Python for Numerical and Tabular Work

Create a reproducible Python project environment with pinned dependencies and a notebook kernel.

Implement numerical computations with vectorized NumPy array operations instead of Python loops.

Manipulate NumPy array shapes, axes, indexing, and broadcasting without dimension errors.

Load and inspect structured datasets with pandas to identify schema and quality issues.

Clean missing, duplicated, and invalid values in a pandas DataFrame.

Combine and summarize tabular data with pandas joins, grouping, and aggregation.

Visualize numerical distributions and feature relationships with appropriate statistical plots.

Refactor notebook-based data preparation into a reusable Python function and script.