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The N-dimensional array (ndarray) — NumPy v2. 3 Manual New arrays can be constructed using the routines detailed in Array creation routines, and also by using the low-level ndarray constructor: ndarray (shape[, dtype, buffer, offset, ]) An array object represents a multidimensional, homogeneous array of fixed-size items
numpy. array — NumPy v2. 3 Manual numpy array# numpy array (object, dtype = None, *, copy = True, order = 'K', subok = False, ndmin = 0, like = None) # Create an array Parameters: object array_like An array, any object exposing the array interface, an object whose __array__ method returns an array, or any (nested) sequence If object is a scalar, a 0-dimensional array
Indexing on ndarrays — NumPy v2. 3 Manual Most of the following examples show the use of indexing when referencing data in an array The examples work just as well when assigning to an array See Assigning values to indexed arrays for specific examples and explanations on how assignments work
numpy. isin — NumPy v2. 3 Manual numpy isin (element, test_elements, assume_unique = False, invert = False, *, kind = None) [source] # Calculates element in test_elements , broadcasting over element only Returns a boolean array of the same shape as element that is True where an element of element is in test_elements and False otherwise