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
