⚙️ Settings
Delimiter Converter — Convert Comma, Pipe, Tab & Custom-Separated Lists
The Delimiter Converter turns a plain list of values into whatever separated format you need — comma-separated (CSV), pipe-delimited, tab-separated, or a custom character of your choice. It's built for analysts, developers, and QA engineers who need to convert a list to comma separated values for a spreadsheet, reformat a tab separated to comma separated export, or quickly wrap each item in quotes for a SQL IN (...) clause. Paste your list, pick a delimiter, and get a clean result instantly — no spreadsheet software or scripting required.
⚡ Key Takeaways
- Converts between comma, pipe, tab, and custom delimiters in one click — no spreadsheet needed.
- Optional quoting and wrapping build a ready-to-paste SQL IN (...) clause or code array instantly.
- Duplicate removal and whole-output wrapping are built in, not separate steps.
- Everything runs in your browser — your list is never uploaded anywhere.
What, Who, When & Why
| What it's for | Converts a list of values into a different delimiter format — comma, pipe, tab, or a custom character — with optional quoting and wrapping. |
|---|---|
| Who it's for | Analysts, developers, and QA engineers who regularly move data between spreadsheets, SQL queries, and code. |
| When to use it | Whenever you've copied a column of values and need it reshaped into a different separated format, or wrapped in quotes for a query. |
| Why it's needed | Manually retyping, quoting, or re-separating dozens or hundreds of values by hand is slow and error-prone — one missed comma or quote breaks the whole result. |
| Best way to use it | Paste your raw list first, then toggle quoting and wrapping options one at a time so you can see exactly how each setting changes the output before copying. |
How to Use the Delimiter Converter
- Paste your list into the Input box — one value per line, or already separated by some delimiter.
- Choose your input delimiter (or let it auto-detect line breaks) and your desired output delimiter — comma, pipe, tab, or a custom character.
- Turn on optional settings: wrap each value in quotes, add a prefix/suffix per item (like parentheses for SQL), remove duplicates, or wrap the whole output in brackets.
- Click Convert to see the result appear instantly in the output box.
- Click Copy to copy the converted list to your clipboard, ready to paste elsewhere.
Inputs and Results
The Input box accepts any list of text — one item per line is the most common format, but the tool can also split on an existing delimiter if your data is already separated by commas, pipes, or tabs. The Output box shows the converted result using your chosen delimiter and formatting options. A live item count above each box shows exactly how many values are being processed, so you can quickly confirm nothing was dropped.
Example: Converting a List to Comma-Separated Values
Suppose you have a column of customer names copied from a spreadsheet, one per line:
Alice Bob Charlie
With quotes enabled and comma selected as the output delimiter, the Delimiter Converter turns this into:
'Alice','Bob','Charlie'
Add the "Wrap Whole Output" option with ( and ) and you get a ready-to-paste SQL IN clause: ('Alice','Bob','Charlie') — a common task for anyone writing WHERE clauses against a list of known values.
Key Features
- Multiple delimiter options — comma, pipe, tab, newline, or any custom character.
- Quote wrapping — automatically wrap each value in single or double quotes.
- Per-item tags — add a custom prefix and suffix to every value (useful for building code snippets).
- Whole-output wrapping — enclose the entire result in brackets or parentheses in one click.
- Duplicate removal — strip repeated values before converting.
- Live item counts — see exactly how many values are in your input and output.
- Interval wrapping — group and wrap values at set intervals for more complex formatting needs.
- Runs entirely in your browser — nothing you paste is uploaded anywhere.
Practical Use Cases
- Building SQL queries: convert a column of IDs into a quoted, comma-separated list for a
WHERE id IN (...)clause. - Preparing CSV data: reformat a tab-separated export (common when copying from Excel) into true comma-separated values.
- Generating code arrays: wrap each line in quotes and commas to quickly build a JavaScript or Python array literal.
- Cleaning pasted data: remove duplicate entries from a list before importing it elsewhere.
- Reformatting for APIs: some APIs expect pipe- or tab-delimited input instead of comma-separated — this tool switches between them in one step.
Related Terminology
Delimiter: a character used to separate individual values in a list or file, such as a comma, pipe (|), or tab. CSV (comma-separated values): a plain-text format where each value is separated by a comma, widely used for spreadsheets and data exports. Delimited list: any sequence of values separated by a consistent character. Quoting: wrapping each value in single or double quotes, often required when values may contain the delimiter character itself or when preparing string literals for SQL or code.
Important Considerations
If your source values themselves contain the delimiter character you're converting to (for example, a name containing a comma), consider quoting each value to avoid ambiguity when the result is parsed elsewhere. Blank lines in your input are treated as empty values unless removed beforehand. All processing happens locally in your browser, so there's no file size limit imposed by a server — though extremely large lists (hundreds of thousands of lines) may take a moment to process.
Related Tools
If you're working with structured data, the CSV ⇄ JSON Converter can take your comma-separated output further and turn it into JSON. For building SQL statements from a list, try the SQL Value Generator, which is purpose-built for wrapping values in a ready-to-use IN (...) clause. To clean up whitespace before converting, the Whitespace Trimmer is a handy first step.
Frequently Asked Questions
How do I convert a list to comma-separated values?
Paste your list into the Input box (one item per line), select comma as the output delimiter, and click Convert. The result appears in the Output box, ready to copy.
Can I wrap each value in quotes automatically?
Yes — enable the quote option in the settings panel and choose single or double quotes. Each value in the output will be wrapped automatically, which is especially useful when building SQL queries or code arrays.
How do I convert tab-separated data to comma-separated?
Paste your tab-separated data into the Input box, set the input delimiter to tab (or leave auto-detect on), set the output delimiter to comma, and click Convert.
Does this tool remove duplicate values?
Yes, there's an optional duplicate-removal setting that strips repeated values before the list is converted, so your output only contains unique entries.
Is my data uploaded anywhere when I use this tool?
No. All conversion happens directly in your browser using JavaScript — nothing you paste is sent to a server, which makes it safe to use with sensitive or internal data.
WITH recent_orders AS (
SELECT
customer_id,
order_id,
order_date,
total_amount
FROM orders
WHERE order_date >= DATEADD(day, -30, GETDATE())
)
SELECT
customer_id,
COUNT(order_id) AS order_count,
SUM(total_amount) AS total_spent
FROM recent_orders
GROUP BY customer_id
ORDER BY total_spent DESC;
WITH RECURSIVE employee_hierarchy AS (
-- Anchor: top-level rows (no manager)
SELECT
employee_id,
manager_id,
employee_name,
1 AS level
FROM employees
WHERE manager_id IS NULL
UNION ALL
-- Recursive: join children to their parent's result
SELECT
e.employee_id,
e.manager_id,
e.employee_name,
eh.level + 1
FROM employees e
INNER JOIN employee_hierarchy eh
ON e.manager_id = eh.employee_id
)
SELECT *
FROM employee_hierarchy
ORDER BY level, employee_name;
-- Note: SQL Server / Oracle: drop the RECURSIVE keyword (just WITH employee_hierarchy AS (...))
SELECT
o.order_id,
c.customer_name,
o.order_date,
p.product_name,
oi.quantity
FROM orders o
INNER JOIN customers c
ON o.customer_id = c.customer_id
LEFT JOIN order_items oi
ON o.order_id = oi.order_id
LEFT JOIN products p
ON oi.product_id = p.product_id
WHERE o.order_date >= '2026-01-01'
ORDER BY o.order_date DESC;
SELECT
DATE_TRUNC('month', order_date) AS order_month, -- PostgreSQL
-- FORMAT(order_date, 'yyyy-MM') AS order_month, -- SQL Server
-- DATE_FORMAT(order_date, '%Y-%m') AS order_month, -- MySQL
COUNT(*) AS order_count,
SUM(total_amount) AS revenue
FROM orders
GROUP BY DATE_TRUNC('month', order_date)
ORDER BY order_month;
SELECT
customer_id,
order_id,
order_date,
total_amount,
ROW_NUMBER() OVER (
PARTITION BY customer_id
ORDER BY order_date DESC
) AS order_rank,
SUM(total_amount) OVER (
PARTITION BY customer_id
ORDER BY order_date
ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
) AS running_total
FROM orders;
CREATE PROCEDURE GetCustomerOrders
@CustomerId INT,
@StartDate DATE = NULL,
@EndDate DATE = NULL
AS
BEGIN
SET NOCOUNT ON;
SELECT
order_id,
order_date,
total_amount
FROM orders
WHERE customer_id = @CustomerId
AND (@StartDate IS NULL OR order_date >= @StartDate)
AND (@EndDate IS NULL OR order_date <= @EndDate)
ORDER BY order_date DESC;
END;
-- Call it: EXEC GetCustomerOrders @CustomerId = 101, @StartDate = '2026-01-01';
MERGE INTO customers AS target
USING staging_customers AS source
ON target.customer_id = source.customer_id
WHEN MATCHED THEN
UPDATE SET
target.customer_name = source.customer_name,
target.email = source.email,
target.updated_at = GETDATE()
WHEN NOT MATCHED THEN
INSERT (customer_id, customer_name, email, created_at)
VALUES (source.customer_id, source.customer_name, source.email, GETDATE());
#FACC15
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Tools for analysts, developers & QA engineers
33 free, browser-based utilities — text and list tools, SQL helpers, converters, and small productivity apps. Everything runs locally; nothing you type or paste is ever uploaded.
For Analysts
Clean lists, build SQL fragments, and reshape data without opening a spreadsheet.
For Developers
Format SQL, convert data formats, and handle everyday text and encoding tasks.
For QA Engineers
Generate test data, compare text output, and sanitize queries before sharing them.