parqv

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Supported File Formats: ✅ Parquet | ✅ JSON / JSON Lines (ndjson) | (More planned!)

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parqv is a Python-based interactive TUI (Text User Interface) tool designed to explore, analyze, and understand various data file formats directly within your terminal. Initially supporting Parquet and JSON, parqv aims to provide a unified, visual experience for quick data inspection without leaving your console.

💻 Demo

(Demo shows Parquet features; UI adapts for other formats)

🤔 Why parqv?

1. Unified Interface: Launch parqv <your_data_file> to access metadata, schema, data preview, and column statistics all within a single, navigable terminal window. No more juggling different commands for different file types.
2. Interactive Exploration:
   • 🖱️ Keyboard & Mouse Driven: Navigate using familiar keys (arrows, hjkl, Tab) or even your mouse (thanks to Textual).
   • 📜 Scrollable Views: Easily scroll through large schemas, data tables, or column lists.
   • 🌲 Clear Schema View: Understand column names, data types, and nullability at a glance. (Complex nested structures visualization might vary by format).
   • 📊 Dynamic Stats: Select a column and instantly see its detailed statistics (counts, nulls, min/max, mean, distinct values, etc.).
3. Cross-Format Consistency:
   • 🎨 Rich Display: Leverages rich and Textual for colorful, readable tables and text across supported formats.
   • 📈 Quick Stats: Get key statistical insights consistently, regardless of the underlying file type.
   • 🔌 Extensible: Designed with a handler interface to easily add support for more file formats in the future (like CSV, Arrow IPC, etc.).

✨ Features (TUI Mode)

• Multi-Format Support: Currently supports Parquet (.parquet) and JSON/JSON Lines (.json, .ndjson). Run parqv <your_file.{parquet,json,ndjson}>.
• Metadata Panel: Displays key file information (path, format, size, total rows, column count, etc.). Fields may vary slightly depending on the file format.
• Schema Explorer:
  • Interactive list view of columns.
  • Clearly shows column names, data types, and nullability.
• Data Table Viewer:
  • Scrollable table preview of the file's data.
  • Attempts to preserve data types for better representation.
• Column Statistics Viewer:
  • Select a column in the Schema tab to view detailed statistics.
  • Shows counts (total, valid, null), percentages, and type-specific stats (min/max, mean, stddev, distinct counts, length stats, boolean value counts where applicable).
• Row Group Inspector (Parquet Specific):
  • This panel only appears when viewing Parquet files.
  • Lists row groups with stats (row count, compressed/uncompressed size).
  • (Planned) Select a row group for more details.

🚀 Getting Started

1. Prerequisites:

• Python: Version 3.10 or higher.
• pip: The Python package installer.

2. Install parqv:

• Open your terminal and run:

  pip install parqv

  (This will also install dependencies like textual, pyarrow, pandas, and duckdb)
• Updating parqv:

  pip install --upgrade parqv

3. Run parqv:

• Point parqv to your data file:

  #parquet
  parqv /path/to/your/data.parquet
  
  # json
  parqv /path/to/your/data.json

• The interactive TUI will launch. Use your keyboard (and mouse, if supported by your terminal) to navigate:
  • Arrow Keys / j,k (in lists): Move selection up/down.
  • Tab / Shift+Tab: Cycle focus between the main tab content and potentially other areas. (Focus handling might evolve).
  • Enter (in column list): Select a column to view statistics.
  • View Switching: Use Ctrl+N (Next Tab) and Ctrl+P (Previous Tab) or click on the tabs (Metadata, Schema, Data Preview).
  • Scrolling: Use PageUp / PageDown / Home / End or arrow keys/mouse wheel within scrollable areas (like Schema stats or Data Preview).
  • q / Ctrl+C: Quit parqv.
  • (Help Screen ? is planned)

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📄 License

Licensed under the Apache License, Version 2.0. See LICENSE for the full license text.