NumPy Differences

➖ NumPy Differences (np.diff)

The NumPy diff() function calculates the difference between consecutive elements in an array. It is useful for numerical differentiation, trend analysis, and signal processing.

 1. Basic np.diff() on 1D Array


 

  • Computes: arr[i+1] - arr[i]

  • Result array length = original length - 1

2. Difference of Higher Order

  • Use n parameter to compute higher-order differences


 

  • First-order difference: [2,3,4]

  • Second-order difference: [3-2,4-3] = [1,1]

 3. Differences Along an Axis (2D Array)


 

  • axis=0 → differences down rows

  • axis=1 → differences across columns

 4. Applications

  • Numerical derivatives in scientific computing

  • Velocity / acceleration from position arrays

  • Change detection in signals or stock prices



 

 5. Notes & Tips

  • Output length = original length - n

  • Use np.gradient() if you need centered differences for derivative approximation

  • Works with 1D, 2D, or ND arrays

🎯 Practice Exercises

  1. Compute first and second-order differences of [2, 5, 9, 14, 20].

  2. Create a 2×3 array and compute differences along both axes.

  3. Compute the daily change of stock prices [100, 102, 105, 107, 110].

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