dataframe-textual 1.5.0

Interactive terminal viewer/editor for tabular data

DataFrame Textual

A powerful, interactive terminal-based viewer/editor for CSV/TSV/Excel/Parquet/JSON/NDJSON 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 terminal with multi-tab support for multiple files!

Features

Data Viewing

• 🚀 Fast Loading - Powered by Polars for efficient data handling
• 🎨 Rich Terminal UI - Beautiful, color-coded columns with various data types (e.g., integer, float, string)
• ⌨️ Comprehensive Keyboard Navigation - Intuitive controls

Skip first 5 lines (comments, metadata)

dv -l 5 data_with_metadata.csv

Skip 1 row after header (e.g., units row)

dv -a 1 data_with_units.csv

Complex CSV with comments and units row

dv -l 3 -a 1 -I messy_scientific_data.csv

Combine all options: skip lines, skip after header, no header, no inference, gzipped

dv -l 2 -a 1 -H -I complex_data.csv.gz

Process compressed data from stdin with line skipping

zcat compressed_data.csv.gz | dv -f csv -l 2editing, and manipulating data

• 📊 Flexible Input - Read from files and/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 edits back to file with full undo/redo support

Advanced Features

• 📂 Multi-File Support - Open multiple files in separate tabs
• 🔄 Tab Management - Seamlessly switch between open files with keyboard shortcuts
• 📌 Freeze Rows/Columns - Keep important rows and columns visible while scrolling
• 🎯 Cursor Type Cycling - Switch between cell, row, and column selection modes
• 🔗 Link Column Creation - Generate clickable URLs using template expressions with placeholder support

Installation

Using pip

# Install from PyPI
pip install dataframe-textual

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

This installs an executable dv.

Then run:

dv <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
uv sync --extra excel  # with Excel support

# Run directly with uv
uv run dv <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
dv 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 (auto-detects format; defaults to TSV if not recognized)
cat data.tsv | dv
dv < data.tsv

# Gzipped files are supported
dv data.csv.gz
dv large_dataset.tsv.gz

# Specify format for gzipped stdin
zcat data.csv.gz | dv -f csv

Multi-File Usage - Multiple Tabs

# Open multiple files in tabs
dv file1.csv file2.csv file3.csv

# Open multiple sheets in tabs in an Excel file
dv file.xlsx

# Mix files and stdin (read from stdin, then open file)
dv data1.tsv < data2.tsv

# Mix regular and gzipped files
dv data1.csv data2.csv.gz data3.tsv.gz

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 (edits, sort order, selections, history, etc.)

Command Line Options

usage: dv [-h] [-f {csv,excel,tsv,parquet,json,ndjson}] [-H] [-I] [-E] [-c COMMENT_PREFIX] [-q QUOTE_CHAR] [-l SKIP_LINES] [-a SKIP_ROWS_AFTER_HEADER] [-u NULL [NULL ...]] [files ...]

Interactive terminal based viewer/editor for tabular data (e.g., CSV/Excel).

positional arguments:
  files                 Files to view (or read from stdin)

options:
  -h, --help            show this help message and exit
  -f, --format {csv,excel,tsv,parquet,json,ndjson}
                        Specify the format of the input files
  -H, --no-header       Specify that input files have no header row
  -I, --no-inferrence   Do not infer data types when reading CSV/TSV
  -E, --ignore-errors   Ignore errors when reading CSV/TSV
  -c, --comment-prefix COMMENT_PREFIX
                        Comment lines are skipped when reading CSV/TSV (default: skip none)
  -q, --quote-char QUOTE_CHAR
                        Quote character for reading CSV/TSV (default: "; use None to disable)
  -l, --skip-lines SKIP_LINES
                        Skip lines when reading CSV/TSV (default: 0)
  -a, --skip-rows-after-header SKIP_ROWS_AFTER_HEADER
                        Skip rows after header when reading CSV/TSV (default: 0)
  -u, --null NULL [NULL ...]
                        Values to interpret as null values when reading CSV/TSV

CLI Examples

# View CSV file without header row
dv -H data_no_header.csv

# Disable type inference for faster loading
dv -I large_data.csv

# Ignore parsing errors in malformed CSV
dv -E data_with_errors.csv

# Skip first 3 lines of file (e.g., comments, metadata)
dv -l 3 data_with_comments.csv

# Skip 1 row after header (e.g., units row)
dv -a 1 data_with_units.csv

# Treat specific values as null/missing (e.g., 'NA', 'N/A', '-')
dv -u NA N/A - data.csv

# Multiple null values with different formats
dv -u NULL NA "" "Not Available" messy_data.csv

# Disable quote character processing for TSV with embedded quotes
dv -q "" data.tsv

# Use different quote character (e.g., single quote for CSV)
dv -q "'" data.csv

# Complex CSV with comments and units row
dv -l 3 -a 1 -I messy_scientific_data.csv

# Combine all options: skip lines, skip after header, no header, no inference, gzipped
dv -l 2 -a 1 -H -I complex_data.csv.gz

# Process compressed data from stdin with line skipping
zcat compressed_data.csv.gz | dv -f csv -l 2

# CSV with custom null values and no header
dv -H -u NA "N/A" "-" raw_data.csv

# Skip lines, specify null values, and disable type inference
dv -l 5 -u NA "" data_with_metadata.csv

# TSV file with problematic quotes in data fields
dv -q None data.tsv

# CSV with comment lines and custom null values
dv -c "#" -u NA "N/A" commented_data.csv

Keyboard Shortcuts

App-Level Controls

File & Tab Management

Key	Action
Ctrl+O	Open file in a new tab
Ctrl+W	Close current tab
Ctrl+A	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
F1	Toggle help panel
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
Home / End	Jump to first/last column in current row
Ctrl + Home / Ctrl + End	Jump to top/bottom in current page
PageDown / PageUp	Scroll down/up one page
Ctrl+F	Page down
Ctrl+B	Page up

Viewing & Display

Key	Action
Enter	View full details of current row in modal
F	Show frequency distribution for column
s	Show statistics for current column
S	Show statistics for entire dataframe
K	Cycle cursor type: cell → row → column → cell
~	Toggle row labels
_ (underscore)	Expand column to full width

Data Editing

Key	Action
Double-click	Edit cell or rename column header
delete	Clear current cell (set to NULL)
e	Edit current cell (respects data type)
E	Edit entire column with expression
a	Add empty column after current
A	Add column with name and value/expression
@	Add a link column from template expression
- (minus)	Delete current column
x	Delete current row
X	Delete current row and all rows below
Ctrl+X	Delete current row and all rows above
d	Duplicate current column (appends '_copy' suffix)
D	Duplicate current row
h	Hide current column
H	Show all hidden rows/columns

Searching & Filtering

Key	Action
\	Search in current column using cursor value and select rows
| (pipe)	Search in current column with expression and select rows
/	Find in current column with cursor value and highlight matches
?	Find in current column with expression and highlight matches
n	Go to next match
N	Go to previous match
{	Go to previous selected row
}	Go to next selected row
'	Select/deselect current row
t	Toggle selected rows (invert)
T	Clear all selected rows and/or matches
" (quote)	Filter to selected rows only
v	View only rows by selected rows and/or matches or cursor value
V	View only rows by expression

SQL Interface

Key	Action
l	Simple SQL interface (select columns & WHERE clause)
L	Advanced SQL interface (full SQL queries)

Find & Replace

Key	Action
;	Find across all columns with cursor value
:	Find across all columns with expression
r	Find and replace in current column (interactive or replace all)
R	Find and replace across all columns (interactive or replace all)

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

Type Conversion

Key	Action
#	Cast current column to integer (Int64)
%	Cast current column to float (Float64)
!	Cast current column to boolean
$	Cast current column to string

Data Management

Key	Action
z	Freeze rows and columns
,	Toggle thousand separator for numeric display
c	Copy current cell to clipboard
Ctrl+C	Copy column to clipboard
Ctrl+R	Copy row to clipboard (tab-separated)
Ctrl+S	Save current tab to file
u	Undo last action
U	Redo last undone action
Ctrl+U	Reset to initial state

Features in Detail

1. Color-Coded Data Types

Columns are automatically styled based on their data type:

• integer: Cyan text, right-aligned
• float: Magenta text, right-aligned
• string: Green text, left-aligned
• boolean: Blue text, centered
• temporal: Yellow 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 view the main table to show only rows with the selected column value
• Press " to filter all rows containing the selected column value
• Press q or Escape to close the modal

3. Search & Filtering

The application provides multiple search modes for different use cases:

Search Operations - Direct value/expression matching in current column:

• | - Column Expression Search: Opens dialog to search current column with custom expression
• \ - Column Cursor Search: Instantly search current column using the cursor value

Find Operations - Find by value/expression:

• / - Column Find: Find cursor value within current column
• ? - Column Expression Find: Open dialog to search current column with expression
• ; - Global Find: Find cursor value across all columns
• : - Global Expression Find: Open dialog to search all columns with expression

Selection & Filtering:

• ' - Toggle Row Selection: Select/deselect current row (marks it for filtering)
• t - Invert Selections: Flip selection state of all rows at once
• T - Clear Selections: Remove all row selections and matches
• " - Filter Selected: Display only the selected rows and remove others
• v - View by Value: Filter/view rows by selected rows or cursor value (others hidden but preserved)
• V - View by Expression: Filter/view rows using custom Polars expression (others hidden but preserved)

Advanced Matching Options:

When searching or finding, you can use checkboxes in the dialog to enable:

• Match Nocase: Ignore case differences (e.g., "john", "John", "JOHN" all match)
• Match Whole: Match complete value, not partial substrings or words (e.g., "cat" won't match in "catfish")

These options work with plain text searches. Use Polars regex patterns in expressions for more control:

• Case-insensitive matching in expressions: Use (?i) prefix in regex (e.g., (?i)john)
• Word boundaries in expressions: Use \b in regex (e.g., \bjohn\b matches whole word)

Quick Tips:

• Search results highlight matching rows/cells in red
• Multiple searches accumulate selections - each new search adds to the selections
• Type-aware matching automatically converts values. Resort to string comparison if conversion fails
• Use u to undo any search or filter

3b. Find & Replace

The application provides powerful find and replace functionality for both single-column and global replacements.

Replace Operations:

• r - Column Replace: Replace values in the current column
• R - Global Replace: Replace values across all columns

How It Works:

When you press r or R, a dialog opens where you can enter:

1. Find term: The value or expression to search for
2. Replace term: What to replace matches with
3. Matching options:
   • Match Nocase: Ignore case differences when matching (unchecked by default)
   • Match Whole: Match complete words only, not partial words (unchecked by default)
4. Replace option:
   • Choose "Replace All" to replace all matches at once (with confirmation)
   • Otherwise, review and confirm each match individually

Replace All (r or R → Choose "Replace All"):

• Shows a confirmation dialog with the number of matches and replacements
• Replaces all matches with a single operation
• Full undo support with u
• Useful for bulk replacements when you're confident about the change

Replace Interactive (r or R → Choose "Replace Interactive"):

• Shows each match one at a time with a preview of the replacement
• For each match, press:
  • Enter or press the Yes button - Replace this occurrence and move to next
  • Press the Skip button - Skip this occurrence and move to next
  • Escape or press the No button - Cancel remaining replacements (but keep already-made replacements)
• Displays progress: Occurrence X of Y (Y = total matches, X = current)
• Shows the value that will be replaced and what it will become
• Useful for careful replacements where you want to review each change

Search Term Types:

• Plain text: Exact string match (e.g., "John" finds "John")
  • Use Match Nocase checkbox to match regardless of case (e.g., find "john", "John", "JOHN")
  • Use Match Whole checkbox to match complete words only (e.g., find "cat" but not in "catfish")
• NULL: Replace null/missing values (type NULL)
• Expression: Polars expressions for complex matching (e.g., $_ > 50 for column replace)
• Regex patterns: Use Polars regex syntax for advanced matching
  • Case-insensitive: Use (?i) prefix (e.g., (?i)john)
  • Whole word: Use \b boundary markers (e.g., \bjohn\b)

Examples:

Find: "John"
Replace: "Jane"
→ All occurrences of "John" become "Jane"

Find: "john"
Replace: "jane"
Match Nocase: ✓ (checked)
→ "John", "JOHN", "john" all become "jane"

Find: "cat"
Replace: "dog"
Match Whole: ✓ (checked)
→ "cat" becomes "dog", but "catfish" is not matched

Find: "NULL"
Replace: "Unknown"
→ All null/missing values become "Unknown"

Find: "(?i)active"        # Case-insensitive
Replace: "inactive"
→ "Active", "ACTIVE", "active" all become "inactive"

For Global Replace (R):

• Searches and replaces across all columns simultaneously
• Each column can have different matching behavior (string matching for text, numeric for numbers)
• Preview shows which columns contain matches before replacement
• Useful for standardizing values across multiple columns

Features:

• Full history support: Use u (undo) to revert any replacement
• Visual feedback: Matching cells are highlighted before you choose replacement mode
• Safe operations: Requires confirmation before replacing
• Progress tracking: Shows how many replacements have been made during interactive mode
• Type-aware: Respects column data types when matching and replacing
• Flexible matching: Support for case-insensitive and whole-word matching

Tips:

• Use interactive mode for one-time replacements to be absolutely sure
• Use "Replace All" for routine replacements (e.g., fixing typos, standardizing formats)
• Use Match Nocase for matching variations of names or titles
• Use Match Whole to avoid unintended partial replacements
• Use u immediately if you accidentally replace something wrong
• For complex replacements, use Polars expressions or regex patterns in the find term
• Test with a small dataset first before large replacements

4. Polars Expressions

Complex values or filters can be specified via Polars expressions, with the following adaptions for convenience:

Column References:

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

Row References:

• $# - Current row index (1-based)

Basic Comparisons:

• $_ > 50 - Current column greater than 50
• $salary >= 100000 - Salary at least 100,000
• $age < 30 - Age less than 30
• $status == 'active' - Status exactly matches 'active'
• $name != 'Unknown' - Name is not 'Unknown'

Logical Operators:

• & - AND
• | - OR
• ~ - NOT

Practical Examples:

• ($age < 30) & ($status == 'active') - Age less than 30 AND status is active
• ($name == 'Alice') | ($name == 'Bob') - Name is Alice or Bob
• $salary / 1000 >= 50 - Salary divided by 1,000 is at least 50
• ($department == 'Sales') & ($bonus > 5000) - Sales department with bonus over 5,000
• ($score >= 80) & ($score <= 90) - Score between 80 and 90
• ~($status == 'inactive') - Status is not inactive
• $revenue > $expenses - Revenue exceeds expenses

String Matching:

• $name.str.contains("John") - Name contains "John" (case-sensitive)
• $name.str.contains("(?i)john") - Name contains "john" (case-insensitive)
• $email.str.ends_with("@company.com") - Email ends with domain
• $code.str.starts_with("ABC") - Code starts with "ABC"
• $age.cast(pl.String).str.starts_with("7") - Age (cast to string first) starts with "7"

Number Operations:

• $age * 2 > 100 - Double age greater than 100
• ($salary + $bonus) > 150000 - Total compensation over 150,000
• $percentage >= 50 - Percentage at least 50%

Null Handling:

• $column.is_null() - Find null/missing values
• $column.is_not_null() - Find non-null values
• NULL - a value to represent null for convenience

Tips:

• Use column names that match exactly (case-sensitive)
• Use parentheses to clarify complex expressions: ($a & $b) | ($c & $d)

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 remove the column from sorting

6. Frequency Distribution

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

• Value
• Count
• Percentage
• Histogram
• 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 exclude all rows except those 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. Column & Dataframe Statistics

Press s to see summary statistics for the current column, or press S for statistics across the entire dataframe.

Column Statistics (s):

• Shows calculated statistics using Polars' describe() method
• Displays: count, null count, mean, median, std, min, max, etc.
• Values are color-coded according to their data type
• Statistics label column has no styling for clarity

Dataframe Statistics (S):

• Shows statistics for all numeric and applicable columns simultaneously
• Data columns are color-coded by their data type (integer, float, string, etc.)

In the Statistics Modal:

• Press q or Escape to close the statistics table
• Use arrow keys to navigate
• Useful for quick data validation and summary reviews

This is useful for:

• Understanding data distributions and characteristics
• Identifying outliers and anomalies
• Data quality assessment
• Quick statistical summaries without external tools
• Comparing statistics across columns

8. Data Editing

Edit Cell (e or Double-click):

• Opens modal for editing current cell
• Validates input based on column data type

Rename Column Header (Double-click column header):

• Quick rename by double-clicking the column header

Delete Row (x):

• Delete all selected rows (if any) at once
• Or delete single row at cursor

Delete Row and Below (X):

• Deletes the current row and all rows below it
• Useful for removing trailing data or the end of a dataset

Delete Row and Above (Ctrl+X):

• Deletes the current row and all rows above it
• Useful for removing leading rows or the beginning of a dataset

Delete Column (-):

• Removes the entire column from view and dataframe

Delete Column and After (_):

• Deletes the current column and all columns to the right
• Useful for removing trailing columns or the end of a dataset

Delete Column and Before (Ctrl+-):

• Deletes the current column and all columns to the left
• Useful for removing leading columns or the beginning of a dataset

9. Hide & Show Columns

Hide Column (h):

• Temporarily hides the current column from display
• Column data is preserved in the dataframe
• Hidden columns are included in saves

Show Hidden Rows/Columns (H):

• Restores all previously hidden rows/columns to the display

This is useful for:

• Focusing on specific columns without deleting data
• Temporarily removing cluttered or unnecessary columns

10. Duplicate Column

Press d to duplicate the current column:

• Creates a new column immediately after the current column
• New column has '_copy' suffix (e.g., 'price' → 'price_copy')
• Duplicate preserves all data from original column
• New column is inserted into the dataframe

This is useful for:

• Creating backup copies of columns before transformation
• Working with alternative versions of column data
• Comparing original vs. processed column values side-by-side

11. Duplicate Row

Press D to duplicate the current row:

• Creates a new row immediately after the current row
• Duplicate preserves all data from original row
• New row is inserted into the dataframe

This is useful for:

• Creating variations of existing data records
• Batch adding similar rows with modifications

12. Column & Row Reordering

Move Columns: Shift+← and Shift+→

• Swaps adjacent columns
• Reorder is preserved when saving

Move Rows: Shift+↑ and Shift+↓

• Swaps adjacent rows
• Reorder is preserved when saving

13. Freeze Rows and Columns

Press z to open the dialog:

• Enter number of fixed rows and/or columns to keep top rows/columns visible while scrolling

13.5. Thousand Separator Toggle

Press , to toggle thousand separator formatting for numeric data:

• Applies to integer and float columns
• Formats large numbers with commas for readability (e.g., 1000000 → 1,000,000)
• Works across all numeric columns in the table
• Toggle on/off as needed for different viewing preferences
• Display-only: does not modify underlying data in the dataframe
• State persists during the session

14. Save File

Press Ctrl+S to save:

• Save filtered, edited, or sorted data back to file
• Choose filename in modal dialog
• Confirm if file already exists

15. Undo/Redo/Reset

Undo (u):

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

Redo (U):

• Reapplies the last undone action
• Restores the state before the undo was performed
• Useful for redoing actions you've undone by mistake
• Useful for alternating between two different states

Reset (Ctrl+U):

• Reverts all changes and returns to original data state when file was first loaded
• Clears all edits, deletions, selections, filters, and sorts
• Useful for starting fresh without reloading the file

16. Column Type Conversion

Press the type conversion keys to instantly cast the current column to a different data type:

Type Conversion Shortcuts:

• # - Cast to integer
• % - Cast to float
• ! - Cast to boolean
• $ - Cast to string

Features:

• Instant conversion with visual feedback
• Full undo support - press u to revert
• Leverage Polars' robust type casting

Note: Type conversion attempts to preserve data where possible. Conversions may lose data (e.g., float to int rounding).

17. Cursor Type Cycling

Press K 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

18. SQL Interface

The SQL interface provides two modes for querying your dataframe:

Simple SQL Interface (l)

Select specific columns and apply WHERE conditions without writing full SQL:

• Choose which columns to include in results
• Specify WHERE clause for filtering
• Ideal for quick filtering and column selection

Advanced SQL Interface (L)

Execute complete SQL queries for advanced data manipulation:

• Write full SQL queries with standard SQL syntax
• Support for JOINs, GROUP BY, aggregations, and more
• Access to all SQL capabilities for complex transformations
• Always use self as the table name

Examples:

-- Filter and select specific rows and/or columns
SELECT name, age FROM self WHERE age > 30

-- Aggregate with GROUP BY
SELECT department, COUNT(*) as count, AVG(salary) as avg_salary
FROM self
GROUP BY department

-- Complex filtering with multiple conditions
SELECT *
FROM self
WHERE (age > 25 AND salary > 50000) OR department = 'Management'

19. Clipboard Operations

Copies value to system clipboard with pbcopy on macOS and xclip on Linux

Press Ctrl+C to copy:

• Press c to copy cursor value
• Press Ctrl+C to copy column values
• Press Ctrl+R to copy row values (delimited by tab)

20. Link Column Creation

Press @ to create a new column containing dynamically generated URLs using template expressions.

Template Placeholders:

The link template supports multiple placeholder types for maximum flexibility:

• $_ - Current column (the column where cursor was when @ was pressed)

  • Example: https://example.com/search/$_ - Uses values from the current column
  • Useful for quick links based on the focused column

• $1, $2, $3, etc. - Column by 1-based position index

  • Example: https://example.com/product/$1/details/$2 - Uses 1st and 2nd columns
  • Useful for structured templates spanning multiple columns
  • Index corresponds to column display order (left-to-right)

• $name - Column by name (use actual column names)

  • Example: https://pubchem.ncbi.nlm.nih.gov/search?q=$product_id - Uses product_id column
  • Example: https://example.com/$region/$city/data - Uses region and city columns
  • Useful for readable, self-documenting templates

Features:

• Vectorized Expression: All rows processed efficiently using Polars' vectorized operations
• Type Casting: Column values automatically converted to strings for URL construction
• Multiple Placeholders: Mix and match placeholders in a single template
• URL Prefix: Automatically prepends https:// if URL doesn't start with http:// or https://
• PubChem Support: Special shorthand - replace PC with full PubChem URL

Examples:

Template: https://example.com/$_
Current column: product_id
Result: https://example.com/ABC123 (for each row's product_id value)

Template: https://database.org/view?id=$1&lang=$2
Column 1: item_code, Column 2: language
Result: https://database.org/view?id=X001&lang=en

Template: https://example.com/$username/profile
Column: username (must exist in dataframe)
Result: https://example.com/john_doe/profile

Template: https://example.com/$region/$city
Columns: region, city
Result: https://example.com/north/seattle

Template: PC/compound/$1
Column 1: pubchem_cid
Result: https://pubchem.ncbi.nlm.nih.gov/compound/12345

Error Handling:

• Invalid column index: $5 when only 3 columns exist → Error message showing valid range
• Non-existent column name: $invalid_column → Error message with available columns
• No placeholders: Template treated as constant → All rows get identical URL

Tips:

• Use descriptive column names for $name placeholders to make templates self-documenting
• Test with a small dataset first to verify template correctness
• Use full undo (u) if template produces unexpected URLs
• For complex multi-column URLs, use column names ($name) for clarity over positions ($1)

Examples

Single File Examples

# View Pokemon dataset
dv pokemon.csv

# Chain with other command and specify input file format
cut -d',' -f1,2,3 pokemon.csv | dv -f csv

# Work with gzipped files
dv large_dataset.csv.gz

# CSV file without header row
dv -H raw_data.csv

# Skip type inference for faster loading
dv -I huge_file.csv

# Skip first 5 lines (comments, metadata)
dv -L 5 data_with_metadata.csv

# Skip 1 row after header (units row)
dv -K 1 data_with_units.csv

# Complex CSV with comments and units row
dv -L 3 -K 1 -I messy_scientific_data.csv

# Combine all options: skip lines, skip after header, no header, no inference, gzipped
dv -L 2 -K 1 -H -I complex_data.csv.gz

# Process compressed data from stdin with line skipping
zcat compressed_data.csv.gz | dv -f csv -L 2

Multi-File/Tab Examples

# Open multiple sheets as tabs in a single Excel
dv sales.xlsx

# Open multiple files as tabs (including gzipped)
dv pokemon.csv titanic.csv large_data.csv.gz

# Start with one file, then open others using Ctrl+O
dv initial_data.csv

Dependencies

• polars: Fast DataFrame library for data loading/processing
• textual: Terminal UI framework
• fastexcel: Read Excel files
• xlsxwriter: Write Excel files

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 and Polars