PolarsView

A fast and interactive viewer for CSV, JSON (including Newline-Delimited JSON - NDJSON), and Apache Parquet files, built with Polars and egui.

This project is inspired by and initially forked from the parqbench project.

Features

• Fast Data Handling: Uses the Polars DataFrame library for efficient data loading, processing, and querying.
• Multiple File Format Support:
  • Load data from: CSV, JSON, NDJSON (Newline-Delimited JSON), Parquet.
  • Save data as: CSV, JSON, NDJSON, Parquet (via "Save As..." [Ctrl+A]).
• Interactive Table View:

  • Supports sorting by multiple columns simultaneously: Click column header icons to sort the entire DataFrame asynchronously. The order of clicks determines sort precedence. The 5-state cycle for each column controls direction and null placement:

    • ↕: Not Sorted
    • ⏷: Descending, Nulls First
    • ⏶: Ascending, Nulls First
    • ⬇: Descending, Nulls Last
    • ⬆: Ascending, Nulls Last
    • ↕: Back to Not Sorted

    (Numbers indicate sort precedence if multiple columns are sorted)

  • Customizable Header: Toggle visual style ("Enhanced Header"), adjust vertical padding ("Header Padding").

  • Column Sizing: Choose automatic content-based sizing ("Auto Col Width": true) or faster fixed initial widths ("Auto Col Width": false). Manually resize columns by dragging separators.

• SQL Querying: Filter and transform data using Polars' SQL interface. Execute queries asynchronously via the "Query" panel.
• String Column Number Normalization (CLI): Use the --apply-regex (-a) argument to select string columns (via wildcard * or a ^...$ regex pattern matching column names) containing European-style numbers (e.g., '1.234,56') and convert them to standard Float64 format (e.g., 1234.56) on load.
• Configuration Panels (Side Bar):
  • Info: Displays file dimensions (rows, columns).
  • Format: Set text alignment, float decimal places, column width strategy, header style, and header padding.
  • Query: Configure SQL query, add optional row index column (with custom name/offset), normalize columns, null column removal, schema inference rows (CSV/JSON/NDJSON), CSV delimiter, custom CSV null values, and view SQL examples.
  • Columns: Shows column names and Polars data types. Right-click a column name to copy it.
• Asynchronous Operations: Utilizes Tokio for non-blocking file I/O, sorting, and SQL execution, keeping the UI responsive. Shows a spinner during processing.
• Drag and Drop: Load files by dropping them onto the application window.
• Robust Error Handling: Displays errors (file loading, parsing, SQL, etc.) in a non-blocking notification window.
• Theming: Switch between Light and Dark themes via the menu bar.
• Persistence: Remembers window size and position between sessions.

Building and Running

1. Prerequisites:

   • Rust and Cargo (latest stable version recommended, minimum version 1.86 and edition 2024).

2. Clone the Repository:

   git clone https://github.com/claudiofsr/polars-view.git
   cd polars-view
   

3. Build and Install (Release Mode):

   # Build with default features (uses 'format-simple')
   cargo b -r && cargo install --path=.
   
   # --- OR Build with Specific Features ---
   # Example: Build with 'format-special' (formats 'Alíq'/'Aliq' columns differently)
   cargo b -r && cargo install --path=. --features format-special
   

   This compiles optimized code and installs the polars-view binary to ~/.cargo/bin/.

4. Run:

   polars-view [path_to_file] [options]
   

   • If [path_to_file] is provided (CSV, JSON, NDJSON, Parquet), it's loaded on startup.

   • Run polars-view --help for command-line options (-a/--apply-regex, --delimiter, -q, --table-name, --null-values, --remove-null-cols).

   • Logging/Tracing: Control log detail using the RUST_LOG environment variable (values: error, warn, info, debug, trace). Remember to export it before running:

     # Example: Run with debug level logging
     export RUST_LOG=debug
     polars-view data.parquet
     

   • Examples:

     polars-view sales_data.parquet
     polars-view --delimiter="|" transactions.csv --null-values="N/A,-"
     polars-view data.csv -q "SELECT category, SUM(value) AS total FROM AllData GROUP BY category"
     # Normalize Euro numbers in columns matching "^Value.*$"
     polars-view data.csv -a "^Value.*$"
     # Normalize Euro numbers in ALL string columns (Use with caution!)
     polars-view data.csv -a "*"
     # Use backticks/quotes for names with spaces/special chars
     polars-view items.csv -q "SELECT \`Item Name\`, Price FROM AllData WHERE Price > 100.0"
     polars-view logs.ndjson -q 'SELECT timestamp, message FROM AllData WHERE level = "ERROR"'
     # Remove all-null columns on load
     polars-view big_dataset.parquet --remove-null-cols
     

Usage Guide

• Opening Files: Use the command line, "File" > "Open File..." (Ctrl+O), or drag & drop.
• Viewing Data: Scroll the table. Click header icons to apply/cycle sorting (supports multiple columns; order matters). Drag header separators to resize columns.
• Configuring View & Data: Use left-side panels ("Info", "Format", "Query", "Columns"). Format changes update the view efficiently; Query/Filter changes trigger an asynchronous data reload/requery.
• Applying SQL: Enter query in "Query" panel (default table: AllData). Click "Apply SQL Commands". See examples or Polars SQL docs.
• Saving Data:
  • Save (Ctrl+S): Overwrites the original file path.
  • Save As... (Ctrl+A): Saves current view to a new file. Choose format (CSV, JSON, NDJSON, Parquet) via dialog.
• Exiting: Use "File" > "Exit" or close the window.

Core Dependencies

• GUI Framework: eframe, egui, egui_extras
• Data Handling: polars (with features like lazy, csv, json, parquet, sql)
• Asynchronous Runtime: tokio (with features like rt, sync, rt-multi-thread)
• Command Line: clap, anstyle
• File Dialogs: rfd
• Logging/Diagnostics: tracing, tracing-subscriber
• Utilities: thiserror, cfg-if, env_logger (non-wasm)

License

This project is licensed under the MIT License.