04-02: Student Score Analyzer (NumPy)¶

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Analyses a 10×5 array of student scores using NumPy 2D operations. Produces per-student averages/grades/ranks, per-subject statistics, min-max normalisation, and failure detection — all without Python loops.

Topics covered: np.mean(axis=0/1) · np.where · np.argsort · np.sum (boolean) · 2D boolean indexing · reshape · min-max normalisation · np.argmin/argmax

Difficulty: ⭐⭐⭐ Intermediate

Requirements¶

pip install numpy

How to Run¶

python main.py

Sample Report (truncated)¶

  Name       Math  English  Science  History  ICT   Avg  Grade  Rank  Fail
  ─────────────────────────────────────────────────────────────────────────
  Riya         95       92       98       94   97  95.2  A+       1     0
  Nasrin       92       88       95       91   93  91.8  A+       2     0
  Salma        40       38       45       50   42  43.0  F        10    5 ⚠️

Key Concepts Practised¶

Concept	Where used
`np.mean(scores, axis=1)`	Per-student averages
`np.mean(scores, axis=0)`	Per-subject means
`np.where(cond, true, false)`	Vectorised grade labels
`np.argsort()[::-1]`	Descending rank
`scores < 50` (boolean mask)	Failure detection
`(x - min) / (max - min) * 100`	Min-max normalisation