Python • NZ Business Financials

Overview

From raw CSV to clear visuals in minutes. This project cleans the Stats NZ data, builds focused subsets, and outputs PNG charts for your portfolio.

Data

business_financial_data.csv
Quarterly NZ business financials — sales (operating income), operating profit, assets, inventories and more, broken down by industry.

Stack

Python 3 • pandas • matplotlib • seaborn • matplotlib.ticker

Outputs

High‑res PNGs exported by the script:
financial_correlation_heatmap.png, growth_profit_matrix.png, industry_profitability_scorecard.png, industry_sales_volatility.png, key_sector_sales_trends.png, manufacturing_profit_margin.png, post_covid_recovery.png, profit_contribution_over_time.png, sector_seasonality.png, top_industries_by_profit.png, top_industries_by_sales.png, yoy_sales_growth.png

How to run locally

Open the folder in VS Code.
Ensure the CSV is named business_financial_data.csv and sits next to the Python file.

Create a virtual env (optional) and install deps:

python -m venv .venv
. .venv/bin/activate    # (Mac/Linux)
# .venv\Scripts\activate  # (Windows)
pip install pandas matplotlib seaborn

Run the script:

python "try 1 gemini.py"    # uses the provided filename
# or rename to main.py and run
# python main.py

PNG charts will be saved in the project folder. Move them into images/projects/python/ to show on this page.

Tip: commit both the code and images/projects/python/ folder to GitHub Pages so the visuals appear on your live site.

Gallery (auto‑generated charts)

Key highlights

Clean, typed time series (e.g., period to datetime) and numeric casting with safe coercion.
Focused subsets: Sales vs Profit vs Assets vs Inventories; key sectors like Manufacturing & Retail.
Consistent chart styling, proper currency tick formatting and readable legends.

Scorecards for profit margin & contribution; recovery comparison (pre/post‑COVID); efficiency (asset turnover).
Portable: produces static PNGs ready for portfolio pages like this.
VS Code + GitHub friendly: simple run, commit, and publish on GitHub Pages.

Code

Below is a trimmed header so recruiters can preview the project. The full code lives in the repository.

# analyze_business_financials() — loads the CSV, cleans columns & types,
# creates sales/profit/assets/inventories subsets, and exports 10+ charts.
# Stack: pandas, matplotlib, seaborn, matplotlib.ticker

if __name__ == "__main__":
    analyze_business_financials()