Here is the code I use. It's not an optimal one (which I'm unable to write with numpy), but still much faster and more reliable than accepted solution

def weighted_quantile(values, quantiles, sample_weight=None,                        values_sorted=False, old_style=False):     """ Very close to numpy.percentile, but supports weights.     NOTE: quantiles should be in [0, 1]!     :param values: numpy.array with data     :param quantiles: array-like with many quantiles needed     :param sample_weight: array-like of the same length as `array`     :param values_sorted: bool, if True, then will avoid sorting of         initial array     :param old_style: if True, will correct output to be consistent         with numpy.percentile.     :return: numpy.array with computed quantiles.     """     values = np.array(values)     quantiles = np.array(quantiles)     if sample_weight is None:         sample_weight = np.ones(len(values))     sample_weight = np.array(sample_weight)     assert np.all(quantiles >= 0) and np.all(quantiles <= 1), \         'quantiles should be in [0, 1]'      if not values_sorted:         sorter = np.argsort(values)         values = values[sorter]         sample_weight = sample_weight[sorter]      weighted_quantiles = np.cumsum(sample_weight) - 0.5 * sample_weight     if old_style:         # To be convenient with numpy.percentile         weighted_quantiles -= weighted_quantiles[0]         weighted_quantiles /= weighted_quantiles[-1]     else:         weighted_quantiles /= np.sum(sample_weight)     return np.interp(quantiles, weighted_quantiles, values) 

Examples:

weighted_quantile([1, 2, 9, 3.2, 4], [0.0, 0.5, 1.])

array([ 1. , 3.2, 9. ])

weighted_quantile([1, 2, 9, 3.2, 4], [0.0, 0.5, 1.], sample_weight=[2, 1, 2, 4, 1])

array([ 1. , 3.2, 9. ])