➖ 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].