applying a mask on a nested numpy array - numpy - python

Question

a bit embarassing to ask since the heavy documentation on Numpy but I was stuck doing this simple task, that is getting all the records for which a mask is true in a nested numpy representation (equivalent to the dataframe.loc[cond] in pandas):

import numpy as np
a1 = np.array([1,2,3])
a2 = np.array(['a','b','c'])
a3 = np.array(['luca','paolo','francesco'])
a4 = np.array([True, False,False], dtype='bool')

combination = np.array([a1,a2,a3,a4])
print(combination)

# slice for a4 == True 
combination[combination[3] == 'True']

but the result is not what I want.

in fact from combination :

[['1' '2' '3']
 ['a' 'b' 'c']
 ['luca' 'paolo' 'francesco']
 ['True' 'False' 'False']]

it yields with combination[combination[3] == 'True']:

array([['1', '2', '3']], 
      dtype='<U11')

when in reality I want:

[['1']
 ['a' ]
 ['luca']
 ['True' ]]

any ideas on what I am doing wrong?

P.S.: no i can't do it in pandas because pandas has my RAM exploding when converting this to a pandas.Dataframe

Tetrax · Accepted Answer · 2018-02-24 10:00:33Z

2

I believe you're simply missing the indices of the other dimension:

combination[combination[3] == 'True']

should be

combination[:, combination[3] == 'True']

Note the colon.

This yields a new ndarray indexed over all of the first dimension and only 0 in the second.

edited Feb 24, 2018 at 10:00

answered Sep 19, 2016 at 10:21

Tetrax

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1 Comment

Asher11 Over a year ago

I feel like smashing the keyboard after realizing this. Thank you for your quick answer!

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