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To return the data of a masked array as an ndarray, use the ma.getdata() method in Python Numpy. Returns the data of a (if any) as an ndarray if a is a MaskedArray, else return a as a ndarray or subclass if not.The subok parameter suggest whether to force the output to be a pure ndarray (False) or to return a subclass of ndarray if appropriate (True, default).A masked array is the combination of a standard numpy.ndarray and a mask. A mask is either nomask, indicating that no value of the associated array is invalid, or an array of booleans ... Read More

To return the mask of a masked array, use the ma.getmaskarray() method in Python Numpy. Returns the mask of arr as an ndarray if arr is a MaskedArray and the mask is not nomask, else return a full boolean array of False of the same shape as arr.A masked array is the combination of a standard numpy.ndarray and a mask. A mask is either nomask, indicating that no value of the associated array is invalid, or an array of booleans that determines for each element of the associated array whether the value is valid or not.StepsAt first, import the required ... Read More

To compute the bit-wise AND of a 1D and a 2D array element-wise, use the numpy.bitwise_and() method in Python Numpy.Computes the bit-wise AND of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator &.The 1st and 2nd parameter are the arrays, only integer and boolean types are handled. If x1.shape != x2.shape, they must be broadcastable to a common shape.The where parameter is the condition broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain ... Read More

To compute the bit-wise AND of two 1D arrays element-wise, use the numpy.bitwise_and() method in Python Numpy. Computes the bit-wise AND of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator &.The 1st and 2nd parameter are the arrays, only integer and boolean types are handled. If x1.shape != x2.shape, they must be broadcastable to a common shape.The where parameter is the condition broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original ... Read More

To compute the bit-wise AND of two 2D arrays element-wise, use the numpy.bitwise_and() method in Python Numpy. Computes the bit-wise AND of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator &.The 1st and 2nd parameter are the arrays, only integer and boolean types are handled. If x1.shape != x2.shape, they must be broadcastable to a common shape.The where parameter is the condition broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original ... Read More

To determine whether input is an instance of masked array, use the ma.isMaskedArray() method in Python Numpy. This function returns True if x is an instance of MaskedArray and returns False otherwise. Any object is accepted as input.A masked array is the combination of a standard numpy.ndarray and a mask. A mask is either nomask, indicating that no value of the associated array is invalid, or an array of booleans that determines for each element of the associated array whether the value is valid or not.StepsAt first, import the required library −import numpy as np import numpy.ma as maCreating a ... Read More

To determine whether input has masked values, use the ma.is_masked() method in Python Numpy. Accepts any object as input, but always returns False unless the input is a MaskedArray containing masked values. Returns True if the array is a MaskedArray with masked values, False otherwise.A masked array is the combination of a standard numpy.ndarray and a mask. A mask is either nomask, indicating that no value of the associated array is invalid, or an array of booleans that determines for each element of the associated array whether the value is valid or not.StepsAt first, import the required library −import numpy ... Read More

To compute the bit-wise AND of two arrays element-wise, use the numpy.bitwise_and() method in Python Numpy.Computes the bit-wise AND of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator &.The 1st and 2nd parameter are the arrays, only integer and boolean types are handled. If x1.shape != x2.shape, they must be broadcastable to a common shape.The where parameter is the condition broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note ... Read More

To compute the bit-wise AND of two arrays element-wise, use the numpy.bitwise_and() method in Python Numpy. Computes the bit-wise AND of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator &.The 1st and 2nd parameter are the arrays, only integer and boolean types are handled. If x1.shape != x2.shape, they must be broadcastable to a common shape.The where parameter is the condition broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. ... Read More

To build a block of matrix, use the numpy.block() method in Python Numpy. Here, we will build a block matrix from a list with depth two. Blocks in the innermost lists are concatenated along the last dimension (-1), then these are concatenated along the second-last dimension (-2), and so on until the outermost list is reached.Blocks can be of any dimension, but will not be broadcasted using the normal rules. Instead, leading axes of size 1 are inserted, to make block.ndim the same for all blocks. This is primarily useful for working with scalars, and means that code like np.block([v, ... Read More