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ndarrays can be indexed using the standard Python x [obj] syntax, where x is the array and obj the selection. There are different kinds of indexing available depending on obj: basic indexing, advanced indexing and field access. Most of the following examples show the use of indexing when referencing data in an array.
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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.
To get the indices of each maximum or minimum value for each (N-1)-dimensional array in an N-dimensional array, use reshape to reshape the array to a 2D array, apply argmax or argmin along axis=1 and use unravel_index to recover the index of the values per slice:
16 wrz 2022 · Much like working with Python lists, NumPy arrays are based on a 0 index. This means that the index starts at position 0 and continues through to the length of the list minus 1. Similarly, NumPy arrays can be negatively indexed, meaning that their last item can be accessed using the value of -1.
26 mar 2014 · ndarrays can be indexed using the standard Python x [obj] syntax, where x is the array and obj the selection. There are three kinds of indexing available: record access, basic slicing, advanced indexing. Which one occurs depends on obj. Note.
22 cze 2021 · ndarrays can be indexed using the standard Python x [obj] syntax, where x is the array and obj the selection. There are three kinds of indexing available: field access, basic slicing, advanced indexing. Which one occurs depends on obj. Note.
numpy.ndarray# class numpy. ndarray ( shape , dtype = float , buffer = None , offset = 0 , strides = None , order = None ) [source] # An array object represents a multidimensional, homogeneous array of fixed-size items.
