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Articles on Trending Technologies
Technical articles with clear explanations and examples
Test array values for NaN in Numpy
To test array for NaN, use the numpy.isnan() method in Python Numpy. Returns True where x is NaN, false otherwise. This is a scalar if x is a scalar. 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, locations within it where the condition is False will remain uninitialized.NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that ...
Read MoreReturn mantissa and exponent as a pair of a given value in Numpy
To return mantissa and exponent as a pair of a given value, use the numpy.frexp() method in Python Numpy. 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 length equal to the number of outputs.The condition is broadcast over the input. At locations where the condition is True, the out array willbe set to the ufunc result. Elsewhere, the out array will retain its original value. ...
Read MoreReturn the fractional and integral parts of a value in Numpy
To return the fractional and integral parts of a value, use the numpy.modf() method in Python Numpy. The fractional and integral parts are negative if the given number is negative.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 length equal to the number of outputs.The condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ...
Read MoreChange the sign of Numpy array values to that of a scalar element-wise
To change the sign of array values to that of a scalar, element-wise, use the numpy.copysign() method in Python Numpy. The 1st parameter of the copysign() is the value (array elements) to change the sign of. The 2nd parameter is the sign to be copied to 1st parameter value.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 length equal to the number of outputs.The condition is ...
Read MoreReturn element-wise True where signbit is set (less than zero) in Numpy
To return element-wise True where signbit is set (less than zero), use the numpy.signbit() method in Python Numpy. Returns the output array, or reference to out if that was supplied. This is a scalar if x is a scalar.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 length equal to the number of outputs.The condition is broadcast over the input. At locations where the condition is ...
Read MoreCompute the absolute values element-wise and store the result in a new location in Numpy
To compute the absolute values element-wise, use the numpy.fabs() method in Python Numpy. The new location where we will store the result is a new array.This function returns the absolute values (positive magnitude) of the data in x. Complex values are not handled, use absolute to find the absolute values of complex data. 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, locations ...
Read MoreTest element-wise for NaT (not a time) in Numpy
To test element-wise for NaT, use the numpy.isnat() method in Python Numpy. It checks the value for datetime or timedelta data type.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, locations within it where the condition is False will remain uninitialized.StepsAt first, import the required library −import numpy as npTo test element-wise for NaT, use the numpy.isnat() method in Python Numpy. It checks ...
Read MoreTest Numpy array values for infiniteness and store the result in a new location
To test array values for infiniteness, use the numpy.isinf() method in Python Numpy. The new location where we will store the result is a new array. Returns a boolean array of the same shape as x, True where x == +/-inf, otherwise False.NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). Errors result if the second argument is supplied when the first argument is a scalar, or if the first and second arguments have different shapes.StepsAt first, import the required library −import numpy as npCreate an array with some inf values −arr = np.array([1, 2, 10, 50, ...
Read MoreTest array values for positive or negative infinity in Numpy
To test array for positive or negative infinity, use the numpy.isinf() method in Python Numpy. Returns a boolean array of the same shape as x, True where x == +/-inf, otherwise False.NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). Errors result if the second argument is supplied when the first argument is a scalar, or if the first and second arguments have different shapes.StepsAt first, import the required library −import numpy as npCreate an array with some inf values −arr = np.array([1, 2, 10, 50, -np.inf, 0., np.inf]) Display the arrays −print("Array...", arr)Get the type of ...
Read MoreUsing the numpy.ldexp() in Numpy
To return the x1 * 2**x2, element-wise, use the numpy.ldexp() method in Python Numpy. The 1st parameter X1 is the array of multipliers. The 2nd parameter X2 is the array of twos exponents. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).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 length equal to the number of ...
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