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Articles on Trending Technologies
Technical articles with clear explanations and examples
Use an index array to construct a new array from a set of choices with clip mode in Numpy
A new array from the set of choices is constructed using the np.ma.choose() method. The mode parameter is set to 'clip'. If mode='clip', values greater than n-1 are mapped to n-1; and then the new array is constructed.Given an array of integers and a list of n choice arrays, this method will create a new array that merges each of the choice arrays. Where a value in index is i, the new array will have the value that choices[i] contains in the same place.The choices parameter is the choice arrays. The index array and all of the choices should be ...
Read MoreCalculate the n-th discrete difference in Numpy
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 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 the difference between any two elements of a. This is the same as the type of a in most cases. A notable exception ...
Read MoreCompute the minimum of the masked array elements along a given axis in Numpy
To compute the minimum of the masked array elements along a given axis, use the MaskedArray.min() method in Python Numpy. The axis is set using the "axis" parameter. The axis is the axis along which to operate.The function min() 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 minimum_fill_value(). The keepdims, ...
Read MoreMask an array where less than or equal to a given value in Numpy
To mask an array where less than equal to a given value, use the numpy.ma.masked_less_equal() method in Python Numpy. This function is a shortcut to masked_where, with condition = (x
Read MoreReturn the dot product of two masked arrays in Numpy
To return the dot product of two masked arrays, use the ma.dot() method in Python Numpy. This function is the equivalent of numpy.dot that takes masked values into account. The strict and out are in different position than in the method version. In order to maintain compatibility with the corresponding method, it is recommended that the optional arguments be treated as keyword only. At some point that may be mandatory.The strict parameter sets whether masked data are propagated (True) or set to 0 (False) for the computation. Default is False. Propagating the mask means that if a masked value appears ...
Read MoreMask array elements less than a given value in Numpy
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 MoreMask array elements greater than or equal to a given value in Numpy
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 MoreCompute the maximum of the masked array elements along a given axis in Numpy
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 MoreReturn element-wise base masked array raised to power from second array in Numpy
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 MoreCompute the median of the masked array elements along specified axis in Numpy
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 ...
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