I’m building a proof-of-concept that turns raw population figures stored in our SQL database into clear, insightful visuals. The dataset is large—think country-by-country numbers spanning decades—so I need a Python workflow that can pull the data directly from the database, process it efficiently, and output the first round of graphics: • Bar charts to compare populations across regions or years • Pie charts for proportional breakdowns (e.g., age groups) • Histograms to show distribution patterns such as urban vs. rural counts If you prefer Matplotlib, Seaborn, Plotly, or another well-supported library, feel free to choose the best fit; interactive elements are a plus but not mandatory at this stage. Lean, well-commented code is essential, along with a short README that explains environment setup, database connection parameters, and how to reproduce the visuals with a sample query. Deliverables 1. Python script or Jupyter notebook that connects to the database and generates the three listed chart types from real data. 2. Exported static images (PNG or SVG) for quick review. 3. README/setup guide. Acceptance criteria: the script must handle multi-million-row tables without crashing and complete a full visualization pass in a reasonable timeframe on a standard workstation. Once the prototype proves stable, we can expand it to extra chart types such as line graphs or heat maps, but today’s focus is squarely on bar, pie, and histogram views of population data.