CODEWITHSUNDEEP
pythonnumpypandas
Baktaawar
Baktaawar

Reputation: 7490

Getting argsort of numpy array

I have a numpy array as follows: 

array([ True,  True,  True,  True,  True, False,  True,  True, False,
        True, False,  True,  True,  True,  True,  True,  True, False,
       False, False, False, False,  True,  True, False, False, False,
        True,  True,  True,  True,  True,  True,  True, False,  True,
        True,  True,  True, False,  True,  True, False, False,  True,
        True,  True, False,  True,  True,  True, False],

I want to get the indices of all the True elements. There is no get_loc method in numpy like Pandas Series and similarly no index method like a list. I don't want to convert it into a list and then use .index.

Any idea?

Upvotes: 5

Views: 243

Answers (3)

fixxxer
fixxxer

Reputation: 16134

The np.ix_ way seems to be the slowest. 

In [846]: % timeit a.nonzero()
1000000 loops, best of 3: 707 ns per loop

In [845]: % timeit np.where(a)
1000000 loops, best of 3: 883 ns per loop

In [849]: %timeit np.ix_(a==True)
100000 loops, best of 3: 9.21 µs per loop

Upvotes: 0

EdChum
EdChum

Reputation: 393963

To do this in pandas:

In [255]:

s[s==True].index
Out[255]:
Int64Index([0, 1, 2, 3, 4, 6, 7, 9, 11, 12, 13, 14, 15, 16, 22, 23, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 40, 41, 44, 45, 46, 48, 49, 50], dtype='int64')

Update

Actually you can use the fact that the values are already boolean values to mask the series:

In [256]:

s[s].index
Out[256]:
Int64Index([0, 1, 2, 3, 4, 6, 7, 9, 11, 12, 13, 14, 15, 16, 22, 23, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 40, 41, 44, 45, 46, 48, 49, 50], dtype='int64')

Similarly for numpy arrays you can use the boolean values to mask the array and get the index values using np.where:

In [261]:

np.where(a)
​
Out[261]:
(array([ 0,  1,  2,  3,  4,  6,  7,  9, 11, 12, 13, 14, 15, 16, 22, 23, 27,
        28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 40, 41, 44, 45, 46, 48, 49,
        50], dtype=int64),)

Upvotes: 0

wim
wim

Reputation: 362557

Use ndarray.nonzero:

>>> a.nonzero()
(array([ 0,  1,  2,  3,  4,  6,  7,  9, 11, 12, 13, 14, 15, 16, 22, 23, 27,
        28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 40, 41, 44, 45, 46, 48, 49,
        50]),)

Upvotes: 3

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