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I have a numpy array df for which I calculate 10 quantiles using this:

np.quantile(df, np.arange(0.1, 1.1, 0.1))

which gives:

array([0.73518751, 0.73774966, 0.7415471 , 0.74462405, 0.74640229, 0.74952848, 0.7522163 , 0.75820953, 0.76289489, 0.76954299])

But I also wanted to know the indices of these 10 values (or nearest values). Basically, which values reflect the quantiles the closest in the original array.

How can I do this?

df

array([[0.74472344, 0.72122723, 0.75860745, 0.76498497, 0.70747197, 0.74813282, 0.74042159, 0.74722695, 0.73554462, 0.75064671, 0.74331897, 0.74716657, 0.73778194, 0.74615586, 0.76285201, 0.75964642, 0.73241562, 0.75377691, 0.75396305, 0.75909799, 0.73139387, 0.74406022, 0.7466802 , 0.74904978, 0.76954299, 0.74861521, 0.73639923, 0.72030777, 0.76361781, 0.74158204, 0.75146145, 0.7445026 , 0.75811005, 0.72869432, 0.74437159, 0.73829693, 0.73757732, 0.75386381, 0.75234902, 0.76638651, 0.75146788, 0.74095088, 0.72755104, 0.74487853, 0.74637753, 0.73871291, 0.76512724, 0.75878656, 0.76340133, 0.75192058, 0.74568671, 0.75875962, 0.74529624, 0.73688561, 0.75444639, 0.74146557, 0.71298224, 0.76272094, 0.73547828, 0.73585916, 0.73830914, 0.7449885 , 0.74602336, 0.75998384, 0.7620157 , 0.75025225, 0.75024652, 0.74897128, 0.736911 , 0.75200063, 0.75215942, 0.75381088, 0.74398333, 0.74050236, 0.75152248, 0.7435388 , 0.75403506, 0.76490963, 0.73895139, 0.74835008, 0.76352328, 0.74488378, 0.75088626, 0.74712956, 0.76071763, 0.75406158, 0.73689425, 0.73673624, 0.76328081, 0.73762053, 0.74111873, 0.74642706, 0.76343733, 0.74263775, 0.73242599, 0.74470502, 0.74260616, 0.74389631, 0.75280219, 0.73257053]])
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    Possible duplicate of How do I get the index of a specific percentile in numpy / scipy? Commented Nov 27, 2018 at 22:29
  • @G.Anderson Both the answers deal with finding the index of a particular quantile, not a range of quantiles. Commented Nov 27, 2018 at 22:33
  • @G.Anderson Can you please explain the duplicate tag? I have my answer, but I would like an explanation. Commented Nov 27, 2018 at 22:46
  • I found another question that might provide either an answer to a similar problem, or a solution that maybe could be adapted and flagged it both to see if admins thought it was a duplicate, and if you might find it helpful Commented Nov 27, 2018 at 22:55

1 Answer 1

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You np.abs() and argmin():

quantiles = np.quantile(df, np.arange(0.1, 1.1, 0.1)) [(np.abs(df - i)).argmin() for i in quantiles]

Returns:

[58, 12, 29, 95, 44, 23, 70, 32, 14, 24]
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Thank you for an actual answer!

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