TablePilot

English | 中文

TablePilot is a local-first desktop workbench for turning Excel, CSV, and TXT tables into explainable data profiles, quality scores, analysis plans, and visual summaries.

It modernizes an older Qt statistical analysis project into a hybrid C++/Python portfolio project: the desktop experience is built with Qt/C++, while a Dockerized FastAPI service handles parsing, profiling, planning, report generation, and API-level tests.

Why This Project Exists

Most small spreadsheet utilities assume a fixed table shape or a single demo file. TablePilot is designed for messier real-world files:

• different row and column counts
• Excel, CSV, and TXT inputs
• delimiter detection for text-like tables
• schema inference instead of hard-coded columns
• quality scoring before analysis
• explainable analysis planning instead of opaque output
• Chinese/English desktop UI

Product Name

TablePilot means a co-pilot for tabular data. It does not try to replace a human analyst; it helps users load unfamiliar tables, understand their structure, spot quality risks, and choose the next useful analysis step.

Highlights

• Dynamic table parsing for Excel, CSV, and TXT files.
• Schema inference for numeric, date, category, text, empty, and high-cardinality fields.
• Data quality scoring using missing values, duplicates, anomalies, sample size, and analyzability.
• Analysis Planner that recommends next steps based on schema, trends, correlations, and anomalies.
• Desktop-native Qt UI with dynamic preview tables, charts, profile cards, and a bilingual insight panel.
• Local-first architecture: the Python analysis service runs locally through Docker.
• Deterministic agent-style API for question answering over loaded datasets.
• CI coverage for parser behavior, API endpoints, Docker build, and package metadata.
• Windows release script for building a distributable desktop package.

Architecture

Excel / CSV / TXT
      |
      v
Qt/C++ Desktop Client
      |
      v
FastAPI Analysis Service
      |
      +--> Parser and schema inference
      +--> Quality scoring
      +--> Trends, correlations, anomalies
      +--> Analysis planner
      +--> Markdown report and agent-style answers

Repository Structure

Statistical_Analysis/          Qt/C++ desktop client
analysis_service/              FastAPI analysis service
analysis_service/tests/        Backend tests and fixtures
config/                        Product config and bilingual UI copy
demo/                          Demo workbook for manual review
packaging/                     Windows release scripts
site/                          GitHub Pages showcase
qss/                           Desktop theme

Quick Start

Start the local analysis service:

docker compose up --build

Open the desktop project with Qt Creator:

Statistical_Analysis/Statistical_Analysis.pro

Recommended kit:

Qt 6.11.1 MinGW 64-bit

Demo workbook:

demo/tablepilot_demo_sales.xlsx

API Smoke Test

Invoke-RestMethod http://127.0.0.1:8000/health

Invoke-RestMethod `
  -Method Post `
  -Uri http://127.0.0.1:8000/api/analyze `
  -ContentType "application/json" `
  -Body '{"filename":"tablepilot_demo_sales.xlsx"}'

Tests

python -m pip install -r analysis_service/requirements-dev.txt
$env:PYTHONPATH = "$PWD\analysis_service"
python -m pytest -q analysis_service\tests

Windows Release Package

powershell -ExecutionPolicy Bypass -File packaging\build-windows-release.ps1

Output:

dist/TablePilot.zip

The package includes the Qt executable, Docker Compose file, analysis service, demo data, config files, and release notes.

Configuration and Localization

Product-level settings live in:

config/app.json

Bilingual copy lives in:

config/i18n.en.json
config/i18n.zh-CN.json

The current desktop UI already supports Chinese/English switching. These config files make the project easier to maintain as the UI copy grows.

Branches

• main: current modern TablePilot version.
• legacy-original-qt: preserved original Qt statistical analysis version.

Roadmap

• Add drag-and-drop welcome screen and recent files.
• Add user-selectable chart dimensions and chart types.
• Add structured report export from the desktop UI.
• Add screenshot-backed project page after final UI polish.
• Add GitHub Release automation for Windows packages.

Disclaimer

TablePilot produces analytical summaries for learning and portfolio demonstration. It is not a business, financial, or operational recommendation system.