[python] Index of element in NumPy array

In Python we can get the index of a value in an array by using .index().

But with a NumPy array, when I try to do:

decoding.index(i)

I get:

AttributeError: 'numpy.ndarray' object has no attribute 'index'

How could I do this on a NumPy array?

You can use the function numpy.nonzero(), or the nonzero() method of an array

import numpy as np

A = np.array([[2,4],
          [6,2]])
index= np.nonzero(A>1)
       OR
(A>1).nonzero()

Output:

(array([0, 1]), array([1, 0]))

First array in output depicts the row index and second array depicts the corresponding column index.

You can convert a numpy array to list and get its index .

for example:

tmp = [1,2,3,4,5] #python list
a = numpy.array(tmp) #numpy array
i = list(a).index(2) # i will return index of 2, which is 1

this is just what you wanted.

If you are interested in the indexes, the best choice is np.argsort(a)

a = np.random.randint(0, 100, 10)
sorted_idx = np.argsort(a)

I'm torn between these two ways of implementing an index of a NumPy array:

idx = list(classes).index(var)
idx = np.where(classes == var)

Both take the same number of characters, but the first method returns an int instead of a numpy.ndarray.

This problem can be solved efficiently using the numpy_indexed library (disclaimer: I am its author); which was created to address problems of this type. npi.indices can be viewed as an n-dimensional generalisation of list.index. It will act on nd-arrays (along a specified axis); and also will look up multiple entries in a vectorized manner as opposed to a single item at a time.

a = np.random.rand(50, 60, 70)
i = np.random.randint(0, len(a), 40)
b = a[i]

import numpy_indexed as npi
assert all(i == npi.indices(a, b))

This solution has better time complexity (n log n at worst) than any of the previously posted answers, and is fully vectorized.