I have data stored in a NumPy structured array where part of the information identifies various cases. I would like to find the row that matches a given case. E.g., let’s say I’m storing the name of a building, room number, and the number of chairs and tables in the room in a (2,) array. This would then look something like this:
import numpy as np my_dtype = [('building', '<U5'), ('room', '<i8'), ('seating', '<i8', (2,))] room_info = np.array([('BLDG0', 12, [24, 6]), ('BLDG1', 34, [32, 10]), ('BLDG0', 14, [10, 20])], dtype=my_dtype)
Now say that I want to find the row for building 'BLDG0'
, room 14
. Based on the answer to Finding a matching row in a numpy matrix, I tried
sub_fields = ['building', 'room'] matching_index, = np.where(room_info[sub_fields] == ('BLDG0', 14))
which would ideally result in [2]
. However, this results in the following warning:
FutureWarning: elementwise == comparison failed and returning scalar instead; this will raise an error or perform elementwise comparison in the future.
and returns an empty array. Is there a way to find the matching sub-row for a large set of data other than comparing each column separately and then finding the matching indices?
I am using NumPy version 1.18.5 through miniconda and it doesn’t look like I can safely update to a newer version within this environment. (Though I’m not sure if newer versions support this type of comparison)
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Answer
In [243]: my_dtype = [('building', '<U5'), ('room', '<i8'), ('seating', '<i8', ( ...: 2,))] ...: room_info = np.array([('BLDG0', 12, [24, 6]), ...: ('BLDG1', 34, [32, 10]), ...: ('BLDG0', 14, [10, 20])], ...: dtype=my_dtype) In [244]: room_info Out[244]: array([('BLDG0', 12, [24, 6]), ('BLDG1', 34, [32, 10]), ('BLDG0', 14, [10, 20])], dtype=[('building', '<U5'), ('room', '<i8'), ('seating', '<i8', (2,))]) In [246]: room_info['building'] Out[246]: array(['BLDG0', 'BLDG1', 'BLDG0'], dtype='<U5') In [247]: room_info['building']=='BLDG0' Out[247]: array([ True, False, True]) In [248]: room_info['room']==14 Out[248]: array([False, False, True])
combine the two:
In [249]: Out[247] & Out[248] Out[249]: array([False, False, True])
Use that as a boolean mask:
In [250]: room_info[_] Out[250]: array([('BLDG0', 14, [10, 20])], dtype=[('building', '<U5'), ('room', '<i8'), ('seating', '<i8', (2,))])
and getting the index:
In [251]: np.nonzero(Out[247]&Out[248]) Out[251]: (array([2]),)
Looks like we can test both fields, using a properly constructed structured array:
In [254]: test=np.array(('BLDG0',14),dtype=my_dtype[:2]) In [255]: room_info[['building','room']] Out[255]: array([('BLDG0', 12), ('BLDG1', 34), ('BLDG0', 14)], dtype={'names':['building','room'], 'formats':['<U5','<i8'], 'offsets':[0,20], 'itemsize':44}) In [256]: room_info[['building','room']]==test Out[256]: array([False, False, True])