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  2. 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 '

  3. Here is a fast vectorized version of @Dimitri's solution if you have many values to search for (values can be multi-dimensional array):

  4. I've made good use of this to find multiple values in a list using a list comprehension: [find_list.index(index_list[i]) for i in range(len(index_list))] – Matti Wens Commented Apr 29, 2019 at 21:24

  5. A simple and clean way: use np.argwhere to group the indices by element, rather than dimension as in np.nonzero(a) (i.e., np.argwhere returns a row for each non-zero element).

  6. I have a NumPy array, A. I want to know the indexes of the elements in A equal to a value and which indexes satisfy some condition: import numpy as np A = np.array([1 ...

  7. Although it is way too late for you, but for future reference: Using numba (1) is the easiest way until numpy implements it. If you use anaconda python distribution it should already be installed. The code will be compiled so it will be fast. @jit(nopython=True) def find_first(item, vec): """return the index of the first occurence of item in ...

  8. NumPy has the efficient function/method nonzero() to identify the indices of non-zero elements in an ndarray object. What is the most efficient way to obtain the indices of the elements that do hav...

  9. axis=0, the index of the max element per column will be returned. axis=1 the index of the max element per row will be returned. The following code will help you to better understand. import numpy as np. a = np.array([[1,2,3],[4,3,1]]) #This will print the index of the max value for each column.

  10. In case of a range or any other linearly increasing array you can simply calculate the index programmatically, no need to actually iterate over the array at all: def first_index_calculate_range_like(val, arr): if len(arr) == 0: raise ValueError('no value greater than {}'.format(val)) elif len(arr) == 1:

  11. NumPy proposes a way to get the index of the maximum value of an array via np.argmax. I would like a similar thing, but returning the indexes of the N maximum values.