To return a copy of the array collapsed into one dimension, use the ma.MaskedArray.flatten() method in Numpy.The order ‘C’ means to flatten in row-major (C-style) order. ‘F’ means to flatten in column-major (Fortran- style) order. ‘A’ means to flatten in column-major order if a is Fortran contiguous in memory, row-major order otherwise. ‘K’ means to flatten a in the order the elements occur in memory. The default is ‘C’.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 ... Read More

To return each element rounded, use the ma.MaskedArray.around() method in Python Numpy. The decimals parameter are the number of decimal places to round to (default: 0). If decimals is negative, it specifies the number of positions to the left of the decimal point.The out parameter is an alternative output array in which to place the result. It must have the same shape as the expected output, but the type of the output values will be cast if necessary. See Output type determination for more details.The around() method returns an array of the same type as a, containing the rounded values. ... Read More

To return the range of values from a masked array, use the ma.MaskedArray.ptp() method in Numpy. Peak to peak (maximum - minimum) value along a given axis.The ptp() method returns a new array holding the result, unless out was specified, in which case a reference to out is returned.The axis parameter is the axis along which to find the peaks. If None (default) the flattened array is used. The out is a parameter, an alternative output array in which to place the result. It must have the same shape and buffer length as the expected output but the type will ... Read More

To return the range of values from a masked array, use the ma.MaskedArray.ptp() method in Numpy. Peak to peak (maximum - minimum) value along a given axis. The axis is set using the axis parameter.The ptp() method returns a new array holding the result, unless out was specified, in which case a reference to out is returned.The axis parameter is the axis along which to find the peaks. If None (default) the flattened array is used. The out is a parameter, an alternative output array in which to place the result. It must have the same shape and buffer length ... Read More

To return the array data as a string containing the raw bytes in the array, use the ma.MaskedArray.tobytes() method in Numpy.The fill_value parameter is the value used to fill in the masked values. Default is None, in which case MaskedArray.fill_value is used.The order parameter is the Order of the data item in the copy. Default is ‘C’.‘C’ - C order (row major).‘F’ - Fortran order (column major).‘A’ - Any, current order of array.None - Same as ‘A’.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 ... Read More

To return the array data as a string containing the raw bytes in the array, use the ma.MaskedArray.tobytes() method in Numpy.The fill_value parameter is the value used to fill in the masked values. Default is None, in which case MaskedArray.fill_value is used.The order parameter is the Order of the data item in the copy. Default is ‘C’.‘C’ - C order (row major).‘F’ - Fortran order (column major).‘A’ - Any, current order of array.None - Same as ‘A’.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 ... Read More

To return the array data as a string containing the raw bytes in the array, use the ma.MaskedArray.tobytes() method in Numpy.The fill_value parameter is the value used to fill in the masked values. Default is None, in which case MaskedArray.fill_value is used.The order parameter is the Order of the data item in the copy. Default is ‘C’.‘C’ - C order (row major).‘F’ - Fortran order (column major).‘A’ - Any, current order of array.None - Same as ‘A’.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 ... Read More

To return the array data as a string containing the raw bytes in the array, use the ma.MaskedArray.tobytes() method in Numpy. The array is filled with a fill value before the string conversion.The fill_value is the value used to fill in the masked values. Default is None, in which case MaskedArray.fill_value is used.The order parameter is the Order of the data item in the copy. Default is ‘C’.‘C’ - C order (row major).‘F’ - Fortran order (column major).‘A’ - Any, current order of arrayNone - Same as ‘A’.StepsAt first, import the required library −import numpy as np import numpy.ma as ... Read More

To transform a masked array into a flexible-type array, use the ma.MaskedArray.torecords(). The flexible type array that is returned will have two fieldsThe _data field stores the _data part of the array.The _mask field stores the _mask part of the array.method in Numpy.Returns a new flexible-type ndarray with two fields − the first element containing a value, the second element containing the corresponding mask boolean. The returned record shape matches self.shape.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([[49, 85, 45], [67, 33, ... Read More

To return the data portion of the masked array as a hierarchical Python list, use the ma.MaskedArray.tolist() method in Numpy. Data items are converted to the nearest compatible Python type.Masked values are converted to fill_value. If fill_value is None, the corresponding entries in the output list will be None. The method returns the Python list representation of the masked array.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 ... Read More