We can use the numpy.gradient() function to find the gradient of an N-dimensional array. For gradient approximation, the function uses either first or second-order accurate one-sided differences at the boundaries and second-order accurate central differences in the interior (or non-boundary) points.
Note: To create a two-dimensional (2D) array in Python, we can use a list of lists.
numpy.gradient(f, *varargs, axis=None, edge_order=1)
f: This is the N-dimensional array containing scalar function samples for which gradient will calculate the gradient.varargs: This is an optional parameter that represents a scalar list. It contains the sample distances for each dimension—dx, dy, dz, and so on—total N scalars. 1 is the default value for this argument.axis: This is an optional parameter representing the axis along which gradient will calculate the gradient. If this is not None, then the number of varargs must equal the axes. The default value for this argument is None.edge_order: {1,2} This is an optional parameter.gradient: This will calculate the gradient using Nth order (as specified by edge_order) accurate boundary differences. The default value for this argument is 1.The numpy.gradient() method returns an ndarray or a list of ndarrays representing the gradient.
Note: The result for 2D arrays will be two arrays ordered by axis.
The following code shows how to use the numpy.gradient() method for 2D arrays.
# import numpyimport numpy as np# create listx1 = [7, 4, 8, 3]x2 = [2, 6, 5, 9]# convert the lists to 2D array using np.arrayf = np.array([x1, x2])# compute the gradient of an N-dimensional array# and store the result in resultresult = np.gradient(f)print(result)
numpy library.x1 and x2.np.array() method to transform the lists into a 2D array and store it in f.f using the np.gradient() function and store it in result.result.