Say you have a list of numbers and you’re interested in the difference between each number and the one before it. For instance, with [5, 10, 15]<\/strong>, you’d like to know there’s a 5<\/strong>-unit increase between 5<\/strong> and 10<\/strong>, and another 5<\/strong>-unit increase between 10 <\/strong>and 15<\/strong>. Numpy Diff handles this task swiftly and effectively.<\/p>\n\n\n\n Following is the Syntax of this function:<\/p>\n\n\n Now let’s learn about what each parameter means:<\/p>\n\n\n\n Now that we know about the function and its parameters, let’s try to implement the concept we learned and calculate differences in data. Here are some examples:<\/p>\n\n\n\n First, let’s see a simple implementation on 1-D array data.<\/p>\n\n\n Output:<\/strong><\/p>\n\n\n\n This code finds the differences between consecutive elements in the array using NumPy’s diff function. It then prints both the original array and the resulting differences.<\/p>\n\n\n\n Now let’s see if we can use the same function with multidimensional data.<\/p>\n\n\n When axis=0<\/strong>, it means we are calculating differences along the vertical axis<\/strong>, which implies differences between consecutive rows<\/strong>. For example, the difference between the first row [1, 2, 3]<\/strong> and the second row [4, 4, 6]<\/strong> would be [4-1, 4-2, 6-3]<\/strong>, which is [3, 2, 3]<\/strong>. Likewise, the difference between the second row and the third row would be [7-4, 8-4, 10-6]<\/strong>, resulting in [3, 4, 4].<\/strong><\/p>\n\n\n\n And when axis=1<\/strong>, differences are calculated horizontally<\/strong>, meaning between consecutive columns<\/strong>. For example, the difference between the first column [1, 4, 7]<\/strong> and the second column [2, 4, 8] <\/strong>would be [2-1, 4-4, 8-7],<\/strong> resulting in [1, 0, 1]<\/strong>. Similarly, the difference between the second column and the third column would be [3-2, 6-4, 10-8]<\/strong>, resulting in [1, 2, 2]<\/strong>.<\/p>\n\n\n\n Output:<\/strong><\/p>\n\n\n\n We can also calculate differences according to orders and recursion. In this example, let’s try to calculate the differences in different orders: first, second, and third. We will achieve this using the n<\/strong> parameter.<\/p>\n\n\n We’re finding the differences between neighbouring numbers in the array arr<\/strong> diff_first_order<\/strong> stores these differences. Then, diff_second_order<\/strong> finds differences between those differences, and diff_third_order<\/strong> calculates differences again. This helps us understand how the values change over time and their patterns.<\/p>\n\n\n\n Output:<\/strong><\/p>\n\n\n\n Now, if you don’t wish to use the NumPy diff<\/strong> function to calculate the difference, there is an easy alternative. You can simply calculate the difference using some logic. Observe the given code closely:<\/p>\n\n\n Here we find the differences between consecutive elements in the array arr<\/strong> by subtracting each element from the next one. This generates a new array named differences<\/strong>, storing these calculated differences. Finally, we print both the original array and the differences array.<\/p>\n\n\n\n So that’s it for this article. I hope you now have a good understanding of the topics discussed. We covered the ‘diff<\/strong>‘ function in detail, including its parameters and implementations. The examples provided were easy and simple, I hope you found them helpful. Additionally, we discussed another option to the ‘diff’ function. Now it’s your time to try them out and use them yourself.<\/p>\n\n\n\n Further Reading:<\/em><\/strong><\/p>\n\n\n\n![\"Introduction](\"https:\/\/codeforgeek.com\/wp-content\/uploads\/2024\/04\/illustration-5.png\")
<\/figure>\n\n\n\nSyntax and Parameters<\/strong><\/h3>\n\n\n\n
\nnumpy.diff(a, n=1, axis=-1)\n<\/pre><\/div>\n\n\n
\n
Examples of NumPy.diff()<\/h2>\n\n\n\n
Example 1<\/h3>\n\n\n\n
\nimport numpy as np\n\narr = np.array([0, 5, 10, 15, 20])\ndifferences = np.diff(arr)\nprint("Original array:", arr)\nprint("1-Dimensional Differences array:", differences)\n<\/pre><\/div>\n\n\n![\"Example](\"https:\/\/codeforgeek.com\/wp-content\/uploads\/2024\/04\/output1-3.png\")
<\/figure>\n\n\n\nExample 2<\/h3>\n\n\n\n
\nimport numpy as np\n\narr = np.array([[1, 2, 3],\n [4, 4, 6],\n [7, 8, 10]])\ndiff_r = np.diff(arr, axis=0)\ndiff_c = np.diff(arr, axis=1)\nprint("Original 2D array:")\nprint(arr)\nprint("\\nDifferences along rows (axis=0):")\nprint(diff_r)\nprint("\\nDifferences along columns (axis=1):")\nprint(diff_c)\n<\/pre><\/div>\n\n\n![\"Example](\"https:\/\/codeforgeek.com\/wp-content\/uploads\/2024\/04\/illustration2-5.png\")
<\/figure>\n\n\n\n![\"Example](\"https:\/\/codeforgeek.com\/wp-content\/uploads\/2024\/04\/illustration3-3.png\")
<\/figure>\n\n\n\n![\"Example](\"https:\/\/codeforgeek.com\/wp-content\/uploads\/2024\/04\/output2-4.png\")
<\/figure>\n\n\n\nExample 3<\/h3>\n\n\n\n
\nimport numpy as np\n\narr = np.array([5, 7, 13, 18, 21])\ndiff_first_order = np.diff(arr)\ndiff_second_order = np.diff(arr, n=2)\ndiff_third_order = np.diff(arr, n=3)\n\nprint("Original array:", arr)\nprint("First-order differences array:", diff_first_order)\nprint("Second-order differences array:", diff_second_order)\nprint("Third-order differences array:", diff_third_order)\n<\/pre><\/div>\n\n\n![\"Illustration](\"https:\/\/codeforgeek.com\/wp-content\/uploads\/2024\/04\/illustration5-3.png\")
<\/figure>\n\n\n\n![\"Example](\"https:\/\/codeforgeek.com\/wp-content\/uploads\/2024\/04\/output3-4.png\")
<\/figure>\n\n\n\nSimple Alternatives to NumPy.diff()<\/h2>\n\n\n\n
\narr = [2, 5, 7, 13, 20]\ndifferences = [arr[i+1] - arr[i] for i in range(len(arr)-1)]\nprint("Original array:", arr)\nprint("Differences array:", differences)\n<\/pre><\/div>\n\n\nConclusion<\/strong><\/h2>\n\n\n\n