NumPy Diff Simply Explained [Tutorial + Video]

NumPy’s np.diff() function calculates the difference between subsequent values in a NumPy array. For example, np.diff([1, 2, 4]) returns the difference array [1 2].

Here is a simple example to calculate the Fibonacci number differences:

import numpy as np

# Fibonacci Sequence with first 8 numbers
fibs = np.array([0, 1, 1, 2, 3, 5, 8, 13, 21])


diff_fibs = np.diff(fibs)
print(diff_fibs)
# [1 0 1 1 2 3 5 8]

This code snippet shows the most simple form of the np.diff() method: how to use it on a one-dimensional NumPy array. It calculates the difference between two subsequent values of a NumPy array. Hence, an array with n elements results in a diff array with n-1 elements.

Formal Syntax

numpy.diff(a, n=1, axis=-1, prepend=<no value>, append=<no value>)

Calculate the n-th discrete difference along the given axis.

• First difference: out[i] = a[i+1] - a[i] along the given axis.
• Higher differences: use np.diff() recursively.

Executing the NumPy Diff Method Multiple Times

We can also run the NumPy diff function multiple times by setting the second optional argument n:

import numpy as np

a = np.array([2, 4, 7, 4, 1, 8, 11, 12])
print(np.diff(a, n=1))
# [ 2  3 -3 -3  7  3  1]

print(np.diff(a, n=2))
# [ 1 -6  0 10 -4 -2]

print(np.diff(a, n=3))
# [ -7   6  10 -14   2]

print(np.diff(a, n=4))
# [ 13   4 -24  16]

print(np.diff(a, n=5))
# [ -9 -28  40]

print(np.diff(a, n=6))
# [-19  68]

print(np.diff(a, n=7))
# [87]

print(np.diff(a, n=8))
# []

By defining the argument n, you can execute the diff function multiple times on the respective output of the last execution. Hence, the call np.diff(x, n=2) results in the same output as np.diff(np.diff(x)).

>>> np.diff([1, 2, 4], 2)
array([1])
>>> np.diff(np.diff([1, 2, 4]))
array([1])

NumPy Diff with Two Axes

But what happens if you have a two-dimensional NumPy array? In other words, how does the diff function work with multiple axes?

Here is an example of how you can use the diff function to calculate the differences along the columns (axis=1):

import numpy as np

a = np.array([[0, 1, 1],
              [2, 3, 5],
              [8, 13, 21]])


diffs = np.diff(a, axis=1)
print(diffs)
"""
[[1 0]
 [1 2]
 [5 8]]
"""

You can see that each row with three columns is collapsed into a row with only two columns (the differences).

Let’s make it even more complex and combine the axis with the n argument for multiple diff executions in a single function call:

import numpy as np

a = np.array([[0, 1, 1],
              [2, 3, 5],
              [8, 13, 21]])


diffs = np.diff(a, n=2, axis=1)
print(diffs)
"""
[[-1]
 [ 1]
 [ 3]]
"""

In this puzzle, we use the axis argument axis=1 which means that we calculate the differences along the columns. For example, the first column results in the diff array [0 1].

When defining the parameter n, the diff function is applied n times to the output of the previous function execution. Thus, the first column undergoes the following transformations:

[0 1 1] diff--> [1 0] diff--> [-1]

Calculating Differences in a 3D NumPy Array

In NumPy, you can calculate differences along different axes in a 3D array using numpy.diff(). This function computes the difference between consecutive elements along a specified axis.

import numpy as np

arr = np.array([[[1, 3], [5, 7]], [[2, 4], [6, 8]]])
diff = np.diff(arr, axis=0)
print(diff)

Explanation: Here, np.diff calculates the difference along the first axis (axis=0) of a 3D array. It subtracts adjacent elements in this axis, resulting in a new array showing these differences.

In NumPy, an array can have one or more dimensions, and each dimension is referred to as an axis. Understanding axes in NumPy is crucial for performing operations like summation, multiplication, or finding differences across different dimensions of an array.

• Axis 0 is often considered the “rows” in a 2D array, but in higher dimensions, it represents the first dimension of the array. For a 3D array, it could be thought of as the different layers or depth.
• Axis 1 in a 2D array corresponds to the columns. In a 3D array, it’s the second dimension, which you can think of as rows across each layer.
• Axis 2 in a 3D array is the third dimension, analogous to the columns in each of the 2D arrays or layers.

When you perform an operation along an axis, NumPy aggregates over that axis. For example, if you calculate a sum along axis 0 in a 2D array, it sums down the columns (vertically). In a 3D array, calculating the difference along axis 0, as in the previous example, means it computes the difference between corresponding elements of the layers.

Where to Go From Here?

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