Store Sales Analysis

A comprehensive Python project for analyzing store sales data to track performance, calculate profits, and generate actionable insights. This system integrates a billing module, inventory management, and advanced analytics to streamline business operations and produce reproducible reports and dashboards.

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Table of Contents

• Why this project
• Key features
• Repository layout
• Quick start
• Data format and expectations
• Configuration
• How to run
  • Command-line scripts
  • Programmatic usage (Python API)
  • Notebooks & exploration
• Outputs
• Testing & CI
• Development notes & architecture
• Contributing
• License & contact
• Troubleshooting

Why this project

Store Sales Analysis provides a structured, reproducible pipeline for taking raw sales and inventory data and transforming it into:

• KPIs (revenue, gross profit, margin, units sold)
• Store- and product-level performance metrics
• Inventory turn analysis and restock recommendations
• Billing reconciliation and anomaly detection
• Visual reports and exportable CSV / Excel summaries

It is intended for small-to-medium retail operations, analysts, and data engineers who need a straightforward, customizable solution.

Key features

• Modular design: billing, inventory, analytics modules separated for clarity
• Data validation and cleaning utilities
• Config-driven pipelines (YAML/.env)
• CLI scripts and Python API for automation
• Jupyter notebooks for EDA and reporting
• Unit tests and CI-friendly structure

Repository layout

A recommended project layout (your actual repo may vary slightly):

• README.md
• LICENSE
• requirements.txt
• pyproject.toml (optional)
• config/
  • config.example.yaml
• data/
  • raw/ # place raw CSV/Parquet files here
  • processed/
  • sample_dataset.csv
• src/
  • store_sales_analysis/
    • init.py
    • cli.py
    • pipeline.py
    • billing/
      • init.py
      • reconcile.py
      • billing_utils.py
    • inventory/
      • init.py
      • stock.py
      • reorder.py
    • analytics/
      • init.py
      • kpis.py
      • cohort_analysis.py
    • io/
      • readers.py
      • writers.py
    • utils/
      • validation.py
      • logging.py
• notebooks/ # EDA and demo notebooks
• scripts/ # helper scripts (e.g. run_all.sh)
• tests/ # pytest tests
• docs/ # optional documentation

Quick start

Prerequisites:

• Python 3.8+
• Git

1. Clone the repo

   • git clone https://github.com/DishiGpt/store-sales-analysis.git
   • cd store-sales-analysis

2. Create and activate a virtual environment

   • python -m venv venv
   • source venv/bin/activate # Linux / macOS
   • venv\Scripts\activate # Windows

3. Install dependencies

   • pip install -r requirements.txt Or using poetry:
   • poetry install

4. Copy and edit configuration

   • cp config/config.example.yaml config/config.yaml
   • Edit paths / options to match your environment.

Data format and expectations

The pipeline expects tabular sales data (CSV, Parquet) with (at minimum) the following columns:

• date (YYYY-MM-DD or ISO datetime)
• store_id
• product_id
• quantity
• unit_price
• cost (optional but recommended)
• discount (optional)
• tax (optional)
• transaction_id (for billing/reconciliation)
• payment_type (optional)

Recommended sample header: date,store_id,product_id,transaction_id,quantity,unit_price,cost,discount,tax,payment_type

If you have inventory snapshots, include:

• snapshot_date, store_id, product_id, stock_level

Configuration

Main configuration is stored in YAML (config/config.example.yaml). Example keys:

• data:
  • raw_dir: data/raw
  • processed_dir: data/processed
• reports:
  • output_dir: reports
• analytics:
  • kpi_window_days: 30
  • profit_margin_threshold: 0.1
• inventory:
  • reorder_point_days: 14
• logging:
  • level: INFO

How to run

Command-line scripts

• Run full pipeline (example):
  • python -m store_sales_analysis.cli run --config config/config.yaml
• Run only analytics:
  • python -m store_sales_analysis.cli analytics --data data/processed/sales.parquet --out reports/kpis.csv
• Reconcile billing:
  • python -m store_sales_analysis.cli billing --transactions data/raw/transactions.csv --payments data/raw/payments.csv --out reports/reconciliation.xlsx

Programmatic usage (Python API)

• Import core functions from package:

from store_sales_analysis.pipeline import run_full_analysis

run_full_analysis(
    raw_data_dir="data/raw",
    processed_dir="data/processed",
    reports_dir="reports",
    config_path="config/config.yaml"
)

• Compute KPIs for a DataFrame:

from store_sales_analysis.analytics.kpis import compute_kpis
kpi_df = compute_kpis(sales_df, group_by=["store_id", "product_id"])

Notebooks & exploration

Notebooks in /notebooks are intended for EDA and interactive reporting. Launch with:

• jupyter lab
• or open specific notebook: notebooks/edA_sales_analysis.ipynb

Outputs

Typical outputs the pipeline produces:

• reports/kpis_YYYYMMDD.csv
• reports/profit_summary.xlsx
• reports/inventory_reorder_list.csv
• visualizations: reports/figures/sales_by_store.png
• dashboards (if integrated): a generated HTML report or JSON used by a front-end

Testing & CI

• Unit tests in /tests use pytest.
• Run tests locally:
  • pytest -q
• Suggested CI:
  • GitHub Actions workflow that runs linting, unit tests, and optionally builds docs.

Development notes & architecture

• Modular design isolates billing, inventory, analytics responsibilities.
• Data ingestion -> validation -> transformation -> feature engineering -> reporting.
• Use vectorized pandas operations (or Dask for larger-than-memory).
• Logging and structured exceptions are centralized in src/store_sales_analysis/utils.

Example high-level architecture (ASCII):

Raw data -> Readers -> Cleaner/Validator -> Feature Engineering -> Analytics (KPIs, Cohorts) -> Reports / Dashboards -> Billing reconciliation -> Alerts

Contributing

Contributions welcome — please follow these steps:

1. Fork the repository
2. Create a branch: git checkout -b feat/your-feature
3. Write tests for new functionality
4. Follow code style (black, flake8)
5. Open a pull request with a clear description of changes

Please include an issue for larger features before implementing.

License & contact

This repository is provided under the MIT license (see LICENSE). If you want a different license, update LICENSE accordingly.

For questions or help, open an issue or contact maintainer: DishiGpt (GitHub: @DishiGpt).

Troubleshooting

• Common issues:

  • FileNotFoundError: check paths in config.yaml and ensure data files are in data/raw
  • Pandas parsing error: check delimiter and encoding of CSVs (utf-8 recommended)
  • Memory errors: downsample data or use chunked readers / Dask

• Debug tips:

  • Increase logging to DEBUG in config for detailed traces
  • Use smaller sample datasets in data/raw/sample_dataset.csv to iterate faster

Acknowledgements & next steps

• This project is a solid base for building reporting pipelines, retail dashboards, or automated restock systems.
• Next improvements to consider: real-time ingestion, database-backed storage (Postgres), or a web dashboard (Streamlit/Plotly Dash).

Thank you for using Store Sales Analysis — contributions, issues, and suggestions are encouraged!