How to construct a numpy array with its each element be the minimum value of all possible values?

Question

I want to construct a 1d numpy array a, and I know each a[i] has several possible values. Of course, the numbers of the possible values of any two elements of a can be different. For each a[i], I want to set it be the minimum value of all the possible values.

For example, I have two array:

idx = np.array([0, 1, 0, 2, 3, 3, 3])
val = np.array([0.1, 0.5, 0.2, 0.6, 0.2, 0.1, 0.3])

The array I want to construct is following:

a = np.array([0.1, 0.5, 0.6, 0.1])

So does there exist any function in numpy can finish this work?

Divakar · Accepted Answer · 2017-07-05 14:16:14Z

1

Here's one approach -

def groupby_minimum(idx, val):
    sidx = idx.argsort()
    sorted_idx = idx[sidx]
    cut_idx = np.r_[0,np.flatnonzero(sorted_idx[1:] != sorted_idx[:-1])+1]
    return np.minimum.reduceat(val[sidx], cut_idx)

Sample run -

In [36]: idx = np.array([0, 1, 0, 2, 3, 3, 3])
    ...: val = np.array([0.1, 0.5, 0.2, 0.6, 0.2, 0.1, 0.3])
    ...: 

In [37]: groupby_minimum(idx, val)
Out[37]: array([ 0.1,  0.5,  0.6,  0.1])

Here's another using pandas -

import pandas as pd

def pandas_groupby_minimum(idx, val):
    df = pd.DataFrame({'ID' : idx, 'val' : val})
    return df.groupby('ID')['val'].min().values

Sample run -

In [66]: pandas_groupby_minimum(idx, val)
Out[66]: array([ 0.1,  0.5,  0.6,  0.1])

edited Jul 5, 2017 at 14:16

answered Jul 5, 2017 at 14:06

Divakar

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Comments

jan · Accepted Answer · 2017-07-05 22:51:46Z

0

You can also use binned_statistic:

from scipy.stats import binned_statistic

idx_list=np.append(np.unique(idx),np.max(idx)+1)
stats=binned_statistic(idx,val,statistic='min', bins=idx_list)
a=stats.statistic

I think, in older scipy versions, statistic='min' was not implemented, but you can use statistic=np.min instead. Intervals are half open in binned_statistic, so this implementation is safe.

answered Jul 5, 2017 at 22:51

jan

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