# To check whether int data types of different sizes are not subdtypes of each other, use the numpy.issubdtype() method in Python Numpy.# The parameters are the dtype or object coercible to oneStepsAt first, import the required library −import numpy as npUsing the issubdtype() method in Numpy. Checking for int datatype with different sizes −print("Result...", np.issubdtype(np.int16, np.int32)) print("Result...", np.issubdtype(np.int32, np.int16)) print("Result...", np.issubdtype(np.int64, np.int32)) print("Result...", np.issubdtype(np.int32, np.int64)) print("Result...", np.issubdtype(np.int16, np.int64)) print("Result...", np.issubdtype(np.int64, np.int16))Exampleimport numpy as np # To check whether int data types of different sizes are not subdtypes of each other, use the numpy.issubdtype() method in Python Numpy. # ... Read More

To check whether similar data types of different sizes are not subdtypes of each other, use the numpy.issubdtype() method in Python Numpy. The parameters are the dtype or object coercible to one.StepsAt first, import the required library −import numpy as npUsing the issubdtype() method in Nump to check for similar datatypes with different sizes. Checking for float datatype with different sizes −print("Result...", np.issubdtype(np.float32, np.float64)) print("Result...", np.issubdtype(np.float64, np.float32))Checking for int datatype with different sizes −print("Result...", np.issubdtype(np.int16, np.int32)) print("Result...", np.issubdtype(np.int32, np.int16)) print("Result...", np.issubdtype(np.int64, np.int32)) print("Result...", np.issubdtype(np.int32, np.int64))Exampleimport numpy as np # To check whether similar data types of different sizes are ... Read More

The gradient is computed using second order accurate central differences in the interior points and either first or second order accurate one-sides (forward or backwards) differences at the boundaries. The returned gradient hence has the same shape as the input array. The 1st parameter, f is an Ndimensional array containing samples of a scalar function. The 2nd parameter is the varargs i.e. the spacing between f values. Default unitary spacing for all dimensions.The 3rd parameter is the edge_order{1, 2} i.e. the Gradient is calculated using N-th order accurate differences at the boundaries. Default − 1. The 4th parameter is the ... Read More

To determine whether the given object represents a scalar data-type, use the numpy.issctype() method. The method returns Boolean result of check whether rep is a scalar dtype. The first parameter is the rep. If rep is an instance of a scalar dtype, True is returned.If not, False is returned.StepsAt first, import the required library −import numpy as npUsing the issctype() method in Numpy −print("Result...", np.issctype(np.int32)) print("Result...", np.issctype(np.int64)) print("Result...", np.issctype(np.dtype('str'))) print("Result...", np.issctype(100)) print("Result...", np.issctype(25.9)) print("Result...", np.issctype(np.float32(22.3)))Exampleimport numpy as np # To determine whether the given object represents a scalar datatype, use the numpy.issctype() method # The method returns Boolean result ... Read More

To return the cumulative sum of array elements over a given axis treating NaNs as zero, use the nancumprod() method. The cumulative sum does not change when NaNs are encountered and leading NaNs are replaced by zeros. Zeros are returned for slices that are all-NaN or empty. The method returns a new array holding the result unless out is specified, in which it is returned. The result has the same size as a, and the same shape as a if axis is not None or a is a 1-d array.The 1st parameter is the input array. The 2nd parameter is ... Read More

To return the cumulative sum of array elements over a given axis treating NaNs as zero, use the nancumprod() method. The cumulative sum does not change when NaNs are encountered and leading NaNs are replaced by zeros. Zeros are returned for slices that are all-NaN or empty. Cumulative works like, 5, 5+10, 5+10+15, 5+10+15+20.The 1st parameter is the input array. The 2nd parameter is the axis along which the cumulative sum is computed. The default (None) is to compute the cumsum over the flattened array. The 3rd parameter is the type of the returned array and of the accumulator in ... Read More

The numpy.result_type() method returns the type that results from applying the NumPy type promotion rules to the arguments. The 1st parameter is the operands of some operation whose result type is needed. Type promotion in NumPy works similarly to the rules in languages like C++, with some slight differences. When both scalars and arrays are used, the array’s type takes precedence and the actual value of the scalar is taken into account.StepsAt first, import the required library −import numpy as npThe numpy.result_type() method returns the type that results from applying the NumPy type promotion rules to the arguments −print("Using the ... Read More

The numpy.min_scalar() method finds the minimal data type. The 1st parameter is the value whose minimal data type is to be found. For scalar, returns the data type with the smallest size and smallest scalar kind which can hold its value. For non-scalar array, returns the vector’s dtype unmodified. Floating point values are not demoted to integers, and complex values are not demoted to floats.StepsAt first, import the required library −import numpy as npThe numpy.min_scalar() method finds the minimal data type. The 1st parameter is the value whose minimal data type is to be found −print("Using the min_scalar() method in ... Read More

To get the approximate number of decimal digits to which this kind of float is precise, use the precision attribute of the numpy.finfo() method in Python Numpy. The first parameter of the finfo() is the float i.e. the kind of float data type to get information about.StepsAt first, import the required library −import numpy as npChecking for float16 type. The precision is to get the approximate number of decimal digits. The iexp is to get the number of bits in the exponent portion. The min is the minimum value of given dtype. The max is the minimum value of given ... Read More

To get the number of bits in the exponent portion of the floating point representation, use the iexp attribute of the numpy.finfo() method in Python Numpy. The first parameter is the float i.e. the kind of float data type to get information about.StepsAt first, import the required library −import numpy as npChecking for float16 type. The iexp is to get the number of bits in the exponent portion. The min is the minimum value of given dtype. The max is the minimum value of given dtype. −a = np.finfo(np.float16(45.9)) print("Number of bits in the exponent portion float16 type...", a.iexp) print("Minimum ... Read More