To compute the truth value of an array OR another array element-wise, use the numpy.logical_or() method in Python Numpy. Return value is either True or False. Return value is the boolean result of the logical OR operation applied to the elements of x1 and x2; the shape is determined by broadcasting. This is a scalar if both x1 and x2 are scalarsThe out is a location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a ... Read More

To compute the truth value of an array AND another array element-wise, use the numpy.logical_and() method in Python Numpy. Return value is either True or False. We have set conditions here.Return value is the Boolean result of the logical AND operation applied to the elements of x1 and x2; the shape is determined by broadcasting. This is a scalar if both x1 and x2 are scalars.The condition is 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 that ... Read More

To compute the truth value of an array AND another array element-wise, use the numpy.logical_and() method in Python Numpy. Return value is either True or False. Return value is the Boolean result of the logical AND operation applied to the elements of x1 and x2; the shape is determined by broadcasting. This is a scalar if both x1 and x2 are scalars.The out is a location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a ... Read More

To return the truth value of an array not equal to another element-wise, use the numpy.not_equal() method in Python Numpy. Return value is either True or False. The function returns an output array, element-wise comparison of x1 and x2. Typically of type bool, unless dtype=object is passed. This is a scalar if both x1 and x2 are scalars.The out is a location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have ... Read More

To return the truth value of an array less than another element-wise, use the numpy.less() method in Python Numpy. Return value is either True or False. Returns an output array, element-wise comparison of x1 and x2. Typically, of type bool, unless dtype=object is passed. This is a scalar if both x1 and x2 are scalars.The condition is 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 that if an uninitialized out array is created via the default out=None, ... Read More

To reset the fill value of the ma, use the ma.MaskedArray.fill_value() method in Python Numpy and set it to None.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.NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. It supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse ... Read More

To get the fill value, use the ma.MaskedArray.get_fill_value() method in Python Numpy. The filling value of the masked array is a scalar. 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 maCreate an array with int elements using the numpy.array() method −arr = np.array([[65, 68, 81], [93, 33, ... Read More

To force the mask to hard, use the ma.MaskedArray.soften_mask() method. Whether the mask of a masked array is hard or soft is determined by its hardmask property. The soften_mask() sets hardmask to False.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.NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. It supports a wide range ... Read More

To compute the differences between consecutive elements of a masked array, use the MaskedArray.ediff1d() method in Python Numpy. The "to_begin" parameter sets the number(s) to prepend at the beginning of the returned differences.This function is the equivalent of numpy.ediff1d that takes masked values into account, see numpy.ediff1d for details.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 notStepsAt first, import the ... Read More

To compute the differences between consecutive elements of a masked array, use the MaskedArray.ediff1d() method in Python Numpy. This function is the equivalent of numpy.ediff1d that takes masked values into account, see numpy.ediff1d for details.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 npCreate an array with int elements using the numpy.array() ... Read More