Inventory Trend Analysis in Python

I’d like a data-savvy partner who can dive into our raw inventory history and surface clear, actionable trends. The sole objective is to analyze historical data—specifically inventory data—so our team can understand how stock levels have moved over time and where the pressure points sit.

Here’s what I have: several years of SKU-level records housed in a SQL database, plus a few CSV extracts. I need you to pull and clean the data, run robust trend analysis in Python (Pandas, NumPy, preferably some Matplotlib or Seaborn visuals), and package the insights so they are easy for our planners to act on. Time-series forecasting or demand planning frameworks aren’t required right now, but please structure the code so we could layer those in later if needed.

Deliverables:
• Well-documented Python notebook or script that connects to our SQL source, performs the trend analysis, and generates clear charts/tables.
• A concise slide deck or PDF summary highlighting key inventory trends, anomalies, and suggested next steps.
• A short hand-off call or Loom walkthrough to explain your methodology and how we can reproduce or extend it.

Accuracy, clean code, and business-ready visuals will be my acceptance criteria. If this sounds straightforward to you, let’s get started.