DataFrame Textual

A powerful, interactive terminal-based viewer/editor for CSV/TSV/Excel/Parquet/Vortex/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 and analyzing tabular data directly in terminal with multi-tab support for multiple files!

Features

Data Viewing

• 🚀 Fast Loading - Powered by Polars for efficient batch data handling
• 🎨 Rich Terminal UI - Beautiful, color-coded columns with various data types (e.g., integer, float, string)
• ⌨️ Comprehensive Keyboard Navigation - Intuitive controls
• 📊 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, remove columns, and explode columns
• 🧹 Duplicate Removal - Remove duplicate rows
• 🔍 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, frequency distribution, and histogram 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
• 📑 Duplicate Tab - Create a copy of the current tab with the same data
• 🐍 Embedded Python Console - Inspect and transform the active table with df and pl directly in-app
• 📌 Freeze Rows/Columns - Keep important rows and columns visible while scrolling
• 🎯 Cursor Type Cycling - Switch between cell, row, and column selection modes
• 📸 Take Screenshot - Capture terminal view as a SVG image

Installation

Using pip

# Install from PyPI
pip install dataframe-textual

This installs an executable dv.

Using uv

# Install as a tool
uv tool install dataframe-textual

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

Development installation

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

# Install from local source with development dependencies
pip install -e ".[dev]"

Usage

Basic Usage - Single File

# Open one file
dv pokemon.csv

Multi-File Usage - Multiple Tabs

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

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

# Mix files and stdin
dv data1.tsv < data2.tsv

Multi-File Usage - Single Tab

# Read all parquet files (must be of same format and same structure) into one single table
dv *.parquet --all-in-one

Command Line Options

usage: dv [-h] [-V] [-d DELIMITER] [-f FORMAT] [-F [FIELDS ...]] [-H [HEADER ...]] [-L [N]] [-I] [-T] [-E] [-C [C]] [-Q [C]] [-K N] [-A N] [-M [N]] [-N NULL [NULL ...]] [--theme [THEME]] [--all-in-one]
          [--expr EXPR] [--sql SQL] [-o OUTPUT]
          [files ...]

TUI 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
  -V, --version         show program's version number and exit
  -d, --delimiter DELIMITER
                        Specify the delimiter of the input files (must be a single character, e.g., `|` or `;`). By default, the delimiter is inferred from the file extension. If reading from stdin, the
                        delimiter must be specified unless it is tab delimited.
  -f, --format FORMAT   Specify the format of the input files (e.g., `csv` or `excel`). By default, the format is inferred from the file extension. If reading from stdin, the format must be specified
                        unless it is tab delimited.
  -F, --fields [FIELDS ...]
                        When used without values, list available fields. Otherwise, read only specified fields.
  -H, --header [HEADER ...]
                        Specify header info. When reading CSV/TSV. If used without values, assumes no header. Otherwise, use provided values as column header (e.g., `-H col1 col2 col3`).
  -L, --infer_schema_length [N]
                        Number of rows to use for inferring schema when reading CSV/TSV. Defaults to 100. When used without value, uses all rows for schema inference (can be slow for large files).
  -I, --no-inference    Do not infer data types when reading CSV/TSV. All values will be of string type.
  -T, --truncate-ragged-lines
                        Truncate ragged lines when reading CSV/TSV
  -E, --ignore-errors   Ignore errors when reading CSV/TSV
  -C, --comment-prefix [C]
                        Skip comment lines starting with `C` when reading CSV/TSV
  -Q, --quote-char [C]  Use `C` as quote character for reading CSV/TSV. When used without value, disables special handling of quote characters.
  -K, --skip-lines N    Skip first N lines when reading CSV/TSV
  -A, --skip-rows-after-header N
                        Skip N rows after header when reading CSV/TSV
  -M, --n-rows [N]      Read maximum rows
  -N, --null NULL [NULL ...]
                        Values to interpret as null values when reading CSV/TSV
  --theme [THEME]       Set the theme for the application. If used without value, show available themes.
  --all-in-one, --aio, --one
                        Read all files (must be of same format and same structure) into one single table.
  --expr EXPR           Specify a Polars expression to filter data (e.g., $age > 30)
  --sql SQL             Specify a SQL query to execute on the input file (e.g., to select and filter data)
  -o, --output OUTPUT   Output file (optionally modified) with specified format, which is inferred from file extension (e.g., .csv, .xlsx).

CLI Examples

# Open a file
dv data.csv

# Gzipped files are supported
dv data.csv.gz

# Read from stdin (defaults to TSV)
cat data.tsv | dv
dv < data.tsv

# Specify delimiter
dv data.txt -d '|'

# Specify format
dv data.json -f ndjson

# View headless CSV file
dv data_no_header.csv -H

# View headless CSV file with provided header
dv data_no_header.csv -H country ip requests

# Skip first 3 rows (e.g., metadata)
dv data_with_meta.csv -K 3

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

# Skip 3 rows before header and 1 row after
dv messy_scientific_data.csv -K 3 -A 1

# Skip comment lines (or just -C)
dv commented_data.csv -C '#'

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

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

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

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

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

# Choose the `monokai` theme
dv data.csv --theme monokai

# Show column headers
dv data.csv -F

# Read only specific columns: 'name', 'age', first column, and last column
dv data.csv -F name age 1 -1

# Read all files (must be of same format and same structure) into one single table
dv data-1.csv data-2.csv --all-in-one

# Filter rows before opening the TUI using a Polars expression
dv data.csv --expr '$age > 30'

# Filter data using SQL query (use 'self' as the table name)
dv data.csv --sql 'SELECT * FROM self WHERE age > 30'

# Convert to other format
dv data.csv -o data.parquet

Keyboard Shortcuts

App-Level Controls

File & Tab Management

Key	Action
q	Quit current tab (prompts to save unsaved changes) or view
Q	Quit all tabs then app (prompts to save unsaved changes)
Ctrl+Q	Force to quit app (discards unsaved changes)
Esc	Force to quit current tab (discards unsaved changes) or view
Space	Toggle tab bar visibility
b	Next tab
B	Previous tab
>	Move current tab right (wrap to first)
<	Move current tab left (wrap to last)
Ctrl+T	Save current tab to file
Ctrl+S	Save all tabs to file
Ctrl+V	Save current view to file
w	Save current tab to file (overwrite without prompt)
W	Save all tabs to file (overwrite without prompt)
Ctrl+D	Duplicate current tab
Ctrl+O	Open file in a new tab
Ctrl+N	Create new tab from Polars expression
Double-click	Rename tab

Tips:

• Tabs with unsaved changes are indicated with a bright background
• Closing a tab with unsaved changes triggers a save prompt
• Use Esc to bypass save prompts and quit the current tab/view immediately

View & Settings

Key	Action
F1	Toggle help panel
k	Select theme
` (backtick)	Toggle Python console
Ctrl+P -> Screenshot	Capture terminal view as a SVG image

---

Table-Level Controls

Navigation

Key	Action
g	Go to first row
G	Go to last row
Ctrl + G	Go to specific row
↑ / ↓	Move up/down one row
← / →	Move left/right one column
Home / End	Go to first/last column
Ctrl + Home / Ctrl + End	Go to page top/bottom
PageDown / PageUp	Scroll down/up one page
Ctrl+F	Page forward
Ctrl+B	Page backforward

Undo/Redo/Reset

Key	Action
u	Undo last action
U	Redo last undone action
Ctrl+U	Reset to initial state

Display

Key	Action
Enter	Show details for the current row as two-column key–value pairs
Tab	Show current cell details; press Tab again there to drill deeper
F	Show frequency distribution for current column
i	Show histogram for current column
I	Show histogram for current column with custom bins
s	Show statistics for current column
S	Show statistics for entire dataframe
=	Show histogram using first column as label and current column as value
m	Show metadata for columns (e.g., data types)
K	Cycle cursor types: cell → row → column → cell
~	Toggle row labels
_ (underscore)	Toggle column full width
+	Toggle freeze rows and/or columns
,	Toggle thousand separator for numeric display
^	Toggle internal row index column (RID)
&	Set current row as the new header row
h	Hide current column
H	Show all hidden columns

Editing

Key	Action
Double-click	Edit cell or rename column header
Delete	Clear current cell (set to NULL)
Shift+Delete	Clear current column (set matching cells to NULL)
e	Edit current cell (respects data type)
E	Edit entire column with value/expression
a	Add empty column after current
A	Add column with name and value/expression
@	Add a link column from URL template
- (minus)	Delete current column
x	Delete current row
X	Delete current row and all those below
Ctrl+X	Delete current row and all those above
d	Duplicate current column
D	Duplicate current row
Ctrl+Delete	Remove duplicate rows (keep first occurrence)
o	Explode current list column into multiple rows
O	Explode current string column by delimiter into multiple rows

Row Selection

Key	Action
\	Select rows wth cell matches or those matching cursor value in current column
| (pipe)	Select rows by expression
{	Go to previous selected row
}	Go to next selected row
' (single quote)	Select/deselect current row
t	Toggle row selections (invert)
T	Clear all row selections and/or cell matches

Find & Replace

Key	Action
/	Find across all columns with cursor value and highlight matching cells
?	Find across all columns with expression and highlight matching cells
;	Find in current column with cursor value and highlight matching cells
:	Find in current column with expression and highlight matching cells
n	Go to next matching cell
N	Go to previous matching cell
r	Find and replace in current column (interactive or replace all)
R	Find and replace across all columns (interactive or replace all)

Filter & Collect

Key	Action
v	Basic filter using the current cell value
V	Advanced filter with value or expression
.	Filter rows with non-null values in the current column
f	Filter rows using values in the current column
" (double quote)	Collect rows to a new tab

Sorting (supporting multiple columns)

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 Casting

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

Copy

Key	Action
c	Copy current cell to clipboard
Ctrl+C	Copy column to clipboard
Ctrl+R	Copy row to clipboard (tab-separated)

SQL Interface

Key	Action
l	Simple SQL interface (select columns & where clause)
L	Advanced SQL interface (full SQL query with syntax highlight)

Features in Detail

1. Display & UI

Columns are automatically styled based on their data types:

Data Type	Text Color	Alignment
integer	Cyan	right
float	Yellow	right
string	Green	left
boolean	Blue	centered
temporal	Magenta	centered

Hide/Show Columns (h / H):

• h - Temporarily hide current column (data preserved)
• H - Restore all hidden columns

Freeze Rows and Columns (+):

• Toggle frozen rows and/or columns to keep important headers and fields visible while scrolling

Thousand Separator Toggle (,):

• 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

Cursor Type Cycling (K):

• Cycles through cell, row, and column selection modes
• Use it to switch between inspecting a single cell, an entire row, or an entire column

2. Row Detail View

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

In the Row Detail Modal:

• Press v to filter all rows containing the selected column value
• Press " to collect all rows containing the selected column value to a new tab
• Press { to move to the previous row
• Press } to move to the next row
• Press F to show the frequency table for the selected column
• Press s to show the statistics table for the selected column
• Press Tab to open a cell-detail modal for the selected field
• Press q or Escape to close the modal

3. Cell Detail View

Press Tab in the main table to inspect the current cell in its own modal.

You can also press Tab from the Row Detail modal to drill into the selected field.

Inside the cell-detail modal, press Tab again on the selected row/column to keep drilling into nested values.

• Scalar values are displayed inline. For long text, press Tab again to view the full content in a multi-line modal.
• String values are split into multiple rows using | by default
• List-like values are expanded into a one-column table
• Dict-like values are shown as key/value columns
• Press q or Escape to close the modal

4. Row Selection

The application provides multiple modes for selecting rows (marks it for filtering or collecting):

• \ - Select rows with cell matches or those matching cursor value in current column (respects data type)
• | - Opens dialog to select rows with custom expression
• ' - Select/deselect current row
• t - Flip selections of all rows
• T - Clear all row selections and cell matches
• { - Go to previous selected row
• } - Go to next selected row

Advanced Options:

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

• Match option Nocase for case-insensitive matching
• Match option Whole to match full text
• Match option Literal to ignore special regex characters
• Match option Reverse to perform reverse match

These options work with plain text searches. Use Polars regex patterns in expressions for more control. For example, use (?i) prefix in regex (e.g., (?i)john) for case-insensitive matching.

Quick Tips:

• Search results highlight matching rows in red
• Use expression for advanced selection (e.g., $attack > $defense)
• Type-aware matching automatically converts values. Resort to string comparison if conversion fails
• Use u to undo any search or filter

5. Find & Replace

Find by value/expression and highlight matching cells:

• / - Find cursor value across all columns (global search)
• ? - Open dialog to search all columns with expression (global search)
• ; - Find cursor value within current column (respects data type)
• : - Open dialog to search current column with expression
• n - Go to next matching cell
• N - Go to previous matching cell

Replace values in current column (r) or across all columns (R).

How It Works:

When you press r or R, enter:

1. Find term: Value or expression to search for (done by string value)
2. Replace term: Replacement value
3. Matching options:
   • Nocase for case-insensitive matching
   • Whole to match full text
   • Literal to ignore special regex characters
   • Reverse to perform reverse match
4. Replace mode: All at once or interactive review

Replace All:

• Replaces all matches with one operation
• Shows confirmation with match count

Replace Interactive:

• Review each match one at a time (confirm, skip, or cancel)
• Shows progress

Tips:

• Search are done by string value (i.e., ignoring data type)
• Type NULL to find or replace null values
• Support undo (u)

6. Filter & Collect

Both actions work on a subset of the original dataframe, but they serve different workflows.

Filtering options:

Basic Filter (v):

• Opens the chose subset as a derived view inside the current workflow
• Edits made in the filtered view still apply to the original dataframe
• Press Ctrl+V to save the current view to a file
• Press q to leave the filtered view and return to the main table
• Supports undo with u

Advanced Filter (V):

• Opens a dialog for value-based or expression-based filtering
• Useful when you want to define the subset directly

Column Filter (f):

• Opens a type-aware filter dialog for the current column
• Numeric columns support =, !=, <, <=, >=, and >
• String columns support exact match, prefix, suffix, contains, and regex matching
• Boolean columns support true, false, and null filtering
• Temporal columns support the same comparison operators as numeric columns
• List columns support exact-list matching and item membership checks such as "contains"

Collect ("):

• Creates a separate tab containing only the chosen rows
• The collected tab is independent from the source dataframe
• Edits in the collected tab do not modify the original table

For Basic Filter (v) and Collect ("), rows are chosen in this order:

• Use selected rows if any are present
• Otherwise, use rows with active matches from search or find
• Otherwise, use rows whose current-column value matches the current cell

7. Polars Expressions

Complex values, filters, and advanced operations 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)
• $`col name` - Column by name with spaces (backtick quoted)

Row References:

• $# - Current row index (1-based)

DataFrame References:

• self - Current dataframe

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'
• $# <= 10 - Top 10 rows

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
• $`product id` > 100 - Product ID with spaces in column name greater than 100
• self.drop(RID).is_duplicated() - Duplicate rows (note: the internal RID column must be excluded)

String Operations: (Polars string API reference)

• $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"
• $name.str.len_chars() < 7 - Length of name shorter than 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 values
• $column.is_not_null() - Find non-null values
• NULL - a value to represent null for convenience

Tips:

• Use column indices (e.g., $1, $2) for faster column access.
• Use column names that match exactly (case-sensitive)
• Use parentheses to clarify complex expressions: ($a & $b) | ($c & $d)

8. 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 current column from sorting

9. Column Metadata

View quick metadata about your columns to understand their structure and content.

Column Metadata (m):

• Press m to open a modal displaying details for all columns:
  • Column - Column name
  • Type - Data type (e.g., Int64, String, Float64, Boolean)

In the Column Metadata Table

• Press F to show the frequency table for the selected column
• Press s to show the statistics table for the selected column

In Metadata Modals:

• Press q or Escape to close

10. Frequency Distribution

Press F to see value distributions of the current column. The modal shows:

• Value, Count, Percentage, Histogram
• Total row at the bottom

In the Frequency Modal:

• Press [ and ] to sort by any column (value, count, or percentage)
• Press v to filter all rows containing the selected value
• Press " to collect all rows containing the selected value to a new tab
• Press , to toggle thousand separator for numeric values
• Press g to scroll to top
• Press G to scroll to bottom
• Press Ctrl+S to save the frequency table to file
• Press q or Escape to close the modal

This is useful for:

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

11. Column & Dataframe Statistics

Show summary statistics such as count, null count, mean, median, standard deviation, min, and max using Polars' describe() method.

• s shows statistics for the current column, with an additional sum field for numeric columns
• S shows statistics for all columns in the dataframe

In the Statistics Modal:

• Use arrow keys to navigate
• Press q or Escape to close the modal

This is useful for:

• Understanding data distributions and overall column behavior
• Identifying outliers and anomalies
• Checking data quality quickly
• Reviewing summary statistics without leaving the TUI
• Comparing columns at a glance

12. 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 display and dataframe

Add Empty Column (a):

• Adds a new empty column after the current column
• Column is initialized with NULL values for all rows

Add Column with Value/Expression (A):

• Opens dialog to specify column name and initial value/expression
• Value can be a constant (e.g., 0, "text") or a Polars expression (e.g., $age * 2)
• Expression can reference other columns and perform calculations
• Useful for creating derived columns or adding data with formulas

Duplicate Column (d):

• Creates a new column immediately after the current column
• New column has '_copy' suffix (e.g., 'price' → 'price_copy')
• Useful for creating backups before transformation

Duplicate Row (D):

• Creates a new row immediately after the current row
• Duplicate preserves all data from original row
• Useful for batch adding similar records

Remove Duplicate Rows (Ctrl+Delete):

• Removes duplicated rows while keeping the first occurrence
• Compares row values across the visible columns
• Useful for quick deduplication before further editing, collecting, or exporting
• Supports undo with u

13. 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

14. Save File

The application provides separate save actions for the current tab, all tabs, and the current filtered view.

Save Current Tab (Ctrl+T):

• Saves the active tab to file
• Useful when you want to export only the dataframe you are currently working on

Save All Tabs (Ctrl+S):

• Saves every open tab to file
• Useful after editing multiple datasets in the same session

Save Current View (Ctrl+V):

• Saves only the current filtered or derived view to file
• Useful for exporting a subset without replacing the source dataframe

The output format is determined by the file extension, making it easy to convert between formats such as CSV, TSV, Parquet, or Excel.

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. 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
• Access to all SQL capabilities for complex transformations
• Always use self as the table name
• Syntax highlighted

Examples:

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

-- Use backticks (`) for column names with spaces
SELECT *
FROM self
WHERE `product id` = 7

18. Clipboard Operations

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

Note: may require a X server to work.

• Press c to copy cursor value
• Press Ctrl+C to copy column values
• Press Ctrl+R to copy row values (delimited by tab)
• Hold Shift to select with mouse

19. Link Column Creation

Press @ to create a new column containing dynamically generated URLs using template. Links are typically clickable in a terminal emulator using Ctrl+Click.

Template Placeholders:

The link template supports multiple placeholder types for maximum flexibility:

• $_ - Current column (the column where cursor was when @ was pressed), e.g., https://example.com/search/$_ - Uses values from the current column

• $1, $2, $3, etc. - Column by 1-based position index, e.g., https://example.com/product/$1/details/$2 - Uses 1st and 2nd columns

• $name - Column by name (use actual column names), e.g., https://example.com/$region/$city/data - Uses region and city columns

Features:

• Multiple Placeholders: Mix and match placeholders in a single template
• URL Prefix: Automatically prepends https:// if URL doesn't start with http:// or https://

Tips:

• Use full undo (u) if template produces unexpected URLs
• For complex multi-column URLs, use column names ($name) for clarity over positions ($1)

20. Python Console

Use the built-in Python console for quick interactive transformations without leaving the TUI.

• Open/close console: `
• Run shell commands: prefix with ! (example: !ls)
• In console: clear or cls clears console output
• In console: Esc closes the console panel
• Available names: df (active DataFrame), self (active table), app (viewer app), pl (Polars)
• Assign a DataFrame or Series back to df to refresh the current table immediately

Handling Loading Errors

Most loading failures come from malformed CSV/TSV input, quoting issues, or mixed column types. When this happens, the application prints a hint with a suggested retry option.

Common fixes:

• Use -Q if quote characters are mismatched, improperly escaped, or should be ignored entirely
• Use -T when rows contain more fields than expected and you want to truncate ragged lines
• Use -L to increase the number of rows used for schema inference when early rows do not represent the full column types
• Use -I to disable type inference and read CSV/TSV values as strings
• Check the delimiter and format options if the input appears empty or unreadable
• Use -E as a last resort to ignore recoverable parsing errors

Typical cases:

Malformed CSV or broken quoting:

• Symptom: errors mentioning malformed CSV, mismatched quotes, or improperly escaped fields
• Try: -Q to disable quoting or choose a different quote character

Ragged lines or inconsistent field counts:

• Symptom: errors saying the input has more fields than defined in the schema
• Try: -T to truncate ragged lines

Mixed data types in one column:

• Symptom: errors saying a value could not be parsed as an integer, float, or other inferred type at a specific column
• Try: -L first, then -I if the column is genuinely mixed

No data could be loaded:

• Symptom: errors indicating that no data was available to load
• Check: whether the file is empty, the format is correct, and the delimiter matches the input

Fallback option:

• If none of the above helps and the file is mostly usable, retry with -E to ignore parsing errors

Examples:

# Disable quote handling when CSV quoting is broken
dv bad.csv -Q

# Truncate ragged lines
dv messy.tsv -T

# Increase schema inference depth
dv mixed_types.csv -L 1000

# Disable type inference entirely
dv mixed_types.csv -I

# Ignore recoverable parsing errors
dv partially_broken.csv -E

Dependencies

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