How alimov2008/pharma-sales-data-analysis---roadmap.sh-project works
This is a notebook-based pharmaceutical sales analysis. The user runs the notebook against the historical sales CSV to explore category sales, identify top-selling drugs for periods, examine 2017 sales frequency, compare category averages, and analyze monthly R03 sales. Pandas and Matplotlib are documented analysis tools; the notebook source was not sampled, so specific implementation steps and data-flow details cannot be verified.
Main entry points: pharmaceutical_sales.ipynb Sales notebook · salesdaily.csv Daily sales CSV
Components 11
Sales analysis
- Sales notebook
pharmaceutical_sales.ipynb - Category sales
pharmaceutical_sales.ipynb - Top drugs
pharmaceutical_sales.ipynb - 2017 frequency
pharmaceutical_sales.ipynb - Daily averages
pharmaceutical_sales.ipynb - R03 monthly sales
pharmaceutical_sales.ipynb - Analysis results
pharmaceutical_sales.ipynb
Input data
- Daily sales CSV
data/salesdaily.csv
Analysis tools
- Pandas
- Matplotlib
Other components
- Analyst
Connections 10
- Analyst → Sales notebook: runs
- Sales notebook → Daily sales CSV: reads
- Sales notebook → Pandas: uses
- Sales notebook → Matplotlib: uses
- Sales notebook → Category sales: analyzes
- Sales notebook → Top drugs: identifies
- Sales notebook → 2017 frequency: examines
- Sales notebook → Daily averages: compares
- Sales notebook → R03 monthly sales: analyzes
- Sales notebook → Analysis results: produces