Sectoral Analysis

A collection of tools, notebooks, and scripts for exploratory and reproducible sectoral analysis of economic/industry data using Python. This repository provides data ingestion, cleaning, visualization, and basic modeling utilities to analyze differences and trends across economic sectors.

Contents

• Notebooks for interactive exploration and reproducible reports
• Scripts for data ingestion, preprocessing, and automated analyses
• Utilities for plotting, metrics, and sector-level aggregation
• Example configuration and sample datasets (placeholders)

Features

• Standardized pipeline for loading and cleaning sectoral datasets
• Aggregation tools to compute sector-level metrics (growth, share, volatility)
• Visualization helpers (time series, stacked area charts, heatmaps)
• Example notebooks demonstrating common analyses and reproducible workflows

Getting started

Prerequisites

• Python 3.9+ recommended
• git

Quick setup (recommended: use a virtual environment)

python -m venv .venv
source .venv/bin/activate     # macOS / Linux
.venv\Scripts\activate        # Windows
pip install --upgrade pip
pip install -r requirements.txt

If there is no requirements.txt yet, typical dependencies include:

• pandas
• numpy
• matplotlib or seaborn
• plotly (optional for interactive plots)
• jupyterlab or notebook (for notebooks)

Data

This repository does not include raw proprietary datasets. Add datasets to the data/ directory or configure data paths in config/.

Suggested structure:

data/
  raw/                # original source files (CSV, Excel, etc.)
  processed/          # cleaned, transformed datasets used by scripts/notebooks
notebooks/             # exploratory and reporting notebooks
scripts/               # CLI-style scripts to run parts of the pipeline
src/                   # reusable Python modules and utilities
config/                # example config or environment files
reports/               # exported figures and report artifacts

Data conventions

• Each dataset should include a sector identifier (e.g., sector, industry_code, or NAICS) and a consistent time column (e.g., year, date).
• Where possible, provide a small example CSV in data/example/ to illustrate expected column names and formats.

Usage

Run an example notebook

1. Start Jupyter Lab / Notebook:

jupyter lab

2. Open notebooks/01-sector-overview.ipynb (example)

Run a script (example)

python scripts/aggregate_by_sector.py --input data/raw/example.csv --output data/processed/aggregated.csv

Common tasks included

• Aggregating time series by sector
• Calculating sector shares and growth rates
• Generating comparative visualizations across sectors
• Exporting cleaned datasets for modeling

Examples

• notebooks/01-sector-overview.ipynb — interactive overview of sectoral composition and trends
• scripts/plot_sector_trends.py — produce time-series plots for selected sectors
• src/analysis/metrics.py — functions to compute compound annual growth rate (CAGR), market share, volatility, etc.

Development & contributing

Contributions are welcome. Suggested workflow:

1. Fork the repository
2. Create a feature branch: git checkout -b feat/describe-change
3. Add tests (if applicable) and update documentation
4. Open a pull request describing your change

Coding standards

• Follow PEP8 for Python code
• Keep functions small and focused
• Add docstrings for public functions and modules

Issue reporting

• Open issues for bugs, feature requests, or data-format proposals
• Include a minimal reproducible example and expected vs actual behavior

Tests

If tests are added, run them with:

pytest

License

This project is licensed under the Apache License 2.0. See the LICENSE file for details.

Contact

Repository owner: mads5

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If you want, I can:

• Add example notebooks and a small sample dataset under data/example/
• Generate a requirements.txt with pinned versions
• Create a basic scripts/ scaffold (CLI) and a simple unit test Tell me which you'd like next and I'll produce the files.