python - Replace values of a numpy array by values from another numpy array -

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i have 1000 * 1000 numpy array 1 million values created follows : 

>>import numpy np >>data = np.loadtxt('space_data.txt') >> print (data) >>[[ 13.  15.  15. ...,  15.  15.  16.]    [ 14.  13.  14. ...,  13.  15.  16.]    [ 16.  13.  13. ...,  13.  15.  17.]    ...,     [ 14.   15.  14. ...,  14.  14.  13.]    [ 15.   15.  16. ...,  16.  15.  14.]    [ 14.   13.  16. ...,  16.  16.  16.]]

i have numpy array which has 2 columns follows:

>> print(key) >>[[ 10.,   s],    [ 11.,   s],    [ 12.,   s],    [ 13.,   m],    [ 14.,   l],    [ 15.,   s],    [ 16.,   s],    ...,    [ 92.,   xl],    [ 93.,   m],    [ 94.,   xl],    [ 95.,   s]]

what want replace each element of of data array corresponding element in second column of key array this..

>> print(data) >>[[ m  s  s ...,  s  s  s]    [ l   m  l ...,  m  s  s]    [ s   m  m ...,  m  s  xl]    ...,     [ l   s  l ...,  l  l  m]    [ s   s  s ...,  s  s  l]    [ l   m  s ...,  s  s  s]]

in python dicts natural choice mapping keys values. numpy has no direct equivalent of dict. have arrays can fast integer indexing. example,

in [153]: keyarray = np.array(['s','m','l','xl'])  in [158]: data = np.array([[0,2,1], [1,3,2]])  in [159]: keyarray[data] out[159]:  array([['s', 'l', 'm'],        ['m', 'xl', 'l']],        dtype='|s2')

so if massage key array 1 looked this:

in [161]: keyarray out[161]:  array(['', '', '', '', '', '', '', '', '', '', 's', 's', 's', 'm', 'l',        's', 's', '', '', '', '', '', '', '', '', '', '', '', '', '', '',        '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',        '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',        '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',        '', '', '', '', '', '', '', '', '', '', 'xl', 'm', 'xl', 's'],        dtype='|s32')

so 10 maps 's' in sense keyarray[10] equals s, , forth:

in [162]: keyarray[10] out[162]: 's'

then produce desired result keyarray[data].

import numpy np  data = np.array( [[ 13.,   15.,  15.,  15.,  15.,  16.],                   [ 14.,   13.,  14.,  13.,  15.,  16.],                   [ 16.,   13.,  13.,  13.,  15.,  17.],                   [ 14.,   15.,  14.,  14.,  14.,  13.],                   [ 15.,   15 ,  16.,  16.,  15.,  14.],                   [ 14.,   13.,  16.,  16.,  16.,  16.]])  key = np.array([[ 10., 's'],                 [ 11., 's'],                 [ 12., 's'],                 [ 13., 'm'],                 [ 14., 'l'],                 [ 15., 's'],                 [ 16., 's'],                 [ 17., 'xl'],                 [ 92., 'xl'],                 [ 93., 'm'],                 [ 94., 'xl'],                 [ 95., 's']])  idx = np.array(key[:,0], dtype=float).astype(int) n = idx.max()+1 keyarray = np.empty(n, dtype=key[:,1].dtype) keyarray[:] = '' keyarray[idx] = key[:,1]  data = data.astype('int') print(keyarray[data])

yields

[['m' 's' 's' 's' 's' 's']  ['l' 'm' 'l' 'm' 's' 's']  ['s' 'm' 'm' 'm' 's' 'xl']  ['l' 's' 'l' 'l' 'l' 'm']  ['s' 's' 's' 's' 's' 'l']  ['l' 'm' 's' 's' 's' 's']]

note data = data.astype('int') assuming floats in data can uniquely mapped ints. appears case data, not true arbitrary floats. example, astype('int') maps both 1.0 , 1.5 map 1. 

in [167]: np.array([1.0, 1.5]).astype('int') out[167]: array([1, 1])

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