Transformation & Logic Tools
Welcome to our transformation & logic hub. Here you'll find all the tools you need to handle transformation & logic tasks efficiently and securely.
Why Choose Our Transformation & Logic Tools?
Our transformation & logic tools are designed with privacy and performance in mind. All processing happens locally in your browser, ensuring your data never leaves your device. Whether you're working with small datasets or files with millions of rows, our tools handle it all seamlessly.
Key Features
100% Private: All data processing happens in your browser Lightning Fast: Handle large files without performance issues No Installation: Works directly in your browser, no software to download Free to Use: Access all features without any cost Modern Interface: Intuitive design that makes complex tasks simple
Available Transformation & Logic Tools
Explore our complete toolkit with detailed guides and tutorials for each feature:
Whether you need to force convert columns to numbers, dates, or text, Data Type Converter is your go-to solution. Every column is scanned against your chosen target type using strict parsing rules: numeric strings with currency symbols, thousands separators, or trailing units are stripped and coerced to floats; date strings are matched against a list of common formats (ISO, US, EU) and normalized; anything else falls back to plain text. Values that fail to parse are flagged rather than silently dropped, so you can decide row by row whether to null them out, zero them, or leave the original text. Perfect for fixing a "quantity" column imported as text with values like "1,200 units" into a clean number, converting inconsistent date strings ("03/04/2024", "2024-04-03") from a merged export into a single date type, and more. Read more about Data Type Converter in our introduction guide, or follow our tutorial for step-by-step instructions. Try it now →
If you're looking to combine multiple excel sheets into one unified dataset. supports vertical stacking (append), horizontal joining (merge on common columns), and side-by-side concatenation. perfect for consolidating monthly reports, regional data, or multi-tab workbooks, Merge Excel Sheets is your go-to solution. Working with multi-sheet Excel workbooks? This powerful tool lets you merge multiple sheets into a single unified dataset. Perfect for consolidating monthly sales reports from different sheets, merging regional data from separate tabs, and more. Read more about Merge Excel Sheets in our introduction guide, or follow our tutorial for step-by-step instructions. Try it now →
When it comes to find differences between excel sheets with detailed comparison reports. identify unique rows, changed values, and generate visual diff reports. perfect for version control, audit trails, data validation, and reconciliation tasks, Compare Excel Sheets is your go-to solution. Need to spot differences between two datasets? This powerful comparison tool analyzes Excel sheets to find what changed. Perfect for comparing budget versions to track changes, auditing data modifications between releases, and more. Read more about Compare Excel Sheets in our introduction guide, or follow our tutorial for step-by-step instructions. Try it now →
For those who want to reduce file size by zipping csv and xlsx files, Compress CSV/XLSX is your go-to solution. The file is passed through a lossless DEFLATE/ZIP compression pass entirely in your browser, so the byte-for-byte content of your CSV or XLSX is preserved and just packed into a smaller archive. No rows, columns, or formatting are touched, only the container size shrinks, which is what matters when you are up against an email attachment limit or an upload quota. Perfect for shrinking a 40mb export below a 25mb email attachment limit, packing several xlsx reports into one archive before uploading to a portal with a file size cap, and more. Read more about Compress CSV/XLSX in our introduction guide, or follow our tutorial for step-by-step instructions. Try it now →
Whether you need to upload multiple csv/excel files, clean them all with one preset, and download as a single zip, Batch Processor is your go-to solution. You configure a cleaning preset once (trim whitespace, drop empty rows, standardize date formats, and so on), and the tool replays that exact same set of operations against every file you upload in the batch. Each file is processed independently so column differences between files don't break the run, and the cleaned outputs are packaged into a single ZIP for download. Perfect for cleaning 50 weekly regional export files with identical formatting issues in one pass, standardizing a folder of vendor invoices before loading them into an accounting system, and more. Read more about Batch Processor in our introduction guide, or follow our tutorial for step-by-step instructions. Try it now →
If you're looking to apply math formulas (a + b) to create new data, Calculated Columns is your go-to solution. You write an expression referencing existing column names (like Price * Quantity or Revenue - Cost), and the tool evaluates it row by row to populate a new column. Because it operates per-row rather than as a spreadsheet formula engine, results are static values baked into the export, so there is nothing to recalculate or break when the file is opened elsewhere. Perfect for calculating a "total" column from separate price and quantity columns, deriving profit margin as (revenue - cost) / revenue for every row, and more. Read more about Calculated Columns in our introduction guide, or follow our tutorial for step-by-step instructions. Try it now →
When it comes to perform sql-style joins or vlookup between two files, Join Tables (VLOOKUP) is your go-to solution. Pick a key column in each file and a join type (inner, left, right, or outer), and the tool matches rows where the key values are byte-for-byte identical, exactly like a SQL JOIN or an Excel VLOOKUP/INDEX-MATCH. Because matching is exact, it is fast and deterministic even on large files, but it requires the key values to already be clean and consistently formatted on both sides. Perfect for looking up customer names into an orders file using a shared customer id, left-joining a product catalog onto a sales export by exact sku code, and more. Read more about Join Tables (VLOOKUP) in our introduction guide, or follow our tutorial for step-by-step instructions. Try it now →
For those who want to join tables even when spelling doesn't match perfectly, Fuzzy Match Join is your go-to solution. Instead of requiring identical key values, this tool scores string similarity (using edit-distance style matching) between key values across the two files and pairs up the closest matches above a threshold you set. That makes it possible to join records like "Jon Smith" with "John Smith," or "Acme Corp." with "Acme Corporation," where a normal exact join would find zero matches. Perfect for matching a hand-typed customer list against a crm export where names are spelled slightly differently, joining company names from a scraped dataset against an official registry with abbreviation differences, and more. Read more about Fuzzy Match Join in our introduction guide, or follow our tutorial for step-by-step instructions. Try it now →
Whether you need to smooth out volatile data using rolling windows, Moving Averages is your go-to solution. For each row, the tool computes a statistic (mean, sum, min, max, or standard deviation) over a sliding window of N preceding rows in a time-ordered numeric column, then writes that value into a new column. A 7-row window on daily sales, for example, produces a rolling weekly average that smooths out day-to-day noise while still tracking the underlying trend. Perfect for computing a 7-day moving average of daily website traffic to smooth out weekday/weekend spikes, calculating a 30-day rolling sum of revenue for a trailing-month kpi, and more. Read more about Moving Averages in our introduction guide, or follow our tutorial for step-by-step instructions. Try it now →
If you're looking to scrape all email addresses found in a text file or csv, Extract Emails is your go-to solution. The tool scans every cell of your file with a regular expression tuned to the email address format (local-part@domain), pulling out matches regardless of which column they appear in, including free-text fields like notes, comments, or scraped web content. Every match is captured into a clean output column, ready to deduplicate or import into a mailing list. Perfect for pulling every email address out of a "notes" or "comments" column exported from a crm, extracting contact emails from scraped web page text saved as csv, and more. Read more about Extract Emails in our introduction guide, or follow our tutorial for step-by-step instructions. Try it now →
When it comes to transform wide data (months as columns) into long format, Unpivot / Melt Table is your go-to solution. Also known as "Melting" or "Reverse Pivot". This tool is essential for preparing Excel reports for Tableau, PowerBI, or Database ingestion. Perfect for preparing monthly sales sheets for database import, cleaning survey data for visualization, and more. Read more about Unpivot / Melt Table in our introduction guide, or follow our tutorial for step-by-step instructions. Try it now →
For those who want to extract day, month, year, and weekday from dates, Date Decomposition is your go-to solution. The tool parses each value in your chosen date column and writes its components, day, month, year, and weekday name, into separate new columns, using the same date-parsing logic across the whole column so mixed formats are handled consistently. This turns a single opaque date string into discrete fields you can group, filter, or pivot by without writing any date-parsing formulas yourself. Perfect for splitting an order date column into year and month to build a pivot table by quarter, extracting weekday from a transaction log to analyze which days have the highest sales, and more. Read more about Date Decomposition in our introduction guide, or follow our tutorial for step-by-step instructions. Try it now →
Whether you need to count words, characters, and extract patterns, Text Feature Extractor is your go-to solution. For a chosen text column, the tool computes per-row metrics, word count, character count, and presence/count of specific patterns like digits, punctuation, or capital letters, writing each as its own new column. These numeric features are exactly the kind of engineered inputs used to score text length, complexity, or structure before feeding a column into a model or a filter. Perfect for generating a word-count column from product descriptions to flag entries that are too short for seo, counting digits in a free-text field to separate rows that contain a reference number from those that don't, and more. Read more about Text Feature Extractor in our introduction guide, or follow our tutorial for step-by-step instructions. Try it now →
If you're looking to use regular expressions to extract complex patterns, Regex Extractor is your go-to solution. You supply a standard regular expression with one or more capture groups, and the tool applies it to every row of a chosen column, writing each capture group into its own output column. This handles structured extraction that simple find-and-replace can't, such as pulling an order number, a date, and a status code out of a single freeform log line in one pass. Perfect for extracting an invoice number and date from a single free-text "description" field using one pattern, pulling a product sku embedded inside a longer url or file path string, and more. Read more about Regex Extractor in our introduction guide, or follow our tutorial for step-by-step instructions. Try it now →
When it comes to rename or group specific values (e.g, us -> usa), Category Mapper is your go-to solution. You define a lookup table of old-value-to-new-value pairs (for example "US" and "U.S." both mapping to "USA"), and the tool applies every mapping across the chosen column in a single pass, leaving unmapped values untouched. This is a manual, rule-based alternative to fuzzy matching, useful when you know exactly which variants exist and want deterministic control over the output labels. Perfect for standardizing country codes where a form allowed free text ("us", "u.s.a.", "united states") into one canonical "usa" value, grouping dozens of specific job titles into a handful of broader department categories, and more. Read more about Category Mapper in our introduction guide, or follow our tutorial for step-by-step instructions. Try it now →
For those who want to check if data meets specific logical rules, Column Validator is your go-to solution. You define one or more rules per column, numeric range, non-empty, regex pattern match, or membership in an allowed list, and the tool evaluates every row against them, flagging exactly which rule each failing row broke. Rather than silently rejecting bad data, it produces a per-row pass/fail report so you can decide how to handle exceptions before the file moves downstream. Perfect for verifying an "age" column only contains numbers between 0 and 120 before importing into a database, checking that every row in an "email" column matches a valid email pattern before a mail merge, and more. Read more about Column Validator in our introduction guide, or follow our tutorial for step-by-step instructions. Try it now →