dataframe-textual 0.2.0

Interactive CSV/Excel viewer for the terminal (Textual TUI)

DataFrame Viewer/Editor

A powerful, interactive terminal-based CSV/Excel viewer/editor built with Python, Polars, and Textual. Inspired by VisiData, this tool provides smooth keyboard navigation, data manipulation, and a clean interface for exploring tabular data directly in your terminal. Now with multi-file support for simultaneous data comparison!

Features

Core Data Viewing

• 🚀 Fast CSV Loading - Powered by Polars for efficient data handling with lazy pagination
• 🎨 Rich Terminal UI - Beautiful, color-coded columns with automatic type detection
• ⌨️ Comprehensive Keyboard Navigation - Intuitive controls for browsing, editing, and manipulating data
• 📊 Flexible Input - Read from files or stdin (pipes/redirects)
• 🔄 Smart Pagination - Lazy load rows on demand for handling large datasets

Data Manipulation

• 📝 Data Editing - Edit cells, delete rows, and remove columns
• 🔍 Search & Filter - Find values, highlight matches, and filter selected rows
• ↔️ Column/Row Reordering - Move columns and rows with simple keyboard shortcuts
• 📈 Sorting & Statistics - Multi-column sorting and frequency distribution analysis
• 💾 Save & Undo - Save filtered data back to CSV with full undo/redo support

Advanced Features

• 📌 Pin Rows/Columns - Keep important rows and columns visible while scrolling
• 🎯 Cursor Type Cycling - Switch between cell, row, and column selection modes
• 📂 Multi-File Support - Open multiple CSV files in tabs for side-by-side comparison
• 🔄 Tab Management - Seamlessly switch between open files with keyboard shortcuts

Installation

Using pip

# Install from PyPI
pip install dataframe-textual

# With Excel support (fastexcel, xlsxwriter)
pip install dataframe-textual[excel]

Then run:

dataframe-textual <csv_file>

Using uv

# Quick run using uvx without installation
uvx https://github.com/need47/dataframe-textual.git <csvfile>

# Clone or download the project
cd dataframe-textual

# Run directly with uv
uv run python main.py <csv_file>

#

Development installation

# Clone the repository
git clone https://github.com/need47/dataframe-textual.git
cd dataframe-textual

# Install from local source
pip install -e .

# Or with development dependencies
pip install -e ".[excel,dev]"

Usage

Basic Usage - Single File

# After pip install dataframe-textual
dataframe-textual pokemon.csv

# Or if running from source
python main.py pokemon.csv

# Or with uv
uv run python main.py pokemon.csv

# Read from stdin
cat data.csv | dataframe-textual
dataframe-textual < data.csv

Multi-File Usage - Multiple Tabs

# Open multiple files in tabs
dataframe-textual file1.csv file2.csv file3.csv

# Open multiple sheets in tabs in an Excel file
dataframe-textual file.xlsx

# Mix files and stdin (file opens first, then read from stdin)
dataframe-textual data1.csv < data2.csv

When multiple files are opened:

• Each file appears as a separate tab at the top
• Switch between tabs using > (next) or < (previous)
• Open additional files with Ctrl+O
• Close the current tab with Ctrl+W
• Each file maintains its own state (sort order, selections, history, etc.)
• Edits and filters are independent per file

Keyboard Shortcuts

App-Level Controls

File & Tab Management

Key	Action
Ctrl+O	Open new CSV file in a new tab
Ctrl+W	Close current tab
Ctrl+Shift+S	Save all open tabs to Excel file
> or b	Move to next tab
<	Move to previous tab
B	Toggle tab bar visibility
q	Quit the application

View & Settings

Key	Action
? or h	Toggle help panel (context-sensitive)
k	Cycle through themes

---

Table-Level Controls

Navigation

Key	Action
g	Jump to first row
G	Jump to last row (loads all remaining rows)
↑ / ↓	Move up/down one row
← / →	Move left/right one column
PageDown / PageUp	Scroll down/up
Arrow keys	Navigate the table

Viewing & Display

Key	Action
Enter	View full details of current row in modal
F	Show frequency distribution for column
C	Cycle cursor type: cell → row → column → cell
#	Toggle row labels visibility

Data Editing

Key	Action
e	Edit current cell (respects data type)
d	Delete current row
-	Delete current column

Searching & Filtering

Key	Action
| (pipe)	Search in current column (case-insensitive)
/ (slash)	Global search across all columns
\	Search current column using cell value
s	Select/deselect current row
t	Toggle highlighting of all selected rows (invert)
T	Clear all selected rows
" (quote)	Filter to show only selected rows
v	Filter by selected rows (if any) or current cell value
V	Filter by expression (Polars expression syntax)

Sorting

Key	Action
[	Sort current column ascending
]	Sort current column descending

Reordering

Key	Action
Shift+↑	Move current row up
Shift+↓	Move current row down
Shift+←	Move current column left
Shift+→	Move current column right

Data Management

Key	Action
f	Freeze rows and columns
c	Copy current cell to clipboard
Ctrl+S	Save current tab to CSV/TSV file
u	Undo last action
U	Reset to original data

Modal Interactions

In Frequency Distribution Modal (opened with F):

• [ / ] - Sort frequency table
• v - Filter main table to selected value
• " - Highlight rows with selected value
• q / Escape - Close modal

In Row Detail Modal (opened with Enter):

• v - Filter main table to selected column value
• " - Highlight rows with selected column value
• q / Escape - Close modal

Tip: Press ? or h to open the context-sensitive help panel which displays all available shortcuts based on your current focus.

Features in Detail

1. Color-Coded Data Types

Columns are automatically styled based on their data type:

• Int64 (Integers): Cyan text, right-aligned
• Float64 (Decimals): Magenta text, right-aligned
• String: Green text, left-aligned
• Boolean: Blue text, centered
• Date/Datetime: Blue text, centered

2. Row Detail View

Press Enter on any row to open a modal showing all column values for that row. Useful for examining wide datasets where columns don't fit on screen.

In the Row Detail Modal:

• Press v to filter the main table to show only rows with the selected column value
• Press " to highlight all rows containing the selected column value
• Press q or Escape to close the modal

3. Search & Filtering

Column Search (|):

• Search for values in the current column
• Case-insensitive substring matching
• All matching rows are highlighted in red
• Multiple searches accumulate selections

Global Search (/):

• Search for a term across all columns simultaneously
• Cell-level highlighting in red for each matching cell
• Useful for finding a value anywhere in the dataset
• Automatically loads rows if matches extend beyond visible area
• Type-aware matching: converts values to strings before comparing

Cell-Value Search (\):

• Automatically search using the current cell's value
• Quick way to find all occurrences of a value

Row Filtering ("):

• Display only the selected (highlighted) rows
• Other rows are hidden but preserved
• Use undo (u) to restore

4. Filter by Expression

Press f to open a powerful filter expression dialog. This allows you to write complex filter conditions using a special syntax:

Column References:

• $_ - Current column (based on cursor position)
• $1, $2, etc. - Column by 1-based index
• $age, $salary - Column by name

Operators:

• Comparison: ==, !=, <, >, <=, >=
• Logical: && (AND), || (OR)
• Arithmetic: +, -, *, /, %

Examples:

• $_ > 50 - Current column greater than 50
• $salary >= 100000 - Salary at least 100,000
• $age < 30 && $status == 'active' - Age less than 30 AND status is active
• $name == 'Alice' || $name == 'Bob' - Name is Alice or Bob
• $salary / 1000 >= 50 - Salary divided by 1,000 is at least 50

See FILTER_EXPRESSION_GUIDE.md for comprehensive syntax documentation.

5. Sorting

• Press [ to sort current column ascending
• Press ] to sort current column descending
• Multi-column sorting supported (press multiple times on different columns)
• Press same key twice to toggle direction
• Frequency view (F) shows value distribution with optional sorting

6. Frequency Distribution

Press F to see how many times each value appears in the current column. The modal shows:

• Value
• Count
• Percentage of total
• Total row at the bottom

In the Frequency Table:

• Press [ and ] to sort by any column (value, count, or percentage)
• Press v to filter the main table to show only rows with the selected value
• Press " to highlight all rows containing the selected value
• Press q or Escape to close the frequency table

This is useful for:

• Understanding value distributions
• Quickly filtering to specific values
• Identifying rare or common values
• Finding the most/least frequent entries

7. Data Editing

Edit Cell (e):

• Opens modal for editing current cell
• Validates input based on column data type
• Shows column name and type
• Integer, number, and text inputs available

Delete Row (d):

• Delete single row at cursor
• Or delete all selected rows at once
• Deleted rows are marked internally but kept for undo

Delete Column (-):

• Removes the entire column from view and dataframe
• Cannot be undone directly (use undo feature)

8. Column & Row Reordering

Move Columns: Shift+← and Shift+→

• Swaps adjacent columns
• Reorder is preserved when saving

Move Rows: Shift+↑ and Shift+↓

• Swaps adjacent rows
• Visual reordering without affecting data

9. Pin Rows and Columns

Press f to open the pin dialog:

• Enter number of fixed rows: keeps top rows visible while scrolling
• Enter two numbers: <rows> <columns> (space-separated)
• Example: 2 3 pins top 2 rows and left 3 columns

10. Save to CSV

Press Ctrl+S to save:

• Save filtered, edited, or sorted data back to CSV
• Choose filename in modal dialog
• Confirm if file already exists
• Automatic .tsv or .csv detection

11. Undo/Redo

Press u to undo:

• Reverts last action with full state restoration
• Works for edits, deletions, sorts, searches, etc.
• Shows description of reverted action

12. Cursor Type Cycling

Press C to cycle through selection modes:

1. Cell mode: Highlight individual cell (and its row/column headers)
2. Row mode: Highlight entire row
3. Column mode: Highlight entire column

Visual feedback shows which mode is active.

13. Clipboard Operations

Press c to copy:

• Copies current cell value to system clipboard
• Works on macOS (pbcopy) and Linux (xclip)
• Shows confirmation notification

Data Type Support

• Int64, Int32, UInt32: Integer values
• Float64, Float32: Decimal numbers (shown with 4 significant figures)
• String: Text data
• Boolean: True/False values
• Date: ISO date format (YYYY-MM-DD)
• Datetime: ISO datetime format
• Null values: Displayed as -

Examples

Single File Examples

# View Pokemon dataset
dataframe-textual pokemon.csv

# View Titanic dataset with analysis
dataframe-textual titanic.csv

# Filter and view specific columns
cut -d',' -f1,2,3 pokemon.csv | dataframe-textual

# View with grep filter (then use | search in viewer)
grep "Fire" pokemon.csv | dataframe-textual

# Chain with other commands
cat data.csv | sort -t',' -k2 | dataframe-textual

Multi-File Examples

# Compare two versions of a dataset
dataframe-textual pokemon_v1.csv pokemon_v2.csv

# Side-by-side analysis of related files
dataframe-textual sales_2022.csv sales_2023.csv forecast_2024.csv

# Cross-reference datasets
dataframe-textual customers.csv orders.csv products.csv

# Start with one file, open others using Ctrl+O
dataframe-textual initial_data.csv
# Then press Ctrl+O to open more files interactively

Advanced Workflows

# Start with a filtered file, compare with original
grep "status=active" data.csv > filtered.csv
dataframe-textual data.csv filtered.csv
# Now compare the full dataset with the filtered version in separate tabs

# Multi-step analysis
# 1. Open multiple related CSVs
# 2. Use Ctrl+O to open additional files as you discover relationships
# 3. Each tab maintains independent sort/filter/search state
# 4. Use Ctrl+W to close tabs when done analyzing

Performance

• Lazy loading: Only loads visible rows + 10 rows ahead
• Efficient sorting: Uses Polars' optimized sort algorithms
• Smooth scrolling: No lag when paging through large files
• Memory efficient: Handles datasets larger than RAM

Tested with:

• 10,000+ row CSV files
• Wide datasets (100+ columns)
• Various data types and sizes

Dependencies

• polars: Fast DataFrame library for CSV processing
• textual: Terminal UI framework
• rich: Rich text and formatting in the terminal

Architecture Overview

Single-Table Design

The core of the application is built around the DataFrameTable widget:

• Self-contained: Each table instance maintains its own complete state (13 independent variables)
• Fully autonomous: All operations (editing, sorting, filtering, searching) are handled within the table
• Event-driven: Each table owns and handles its keyboard events
• Backward compatible: Works identically in single-file mode

Multi-Table Design

The DataFrameApp coordinates multiple independent DataFrameTable instances:

• Tab-based interface: Uses Textual's TabbedContent for tab management
• Independent state: Each tab has completely separate state (sort order, selections, history)
• Seamless switching: Switch between files without losing context or state
• File management: Open/close files dynamically without restarting the application

State Isolation

Each DataFrameTable instance owns:

• DataFrame (self.df)
• Sorted columns (self.sorted_columns)
• Selected rows (self.selected_rows)
• Edit history (self.histories)
• Cursor state (position, type)
• Search/filter state
• And 8 more internal state variables

This ensures perfect isolation between tabs with zero cross-contamination.

Requirements

• Python 3.11+
• POSIX-compatible terminal (macOS, Linux, WSL)
• Terminal supporting ANSI escape sequences and mouse events

Acknowledgments

• Inspired by VisiData
• Built with Textual, Polars, and Rich
• All code created through iterative development