Array Statistics

1. Background Knowledge

NumPy arrays are the standard for efficient numerical computation in Python, enabling vectorized operations that avoid Python loops for high performance. Key statistical functions are built-in:

• Sum: $\sum_{i=1}^n x_i$
• Mean: $\mu =$\frac{1}{n}$$\sum_{i=1}$^n x_i$
• Min/Max: \min(x) = x_{\text{min}}, \max(x) = x_{\text{max}}
• Standard Deviation: \sigma = \sqrt{\frac{1}{n}$$\sum_{i=1}$^n (x_i -$\mu)^2} (population std by default in np.std)

Prerequisites: Basic NumPy array creation (np.array()), dictionary usage, and rounding (round(value, 2)).

2. Algorithm Approach

Use NumPy's optimized C-implemented functions for O(n) linear scans across the array—no custom loops needed. This leverages array programming paradigm: single function calls compute statistics via vectorization, minimizing operation counts and memory copies.

Direct mapping:

3. Step-by-Step Strategy

1. Import NumPy: import numpy as np
2. Define function: def array_stats(arr):
3. Compute statistics using built-in functions
4. Round mean/std: round(np.mean(arr), 2) and round(np.std(arr), 2)
5. Return dictionary: return {"sum":..., "mean":..., "min":..., "max":..., "std":...}