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?
06 Answers
Use np.where to get the indices where a given condition is True.
Examples:
For a 2D np.ndarray called a:
i, j = np.where(a == value) # when comparing arrays of integers i, j = np.where(np.isclose(a, value)) # when comparing floating-point arrays For a 1D array:
i, = np.where(a == value) # integers i, = np.where(np.isclose(a, value)) # floating-point Note that this also works for conditions like >=, <=, != and so forth...
You can also create a subclass of np.ndarray with an index() method:
class myarray(np.ndarray): def __new__(cls, *args, **kwargs): return np.array(*args, **kwargs).view(myarray) def index(self, value): return np.where(self == value) Testing:
a = myarray([1,2,3,4,4,4,5,6,4,4,4]) a.index(4) #(array([ 3, 4, 5, 8, 9, 10]),) 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.
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.
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.
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) 
