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SheetTool Guide

A practical guide to what SheetTool can do, when each tool helps, and how to combine features into repeatable cleanup workflows. Everything runs in your browser with no uploads or sign-ups.

Start with a workflow

If you are not sure which feature to use, start from the outcome you want.

Clean a messy vendor export

Use this when a supplier, CRM, or marketplace export has inconsistent casing, blank rows, duplicates, and odd formatting.

1. Import2. Clean3. Validate4. Export

Import & Export

Start here when you need to get data into SheetTool or choose the right download format for the next system in your workflow.

  • Open CSV files with auto-detected delimiters (comma, semicolon, tab, pipe).
  • Open Excel workbooks (.xlsx, .xls) with multi-sheet support.
  • Load JSON arrays of objects as spreadsheet rows.
  • Drag and drop files onto the editor to load instantly.
  • Paste tables from Excel, Google Sheets, or any HTML source — auto-detected.
  • Export as CSV with your choice of delimiter.
  • Export as Excel (.xlsx), JSON, TSV, Markdown table, or styled PDF.

Useful examples

  • Convert an Excel workbook to CSV before importing it into a database or ecommerce platform.
  • Paste a table copied from a webpage, clean it, then export it as Markdown for documentation.
  • Open a pipe-delimited report from an older internal system and save it as standard CSV.

Tips

  • Use CSV for broad compatibility, Excel when you need multi-sheet handoff, JSON when the next step is code or an API, and PDF when the output is for review only.
  • Check the delimiter before exporting if the destination system expects semicolons, tabs, or pipes.

Spreadsheet Editing

Use the grid for quick manual fixes, column setup, row review, and spreadsheet-style edits before applying bulk tools.

  • Click any cell to edit. Press Enter to confirm, Escape to cancel.
  • Click and drag to select a range of cells.
  • Double-click column headers to rename them.
  • Insert or delete rows and columns at any position.
  • Copy and paste with Ctrl+C / Ctrl+V — supports multi-cell data from Excel.
  • Full undo/redo history with Ctrl+Z and Ctrl+Shift+Z.
  • Drag column borders to resize. Double-click to auto-fit.
  • Virtual scrolling handles 100,000+ rows smoothly.
  • Work with multiple sheets — add, rename, duplicate, or delete tabs.

Useful examples

  • Rename imported headers so they match an upload template exactly.
  • Insert a temporary review column such as status, owner, or notes before exporting.
  • Duplicate a sheet before trying a destructive cleanup so you can compare the result.

Tips

  • Do structural edits like header renames and column order changes before formulas, validation, or macros.
  • Use undo freely while exploring a cleanup path, then export once the result looks right.

Sorting & Filtering

Use sorting and filters to narrow a large file to the rows that need attention.

  • Click column headers to sort ascending or descending (auto-detects numbers vs. text).
  • Filter rows by text: contains, equals, starts with, ends with, empty, not empty.
  • Filter rows by number: =, ≠, >, <, ≥, ≤.
  • Stack multiple filters across different columns.
  • Save filter + sort configurations as named views and reload them later.

Useful examples

  • Find products where inventory is below zero and status is still active.
  • Filter customers with missing email addresses before sending the file to a CRM.
  • Save a named view for open issues so you can come back to the same review slice later.

Tips

  • Stack filters from broad to narrow: first isolate the record type, then the error condition.
  • Sort after filtering when you need to spot clusters, outliers, or repeated values quickly.

Search & Replace

Use search and replace for consistent text fixes across a sheet or within columns.

  • Search across all cells with live match highlighting.
  • Toggle case-sensitive or case-insensitive matching.
  • Use regular expressions for advanced patterns.
  • Replace all matches at once or step through them one by one.
  • Navigate between matches with next/previous controls.

Useful examples

  • Replace deprecated category names before uploading a product catalog.
  • Normalize state names, status labels, or common abbreviations across a customer list.
  • Remove repeated tracking parameters from exported URLs.

Tips

  • Step through replacements when the pattern is broad or the data is customer-facing.
  • Use case-sensitive matching when fixing codes where ABC and abc mean different things.

Data Cleaning

Use cleaning tools when the file structure is mostly right but the values are messy.

  • Trim leading and trailing whitespace from every cell.
  • Remove completely empty rows.
  • Remove duplicate rows across the entire sheet.
  • Remove duplicate values within a specific column.
  • Auto-detect inconsistencies: extra whitespace, casing variations, date format mismatches.
  • Transpose rows and columns (swap wide to tall or vice versa).

Useful examples

  • Clean a lead list that has duplicate submissions and blank rows from form exports.
  • Fix inconsistent date formatting before building a pivot table or summary report.
  • Transpose copied data when a report arrives with months across columns but the next tool expects rows.

Tips

  • Trim whitespace before deduplicating. Hidden spaces can make duplicate rows look different.
  • Review detected inconsistencies before applying bulk changes to identifiers, SKUs, or account codes.
Learn more

Column Transforms

Use transforms for simple one-column cleanup that should apply to every visible row.

  • UPPERCASE — convert all values in a column to uppercase.
  • lowercase — convert to lowercase.
  • Title Case — capitalize the first letter of each word.
  • TRIM — remove extra whitespace.
  • ABS — convert negatives to absolute values.
  • ROUND — round numeric values to the nearest integer.

Useful examples

  • Standardize country codes, states, tags, or departments to one casing style.
  • Round forecast values before sharing a planning file.
  • Convert negative adjustment values to absolute values for a reconciliation report.

Tips

  • Use transforms for direct cleanup and formulas when the result depends on multiple columns.
  • Apply transforms after filtering if only a subset of rows should change.

Formulas & Calculated Columns

Use formulas when a new value depends on existing columns, conditions, or text combinations.

  • Reference columns with [Column Name] syntax.
  • Arithmetic: +, -, *, /. Functions: SUM, ROUND, ABS.
  • Text functions: CONCAT, UPPER, LOWER, TRIM, LEN.
  • Conditional logic: IF(condition, true_value, false_value).
  • Comparisons: ==, !=, >, <, >=, <=.
  • Pre-built formula templates for common calculations (price × quantity, name concatenation, etc.).

Useful examples

  • Create a total column with [Price] * [Quantity].
  • Build a display name by combining first name, last name, and company.
  • Flag rows with IF([Inventory] <= 0, "Review", "OK") before export.

Tips

  • Rename columns clearly before writing formulas so references are easier to read.
  • Use calculated columns for review flags instead of overwriting source values immediately.
Learn more

Type Casting & Conversion

Use type casting when values look right to a person but need a consistent machine-readable format.

  • Text: UPPERCASE, lowercase, Title Case, TRIM, strip formatting.
  • Numbers: fixed decimals, currency ($1,234.56), percentage (50%).
  • Parse numbers from formatted text (remove $, %, commas).
  • Reformat dates between ISO (YYYY-MM-DD), US (MM/DD/YYYY), EU (DD/MM/YYYY), and locale default.

Useful examples

  • Turn $1,299.00 into 1299 before importing prices into a system that expects numbers.
  • Convert 50% to a consistent percentage format for reporting.
  • Standardize dates to ISO format before exporting to a database or API.

Tips

  • Use ISO dates when another system will parse the file automatically.
  • Parse formatted numbers before sorting or filtering numeric ranges.

Regex Transforms

Use regex when the text pattern is consistent but ordinary search and replace is not specific enough.

  • Replace mode — find and replace using regex patterns with capture groups.
  • Extract mode — pull matching portions of text into new values.
  • Built-in presets: extract numbers, emails, area codes, domains. Remove HTML tags or extra spaces.
  • Toggle global (g) and case-insensitive (i) flags.
  • Live preview on sample data before applying.

Useful examples

  • Extract domains from email addresses for account grouping.
  • Pull order IDs or ticket numbers out of mixed notes fields.
  • Remove HTML tags from exported product descriptions.

Tips

  • Start with built-in presets, then adjust the pattern once the preview looks close.
  • Avoid applying a regex globally until you have checked examples from short, long, empty, and unusual rows.
Learn more

Merge & Split Files

Use merge and split when file size, source count, or upload limits are the main problem.

  • Merge 2+ CSV/Excel files with automatic column matching by header name.
  • Union of all headers — missing values become empty cells.
  • Split large files into smaller chunks by row count.
  • Download split files as a ZIP archive with numbered parts.

Useful examples

  • Combine monthly sales exports into one year-to-date file.
  • Merge regional inventory files that have mostly the same headers.
  • Split a large CSV into smaller chunks for an upload form with row limits.

Tips

  • Normalize header names before merging so Product ID and product_id do not become separate columns.
  • After merging, filter for empty cells in important columns to catch mismatched source files.

Compare Files (Diff)

Use compare when you need to understand what changed between two exports or before replacing production data.

  • Side-by-side comparison of two CSV or Excel files.
  • Match rows by index or by a shared key column.
  • Color-coded highlighting: green (added), red (removed), yellow (modified), gray (unchanged).
  • Filter to show only changed rows.

Useful examples

  • Compare yesterday's and today's inventory files to find unexpected stock changes.
  • Review a new price list against the previous version before sending it downstream.
  • Find removed customers or products by matching on a stable ID column.

Tips

  • Match by a unique key column whenever possible. Row order can change between exports.
  • Filter to changed rows first, then inspect added, removed, and modified rows separately.
Learn more

Pivot Tables

Use pivot tables to summarize granular rows into totals, counts, averages, and cross-tab reports.

  • Drag-and-drop row, column, and value field configuration.
  • Aggregation types: SUM, COUNT, AVG, MIN, MAX.
  • Automatic grand totals for rows and columns.
  • Export the pivot result as CSV or Excel.

Useful examples

  • Summarize revenue by month and region from a transaction export.
  • Count support tickets by status and assignee.
  • Average order value by product category.

Tips

  • Clean dates, currencies, and percentages before building the pivot so aggregation works correctly.
  • Use COUNT first to validate row coverage, then switch to SUM or AVG once the grouping looks right.
Learn more

Unpivot (Melt)

Use unpivot when a report is too wide and each repeated column should become a row.

  • Convert wide-format data to long-format.
  • Select ID columns (stay unchanged) and value columns (become rows).
  • Custom variable and value column names.
  • Preview the result before applying.

Useful examples

  • Turn Jan, Feb, Mar sales columns into rows with Month and Sales columns.
  • Convert survey answer columns into one response-per-row data.
  • Prepare spreadsheet reports for BI tools that expect tidy long-format data.

Tips

  • Keep stable identifiers like customer, product, location, and account as ID columns.
  • Name the variable column after what the old headers represent, such as Month, Metric, or Question.

Charts & Visualization

Use charts for quick inspection, not just presentation. A chart can reveal outliers, missing categories, or wrong units faster than scanning rows.

  • Bar chart — grouped or stacked for categorical comparisons.
  • Line chart — trends over a continuous axis.
  • Pie chart — proportional category distribution.
  • Scatter plot — correlation between two numeric columns.
  • Interactive tooltips on hover.
  • Automatic numeric vs. categorical axis detection.

Useful examples

  • Plot sales by month to spot a missing reporting period.
  • Use a scatter plot to inspect the relationship between price and margin.
  • Chart category counts before and after cleanup to verify consolidation worked.

Tips

  • Filter rows before charting when you only need one segment, region, or time period.
  • Use charts as a sanity check before exporting a summarized report.
Learn more

SQL Query Engine

Use SQL when filtering, grouping, sorting, or selecting columns is easier to express as a query than clicks.

  • Write SQL queries against your loaded data (table name: data).
  • Supports SELECT, WHERE, GROUP BY, ORDER BY, DISTINCT, TOP N.
  • Aggregates: SUM, COUNT, AVG, MIN, MAX.
  • Built-in query templates to get started quickly.
  • Push query results back into the spreadsheet.

Useful examples

  • Select only active customers with missing emails for a cleanup queue.
  • Group orders by region and month with SUM and COUNT.
  • Create a smaller export with only the columns another system accepts.

Tips

  • Use SELECT * first to confirm column names, then narrow the query.
  • Push query results back into the sheet when the query output becomes your new working dataset.
Learn more

Data Validation

Use validation to turn quality rules into visible errors and warnings before export.

  • Required — ensure columns have no empty cells.
  • Email — validate email format.
  • Number — verify values are numeric with optional min/max.
  • Length — minimum and maximum text length.
  • Regex — validate against custom patterns.
  • Unique — no duplicate values allowed.
  • Severity levels: error (blocking) or warning (advisory).

Useful examples

  • Require SKU, price, and title before uploading a product import.
  • Validate email format in a lead list before sending it to a CRM.
  • Enforce uniqueness for order IDs, customer IDs, or usernames.

Tips

  • Use errors for rules that would break an import and warnings for items that need human review.
  • Run validation after cleanup and type casting so false positives are reduced.
Learn more

Conditional Formatting

Use conditional formatting to make records needing review stand out while keeping all rows visible.

  • Highlight cells matching conditions with background colors.
  • Conditions: empty, not empty, equals, contains, greater than, less than, duplicates.
  • Apply to specific columns or all columns.
  • Stack multiple formatting rules.

Useful examples

  • Highlight blank required fields without hiding complete rows.
  • Mark negative quantities, high discounts, or duplicate IDs for review.
  • Apply a color to records with status values like Error, Hold, or Needs Review.

Tips

  • Use formatting for visual review and validation for rules you want to enforce.
  • Keep rule colors distinct so duplicates, blanks, and threshold problems are easy to tell apart.

Data Anonymization

Use anonymization before sharing data for support, debugging, demos, or analysis when personal details are not needed.

  • Auto-detects columns with emails, phone numbers, and names.
  • Email masking: john@example.com → john****@example.com.
  • Phone masking: (555) 123-7890 → (***) ***-7890.
  • Name masking: John Smith → John S.
  • Preview masked results before applying.

Useful examples

  • Share a customer export with a contractor while preserving enough structure for analysis.
  • Create demo data from a real support case without exposing full personal details.
  • Mask names and contact details before attaching a file to a bug report.

Tips

  • Preview masked results before export, especially when names or emails appear in notes fields.
  • Anonymize after deduping if duplicates should still be detected using the original values.

Statistics & Profiling

Use profiling to understand a file before changing it. It helps find missing values, type problems, and outliers.

  • Column stats: row count, unique values, empty cells, data type distribution.
  • Type consistency percentage for each column.
  • Distribution histogram for numeric columns.
  • IQR-based outlier detection with highlighted values.
  • Summary reports: group by a column and aggregate (Count, Sum, Avg, Min, Max).

Useful examples

  • Check whether a price column contains text, blanks, or invalid values.
  • Find unusually large orders or negative quantities before reporting totals.
  • Compare unique counts to row counts to understand whether a column can be used as a key.

Tips

  • Profile before and after cleanup to confirm the file improved.
  • Investigate outliers before removing them. Sometimes the unusual value is the important one.

Macro Recorder

Use macros when you repeat the same cleanup sequence on similar files.

  • Record sequences of operations (transforms, sorts, deduplication, search & replace).
  • Replay saved macros on any sheet with one click.
  • Macros persist in your browser across sessions.
  • Save, rename, and delete recorded macros.

Useful examples

  • Apply the same cleanup steps to a weekly vendor catalog.
  • Replay a standard CRM export cleanup before handing the file to sales operations.
  • Record a sequence that trims whitespace, fixes casing, removes blanks, and sorts by status.

Tips

  • Record macros after you have tested the steps manually once.
  • Keep macros focused. A short macro for one file family is easier to trust than a broad all-purpose macro.
Learn more

Changelog & Audit

Use the changelog when you need a record of edits, either for your own review or for handoff.

  • Tracks header renames, cell edits (old → new), row/column additions and removals.
  • Timeline view of all modifications.
  • Export the full changelog as CSV.

Useful examples

  • Document which rows were edited before sending a corrected file back to a teammate.
  • Keep an audit trail for price, status, or inventory changes.
  • Review accidental edits by checking old and new values.

Tips

  • Export the changelog with the final file when someone else needs to approve the changes.
  • Use the changelog alongside compare when reviewing a revised version against the original.

Auto-Save & Recovery

Use auto-save as a safety net while working locally in the browser.

  • Auto-saves to IndexedDB every 2 seconds (debounced).
  • Reload the page and your session is restored automatically.
  • Saves data, column widths, filters, sorting, validation rules, and conditional formatting.

Useful examples

  • Recover work after a browser refresh or accidental tab close.
  • Pause a cleanup session and continue later without uploading the file anywhere.

Tips

  • Export important finished work instead of treating browser storage as long-term backup.
  • Clear browser site data only after exporting files you still need.

Keyboard Shortcuts

  • Navigation: Arrow keys, Tab, Shift+Tab, Ctrl+Home, Ctrl+End, Page Up/Down.
  • Edit: Enter (edit cell), Delete (clear), Ctrl+C (copy), Ctrl+V (paste).
  • Undo/Redo: Ctrl+Z / Ctrl+Shift+Z.
  • Rows: Ctrl+I (insert), Ctrl+Shift+K (delete).
  • File: Ctrl+S (export), Ctrl+Shift+S (export as), Ctrl+F (find), Ctrl+H (replace).
  • Tools: Ctrl+M (merge), Ctrl+D (compare), Ctrl+J (summary), Ctrl+\ (formula).
  • Sort: Alt+↑ (ascending), Alt+↓ (descending).
  • Help: Ctrl+/ opens the shortcuts panel.
Learn more

Privacy & Security

Use SheetTool for sensitive spreadsheets when browser-local processing is important.

  • 100% client-side — all processing happens in your browser.
  • Files are never uploaded to any server.
  • No account, no sign-up, no login required.
  • Session data stays in IndexedDB and localStorage on your device.

Useful examples

  • Clean customer lists, private reports, or internal exports without uploading them to a conversion service.
  • Anonymize data before sharing a sample with support or a contractor.

Tips

  • Because processing is local, very large files depend on your device and browser memory.
  • Use anonymization before sharing exports that no longer need direct identifiers.
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