Build student mark analyzers, sales performance engines, and Monte Carlo simulators.
Synthesize everything learned across the 50 topics into a comprehensive End-to-End Sales & Performance Analyzer combining broadcasting, axis reductions, boolean masking, and statistical summaries.
You are now the master architect: you can take any raw numerical dataset and process millions of records with pure, vectorized, lightning-fast NumPy code.
Real-world data science systems combine creation, slicing, boolean masks, axis reductions, and statistical rankings into clean vectorized pipelines.
# Full Vectorized Pipeline Mastery# Writing Python loops in production data applications# Pure vectorized NumPy pipelinesAlways default to NumPy vectorization for data transformation and analytics tasks.
Solve the objective below using NumPy vectorized syntax
Given `sales = np.array([100, 250, 400])`, compute total sum with `sales.sum()`, average with `sales.mean()`, and print both in a list `[sales.sum(), sales.mean()]`.
[750, 250.0]