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 Gradient, which is calculated only along the given axis or axes. The default (axis = None) is to calculate the gradient for all the axes of the input array. axis may be negative, in which case it counts from the last to the first axis. The method returns a list of ndarrays corresponding to the derivatives of f with respect to each dimension. Each derivative has the same shape as f.
Steps
At first, import the required library −
import numpy as np
Creating a numpy array using the array() method. We have added elements of float type −
arr = np.array([[20, 35, 57], [70, 85, 120]], dtype = float)
Display the array −
print("Our Array...\n",arr)Check the Dimensions −
print("\nDimensions of our Array...\n",arr.ndim)
Get the Datatype −
print("\nDatatype of our Array object...\n",arr.dtype)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 −
print("\nResult (gradient)...\n",np.gradient(arr, axis = 1))Example
import numpy as np
# Creating a numpy array using the array() method
# We have added elements of float type
arr = np.array([[20, 35, 57], [70, 85, 120]], dtype = float)
# Display the array
print("Our Array...\n",arr)
# Check the Dimensions
print("\nDimensions of our Array...\n",arr.ndim)
# Get the Datatype
print("\nDatatype of our Array object...\n",arr.dtype)
# 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.
print("\nResult (gradient)...\n",np.gradient(arr, axis = 1))Output
Our Array... [[ 20. 35. 57.] [ 70. 85. 120.]] Dimensions of our Array... 2 Datatype of our Array object... float64 Result (gradient)... [[15. 18.5 22. ] [15. 25. 35. ]]