What does numpy.gradient() do?

Learn about the Python's numpy.gradient() method, and how does it work?
Submitted by Pranit Sharma, on January 21, 2023

NumPy is an abbreviated form of Numerical Python. It is used for different types of scientific operations in python. Numpy is a vast library in python which is used for almost every kind of scientific or mathematical operation. It is itself an array which is a collection of various methods and functions for processing the arrays.

numpy.gradient() Method

The numpy.gradient() method is used to find the gradient of an N-dimensional array. 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 backward) differences at the boundaries. The returned gradient hence has the same shape as the input array.


numpy.gradient(f, *varargs, axis=None, edge_order=1)


  • f: array_like- An N-dimensional array containing samples of a scalar function.
  • varargs: list of scalar or array, optional- Spacing between f values. Default unitary spacing for all dimensions. Spacing can be specified using:
  • single scalar to specify a sample distance for all dimensions.
    • N scalars to specify a constant sample distance for each dimension. i.e. dx, dy, dz, …
    • N arrays to specify the coordinates of the values along each dimension of F. The length of the array must match the size of the corresponding dimension
    • Any combination of N scalars/arrays with the meaning of 2. and 3.
    • If axis is given, the number of varargs must equal the number of axes.
  • edge_order: {1, 2}, optional- Gradient is calculated using N-th order accurate differences at the boundaries. Default: 1.

Let's understand with the help of an example,

Python code to demonstrate the example of numpy.gradient() method

# Import numpy
import numpy as np

# Creating a numpy array
arr = np.array([1, 2, 4, 7, 11, 16], dtype=float)

# Display original array
print("Original array:\n",arr,"\n")

# Finding gradient
res = np.gradient(arr)

# Display the result


Example: What does numpy.gradient() do?

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