To calculate the n-th discrete difference along the given axis, use the MaskedArray.diff() method in Python Numpy. The first difference is given by out[i] = a[i+1] - a[i] along the given axis, higher differences are calculated by using diff recursively −The axis is set using the "axis" parameterThe axis is the axis along which the difference is taken, default is the last axis.The function returns the n-th differences. The shape of the output is the same as a except along axis where the dimension is smaller by n. The type of the output is the same as the type of ... Read More

To mask an array where less than a given value, use the numpy.ma.masked_less() method in Python Numpy. This function is a shortcut to masked_where, with condition = (x < value).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 ... Read More

To mask an array where greater than equal to a given value, use the numpy.ma.masked_greater_equal() method in Python Numpy. This function is a shortcut to masked_where, with condition = (x >= value).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 ... Read More

To compute the maximum of the masked array elements along a given axis, use the MaskedArray.max() method in Python Numpy. The axis is set using the "axis" parameter. The axis is the axis along which to operate.The function max() returns a new array holding the result. If out was specified, out is returned. The out parameter is alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output. The fill_value is a value used to fill in the masked values. If None, use the output of maximum_fill_value(). The keepdims, ... Read More

To compute the maximum of the masked array elements along a given axis, use the MaskedArray.max() method in Python Numpy −The axis is set using the "axis" parameterThe axis is the axis along which to operateThe function max() returns a new array holding the result. If out was specified, out is returned. The out parameter is alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output. The fill_value is a value used to fill in the masked values. If None, use the output of maximum_fill_value(). The keepdims, if ... Read More

To compute the maximum of the masked array elements along a given axis, use the MaskedArray.max() method in Python Numpy. The function max() returns a new array holding the result. If out was specified, out is returned. The axis is set using the "axis" parameter. The axis is the axis along which to operate.The out parameter is alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output. The fill_value is a value used to fill in the masked values. If None, use the output of maximum_fill_value(). The keepdims, ... Read More

To return element-wise base array raised to power from second array, use the MaskedArray.power() method in Python Numpy.The where parameter is a 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 that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.The out parameter is a location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. ... Read More

To compute the median of the masked array elements along specific axis, use the MaskedArray.median() method in Python Numpy −The axis is set using the "axis" parameterThe axis is axis along which the medians are computed.The default (None) is to compute the median along a flattened version of the array.The overwrite_input parameter, if True, then allow use of memory of input array (a) for calculations. The input array will be modified by the call to median. This will save memory when you do not need to preserve the contents of the input array. Treat the input as undefined, but it ... Read More

To compute the median of the masked array elements along specific axis, use the MaskedArray.median() method in Python Numpy −The axis is set using the "axis" parameterThe axis is axis along which the medians are computed.The default (None) is to compute the median along a flattened version of the array.The overwrite_input parameter, if True, then allow use of memory of input array (a) for calculations. The input array will be modified by the call to median. This will save memory when you do not need to preserve the contents of the input array. Treat the input as undefined, but it ... Read More

To divide masked array elements by a given scalar element and return arrays with Quotient and Remainder, use the ma.MaskedArray.__divmod__() method in Python Numpy. 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 ... Read More