VisiData Documentation

VisiData is an interactive multitool for tabular data in the terminal. It combines the clarity of a spreadsheet, the efficiency of the terminal, and the power of Python — capable of handling millions of rows with ease.

Who This Track Is For

• Data analysts and engineers exploring CSV, JSON, SQLite, and log files in the terminal
• Sysadmins parsing structured output from commands and log files interactively
• Accountants and spreadsheet users migrating from Excel to terminal-based workflows
• Developers who need a fast, keyboard-driven alternative to spreadsheets and pandas

What You Will Build

• A fast sheet-based exploration workflow for any tabular data format
• Column manipulation pipelines: type casting, derived columns, regex splits
• Frequency tables, pivot tables, and in-terminal scatterplots
• A .visidatarc configuration that matches your workflow
• Replayable audit trails via VisiData's CommandLog
• Excel-to-VisiData translation skills for accountants and analysts

How To Use This Track

• Follow the modules in order (1 to 12). Each lesson includes real commands and exercises.
• Practice with actual data files from ~/github/practice-folder/visidata/.
• Build your own .visidatarc incrementally as you complete each module.

Learning Path

Module	Focus	Lessons
1. Introduction	Fundamentals and mental model	3
2. Configuration	.visidatarc, themes, plugins, and options	4
3. Loading and Exporting	Open files, load formats, export, batch mode, performance	3
4. Navigation and Sheets	Cursor, scroll, search, sheet stack, global commands, sheet entry points	5
5. Row Operations	Selection, filtering, sorting, reordering, mutations	4
6. Column Operations	Types, layout, creation, editing, nested JSON, aggregators, Columns Sheet	8
7. Editing and Audit	CommandLog replay, macros, cell editing, incremental values	3
8. Analysis and Grouping	Frequency tables, pivot, descriptive stats, describe sheet, joins	4
9. Visualization	In-terminal graphs, multi-series canvas, categorical plots	4
10. Python and Formulas	Excel-to-VisiData translator for analysts and accountants	8
11. Real-World Workflows	Capstone project, sysadmin, ETL pipeline, API patterns	6
12. Troubleshooting	Common errors, performance, format issues, config debugging	4
13. Cheatsheet	Quick start card, keyboard map, and reference cheatsheets	6

Core Architecture

How VisiData Works

File / Pipe / URL
      │
      ▼  vd file.csv
  VisiData Engine
      │
      ▼
  Sheet Stack
  ├── Source Sheet
  │     ├── Columns  →  Types: str / int / float / date
  │     └── Rows     →  Selection / Filter
  ├── Frequency Table    (Shift+F)
  └── Pivot Table        (Shift+W)

Sheet-Based Mental Model

vd file.csv
  │
  └──▶ Sheet Stack
         │
         └──▶ Source Sheet  (all rows and columns)
                ├──Shift+F──▶ Frequency Table    (grouped by column)
                ├──Shift+W──▶ Pivot Table        (key × aggregator)
                ├──Shift+I──▶ Describe Sheet     (summary statistics)
                ├──Shift+C──▶ Columns Sheet      (inspect/edit metadata)
                └──  g.  ──▶ Graph               (numeric column vs key)

warning

VisiData does not modify your source files unless you explicitly save with Ctrl+S. All transformations happen in memory.

Quick Start

first-visidata-session.sh

# 1) Install VisiData
pip install visidata          # recommended
sudo apt install visidata     # Debian/Ubuntu (may be older version)

# 2) Open a CSV file
vd ~/github/practice-folder/visidata/01-loading/01-employees.csv

# 3) Navigate with arrow keys or hjkl
# 4) Open a frequency table on the current column
#    Press: Shift+F

# 5) Quit the current sheet
#    Press: q

# 6) Quit VisiData entirely
#    Press: gq  (quit all at once)

note

Press Space to open the command palette — a searchable list of every VisiData command. Press z^H for help specific to the current sheet type.

VisiData vs Alternatives

Tool	Use Case	Key Advantage
visidata	Interactive tabular data exploration	Multi-format, Python expressions, sheets
csvkit	CSV transformation pipelines	CLI-composable, scriptable
miller	Stream processing of structured data	Fast, AWK-like syntax
pandas	Programmatic data analysis	Full Python ecosystem
xsv	Fast CSV indexing and slicing	Blazing speed, Rust-based

Prerequisites

• Linux shell basics (ls, cat, pipe, redirection)
• Familiarity with CSV or tabular data concepts
• Python 3.8+ (VisiData is a Python package)

Success Criteria

By the end of this track, you can:

• Open any supported data format and navigate it fluidly
• Select, filter, and sort rows using keyboard-driven workflows
• Create derived columns with Python expressions
• Build frequency tables and pivot tables without leaving the terminal
• Customize VisiData with a .visidatarc that matches your workflow
• Replay and document data transformations via CommandLog
• Translate Excel formulas to VisiData Python equivalents

Next Step

Start with What is VisiData.