Topic 13. Numerical computing, data analysis, and visualization with NumPy, pandas, and Matplotlib

Goal: learn to perform vectorized calculations, validate and aggregate tabular data, build labeled plots, and test a reproducible analysis pipeline with pytest.

Lecture contents

1. Tools and NumPy arrays — Tools and reproducibility · The ndarray array: values, type, and shape
2. Vectorized operations and slices — Vectorized operations, axes, and masks · Slices, copies, and broadcasting
3. Pandas tables and missing data — Pandas tables: labels and positions · Input, missing values, and the data contract
4. Grouping, time series, and plots — Grouping, merging, and time series · Matplotlib plots
5. Program examples and common mistakes — Program examples · Testing the pipeline and common mistakes

Practice and review

• Practice — 3 worked examples with complete code and output
• Tasks — 30 variants, each with tasks at three difficulty levels
• Summary — conclusions, self-check questions, and useful links