finance-manager

You can extract transactions from bank statement PDFs using pdfplumber to pull transaction tables into structured data. This enables subsequent category breakdowns, spending analysis, and financial report generation from your raw bank records.

From CSV transaction data, you can calculate income, expenses, savings rate, and category breakdowns. The analysis uses pandas-based parsing to produce trend metrics, summary metrics, and monthly budgeting insights.

Yes, you can generate interactive HTML financial reports from JSON transaction files. The process outputs charts, recommendations, and monthly comparisons, turning raw JSON transaction data into clear financial insights and visualizations.

Financial analysis works with CSV exports, JSON transaction files, and PDF bank statements. The parsing logic handles multiple formats to produce consistent spending insights, category breakdowns, and trend analysis across all supported inputs.

Analyze monthly spending patterns by calculating category breakdowns and trend metrics from transaction data. This identifies the biggest expense categories and generates recommendations, producing a polished report for review.

Python with pandas and pdfplumber is required for transaction extraction and analysis. These dependencies enable parsing bank statements, calculating savings rates, and generating interactive HTML charts for budgeting and financial reporting.