NumPy Summations

➕ NumPy Summations

NumPy provides fast and efficient functions to calculate summations over arrays. These are vectorized operations, much faster than Python loops, and can operate along specific axes in multi-dimensional arrays.

 1. Sum of All Elements (np.sum)

• Computes the sum of all elements in the array

 2. Sum Along an Axis (2D Array)

• axis=0 → sum down the rows (column-wise)

• axis=1 → sum across columns (row-wise)

 3. Cumulative Sum (np.cumsum)

• Computes a running total of elements

 4. Sum with Conditions

• Use Boolean indexing to sum elements satisfying a condition

 5. Notes & Tips

• np.sum works for 1D, 2D, or ND arrays

• Axis parameter is important for dimension-specific summation

• np.cumsum is useful in time series, cumulative totals, or statistics

🎯 Practice Exercises

1. Compute the sum of all elements in a 3×3 array.

2. Compute row-wise and column-wise sums for a 2×4 array.

3. Compute the cumulative sum of [5, 10, 15, 20].

4. Sum only the odd numbers in [1,2,3,4,5,6,7,8,9].