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Use cases

Clean once and repeat forever. Browse practical examples and the building blocks you can combine to build reusable recipes.

Real examplesOperations

Real examples

Payments & Payout Reconciliation

The messy reality

Your Stripe export looks like chaos after manual edits with your reports — multiple date formats (12/10/24, 2024-10-12), currencies mixed with symbols ($1,250), and duplicate rows for refunds or test transactions.

Fix it with this recipe

  • Drop columns → Remove fields like “Notes,” “Fee breakup,” or unused metadata.
  • Standardize date → Convert every format to a clean YYYY-MM-DD.
  • Currency to number → Strip symbols/commas to pure numeric values.
  • Remove duplicates → Keep the latest entry per txn_id or email.
  • Filter rows → Exclude test or failed payments (status != success).
  • Map values → Normalize variants like “succ,” “Success,” “SUCCESS.”

Result

Clean numeric data ready for reconciliation or pivot analysis — no manual formula wrangling, no broken totals.

Keywords

clean payment CSV, standardize date format, remove duplicates, convert currency to number.

CRM & Lead Import Prep

The messy reality

Your lead data comes from multiple forms, and every export has slightly different headers (Full Name, Name, user_name). Phone numbers are random lengths and emails have stray spaces.

Fix it with this recipe

  • Split full name → Break Full Name into First and Last.
  • Trim spaces → Fix trailing and leading spaces before import.
  • Normalize phone → Convert to consistent 10-digit or E.164 format.
  • Map values → Standardize United States / US / United States of America → USA.”
  • Rename columns → Match CRM schema: “Email ID” → “Email.”
  • Remove duplicates → Deduplicate by email or phone.

Result

Instantly import-ready CSV that matches your CRM column names and validation rules.

Keywords

clean Excel before import, dedupe by email, standardize phone numbers, rename columns in CSV.

Survey & Feedback Cleanup

The messy reality

You exported survey data from Google Forms — dozens of empty columns, inconsistent yes/no entries (“Y”, “Yes”, “1”), and free-text fields broken across lines.

Fix it with this recipe

  • Drop empty columns → Instantly remove blank form fields.
  • Clean line breaks → Flatten multiline answers for CSV safety.
  • Map values → “Y / Yes / 1” → “Yes.”
  • Keep only letters → Clean out rogue symbols from text answers.
  • Clip text → Truncate overly long free-text comments.

Result

Readable, standardized responses ready for visualization or sentiment analysis.

Keywords

clean survey data, normalize values in Excel, remove empty columns, fix line breaks in CSV.

E‑Commerce Orders & Inventory Reports

The messy reality

Your order export from Shopify or WooCommerce has 30+ columns, inconsistent SKUs (000123 vs 123), and totals with commas that break formulas.

Fix it with this recipe

  • Drop columns → Keep only SKU, Product, Quantity, Date, Amount.
  • Standardize date → DD-MM-YYYY → YYYY-MM-DD.
  • Currency to number → Remove commas and currency symbols.
  • Pad left → Make SKUs uniform (000123).
  • Conditional set → Add a “High-value” flag where amount > 5000.
  • Reorder columns → Bring “SKU,” “Date,” “Amount” to the front.

Result

Consistent, analysis-ready order sheets you can feed into dashboards without cleanup.

Keywords

clean ecommerce data, pad SKU numbers, convert currency, reorder columns in CSV.

Support & Ticket Exports

The messy reality

Your ticketing system export has quoted IDs ("1234"), weird characters in subject lines, and a mix of open/closed tickets.

Fix it with this recipe

  • Strip wrapping quotes → Convert "1234" → 1234.
  • Clean special characters → Remove emojis/symbols from text.
  • Filter rows → Keep only status = open.
  • Extract numbers → Pull ticket numbers from strings like #TKT-4502.
  • Merge columns → Combine “Agent Name” + “Status” → “Agent — Status.”

Result

Neat ticket data ready for team reviews, no need for manual text formulas.

Keywords

clean support ticket export, extract numbers from text, remove special characters Excel, merge columns CSV.

HR, Payroll & Attendance Sheets

The messy reality

You receive attendance data from multiple sources. Some rows are blank, dates are inconsistent, and you must share the sheet while hiding personal info.

Fix it with this recipe

  • Drop empty rows → Instantly remove gaps.
  • Standardize date → Fix mixed date formats.
  • Mask email → Hide personal IDs before sharing.
  • Normalize phone → Ensure all numbers have country codes.
  • Remove duplicates → Avoid duplicate entries per employee ID.

Result

Privacy-safe, clean HR data suitable for sharing or audits.

Keywords

clean HR data, mask email in Excel, standardize attendance sheet, remove empty rows.

Logistics, Shipping & Tracking Exports

The messy reality

Tracking data comes in from vendors with random column names (awb_no, AWB, tracking_id) and inconsistent text fields.

Fix it with this recipe

  • Rename columns → Standardize headers across vendors.
  • Trim spaces / Collapse spaces → Clean padded tracking IDs.
  • Extract numbers → Parse IDs from mixed strings.
  • Map values → Normalize carrier names (Fedex → FedEx).
  • Reorder columns → Keep Date, AWB, Status, Delivered On up front.

Result

Unified tracking sheet for dashboards or reconciliations.

Keywords

clean logistics data, rename columns, extract tracking ID, normalize values Excel.

Research & Experiment Logs

The messy reality

Your experiment logs have inconsistent date/time, multiple columns with missing data, and researcher notes full of special characters.

Fix it with this recipe

  • Fill empty → Replace blanks with “N/A.”
  • Standardize date → Harmonize timestamp formats.
  • Clean special characters → Remove unicode junk.
  • Conditional set → Flag “Outliers” when value > threshold.
  • Merge columns → Combine related fields for compact export.

Result

Structured datasets that scientists and analysts can reuse without re-cleaning.

Keywords

clean research data, fill empty cells Excel, conditional data cleaning, merge columns CSV.

Marketing Lists & Event Signups

The messy reality

Exports from Google Forms or Eventbrite include inconsistent name casing, broken emails, and duplicate contacts.

Fix it with this recipe

  • Trim spaces → Remove hidden spaces in emails.
  • Split full name → Separate first/last names.
  • Clean special characters → Fix corrupted text.
  • Map values → Standardize city or source field.
  • Remove duplicates → Deduplicate by email.

Result

Campaign-ready CSV with clean, unique contact data.

Keywords

clean email list, remove duplicates by email, format names Excel, prepare event signup CSV.

Finance & Expense Sheets

The messy reality

Excel exports from accounting tools have commas, currency symbols, and inconsistent columns.

Fix it with this recipe

  • Currency to number → Strip $/€/£ for numeric totals.
  • Clean line breaks → Fix memo or notes columns.
  • Clip text → Limit comment length.
  • Drop columns → Remove irrelevant data.
  • Reorder columns → Keep Account, Date, Amount up front.

Result

Polished, number-ready sheets for pivot tables or upload into BI tools.

Keywords

clean accounting data, convert currency Excel, remove commas CSV, prepare finance sheet.

Operations

Every clean file starts with a few simple steps. Here’s how each SheetChef operation helps you turn raw exports into structured, reusable data.

Prefer to try these live? Open the Builder and use the sample file.

Column Operations

Drop Columns

Remove noise fields you never use — like “Timestamp,” “Form response ID,” or “Raw JSON.”

Example: In a payments report, keep only txn_id, amount, and date while dropping notes, entity, and contact_id.

Result: Fewer distractions, faster processing, cleaner reports.

Keywords: drop columns in CSV, remove unwanted columns Excel.

Rename Columns

Give messy headers a consistent identity.

Example: Rename user_name → Name, created_at → Date, txn_amt → Amount.

Result: Every export aligns with your master recipe, no matter the source.

Keywords: rename columns CSV, standardize headers Excel.

Reorder Columns

Drag-and-drop your columns to the order you want.

Example: Bring “Date,” “Amount,” “Status” to the front for reporting sheets.

Result: Easier to scan and compare runs, especially when sharing exports.

Keywords: reorder CSV columns, arrange Excel columns.

Split Full Name

Automatically break “John Doe” → First: John | Last: Doe.

Example: Use when importing leads or event signups that merge names into one field.

Result: Clean CRM fields, better personalization.

Keywords: split full name Excel, separate first last name CSV.

Merge Columns

Combine multiple fields into one formatted string.

Example: Merge City, State → City, State or First + Last → Full Name.

Result: Compact, human-readable data for summaries or exports.

Keywords: merge columns CSV, combine text fields Excel.

Row Operations

Filter Rows

Show only the rows that matter.

Example: Keep only status = success, or amount > 100.

Result: Slimmer, targeted sheets you can trust.

Keywords: filter rows Excel, filter CSV by value.

Remove Duplicates

Eliminate duplicate entries by key (email, ID, or phone).

Example: Deduplicate contact lists where [email protected] appears twice.

Result: Reliable counts, accurate reports, cleaner imports.

Keywords: remove duplicates CSV, dedupe Excel.

Split to Rows

Turn multi-line or list-like text in a single cell into multiple rows while duplicating all other columns.

Example: Issue Tracking cell like "1. Fix backlog\n2. Move to new state\n3. Hire analyst" → three separate rows, one per task.

Result: Each item gets its own row, so you can filter, extract dates/keywords, and dedupe reliably.

Keywords: split list into rows, expand bullets into rows, normalize multi-line cells.

Drop Empty Rows / Drop Empty Columns

Get rid of dead space automatically.

Example: Delete blank rows from survey responses or unused form fields.

Result: Compact sheets that open faster and analyze cleaner.

Keywords: remove empty rows, delete blank columns Excel.

Standardization Tools

Standardize Date

Fix all date formats to one clean style (YYYY-MM-DD).

Example: “10/12/23,” “12-Oct-2023,” and “2023/10/12” → all become “2023-10-12.”

Result: No more formula errors, perfect sorting and grouping.

Keywords: standardize date format CSV, fix Excel date inconsistencies.

Currency → Number

Convert strings like “$1,250.50” into pure numbers.

Example: “$3,500” → 3500.

Result: Totals and averages work again without manual cleanup.

Keywords: convert currency to number, clean currency Excel.

Normalize Phone

Make phone numbers uniform (remove spaces, dashes, country codes).

Example: +1-202-555-0182, 12025550182, 2025550182 → all become +12025550182.

Result: Consistent contacts, ready for deduplication or imports.

Keywords: normalize phone numbers CSV, clean phone data Excel.

Text Cleanup

Trim Spaces

Remove leading/trailing spaces that break matching.

Example: " [email protected] " → [email protected].

Result: Fewer “false duplicates,” faster imports.

Keywords: trim spaces Excel, remove extra spaces CSV.

Collapse Spaces

Turn multiple spaces into one.

Example: "John Doe" → "John Doe".

Result: Visually clean and consistent text.

Keywords: collapse spaces, clean spacing Excel.

Strip Wrapping Quotes

Remove extra quotes caused by exports.

Example: “"12345"” → 12345.

Result: Clean numeric fields that stop breaking formulas.

Keywords: remove quotes CSV, clean quoted cells Excel.

Clean Line Breaks

Flatten multi-line text into a single line.

Example: “Address Line 1\nAddress Line 2” → “Address Line 1, Address Line 2.”

Result: Safe CSV exports, no broken rows.

Keywords: remove line breaks Excel, clean multi-line CSV.

Clean Special Characters

Strip unwanted symbols or keep only allowed sets (letters, digits, punctuation).

Example: Remove emojis, HTML tags, or copy-pasted bullets from text fields.

Result: Clean, system-safe text that won’t crash imports.

Keywords: remove special characters Excel, clean symbols CSV.

Extract Numbers

Pull digits out of mixed strings.

Example: “Order #4501-A” → 4501.

Result: Numeric IDs you can filter or join on.

Keywords: extract numbers from text, get digits Excel.

Extract from Text

Pull structured data from free‑text (emails, URLs, domains, phone numbers, dates, and UTM).

Example: Find email, first date, and URL in “notes” and write them to email_extracted, date_extracted, and url_extracted. Extract domains with apex-only, and parse phone numbers to E.164.

Result: Turn blobs into columns your other steps love — standardize, filter, dedupe.

Keywords: extract emails from text, extract URL Excel, parse phone numbers, extract dates, extract UTM params.

Extract Keywords

Pull out status words or tags from text columns using a provided list or auto-keyword detection.

Example: From “1. Fix backlog — COMPLETED on 02/04/05” extract statuses like COMPLETED, ONGOING, BLOCKED into a separate column.

Result: Reusable status/tag columns that make filtering, grouping, and dashboards much easier.

Keywords: extract keywords from text, pull status labels, auto keywords CSV.

Keep Only Letters

Remove numbers and symbols.

Example: “Room 12B” → “Room B.”

Result: Neat textual fields like names and categories.

Keywords: keep letters only Excel, remove numbers CSV.

Smart Replace

Replace multiple patterns at once (supports simple and regex).

Example: Replace “N/A”, “NA”, and “--” → “Missing.”

Result: Unified, readable fields.

Keywords: bulk replace CSV, regex replace Excel.

Data Quality & Logic Helpers

Fill Empty

Fill missing values with a constant, today’s date, or a default label.

Example: Empty “Status” → “Pending.”

Result: No blanks in summaries or pivots.

Keywords: fill empty cells Excel, replace blank CSV.

Map Values

Turn inconsistent labels into one standard.

Example: “Y,” “Yes,” “1” → “Yes.”

Result: Uniform categories ready for analysis.

Keywords: map values CSV, normalize text Excel.

Mark Matches

Flag rows when a column’s value appears in a list you provide.

Example: If Phone is in your “VIP numbers” list → set Matched = "Yes".

Result: Instant yes/no flags for “exists in this list?” checks — great for VIP lists, processed rows, or suppression lists.

Keywords: mark matches, lookup list, flag rows, yes no column, exists in list.

Conditional Set

Set a column’s value based on conditions in others.

Example: If Amount > 1000 → set Tier = Gold.

Result: Enriched data that classifies itself.

Keywords: conditional update Excel, rule-based column set.

Pad Left

Add leading zeros to reach a fixed length.

Example: 123 → 000123 (6 digits).

Result: Clean SKUs, ID fields, or postal codes.

Keywords: pad zeros Excel, left pad CSV.

Clip Text

Trim long text fields by length or keep only the part before/after a delimiter.

Example: “Working with the industry to fix the backlog - COMPLETED on 02/04/05” → keep only the text before the first “-”.

Result: Cleaner descriptions without manual formulas — perfect for notes and issue tracking columns.

Keywords: truncate text Excel, limit text length CSV, clip by delimiter, keep text before dash.

Mask Email

Protect privacy by partially hiding sensitive info.

Example: [email protected] → jo***@gmail.com.

Result: Safe to share sheets publicly or with vendors.

Keywords: mask email Excel, anonymize CSV data.

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