Clean and Transform Data Using Plain English

Claude Code lets you clean and transform messy data by describing what you want in plain English — no formulas, no scripts, no uploading to third-party platforms. Just tell it to fix date formats, remove duplicates, fill blanks, or restructure columns, and it handles the rest on your local machine.

Unlike dedicated data-cleaning platforms such as Julius AI or Zoho DataPrep, Claude Code works with any file format already on your computer and handles multi-step transformations in a single conversation. Powered by Claude Opus 4.6, it understands the reasoning behind your cleaning rules — not just the mechanics — so you spend time on analysis instead of wrestling with spreadsheet formulas.

The Most Common Data Cleaning Tasks

Date Formatting

Mixed date formats are everywhere. Some rows have "01/15/2026", others have "2026-01-15", others have "January 15, 2026". Here's how to fix it:

I have a date column with mixed formats:
- MM/DD/YYYY
- YYYY-MM-DD
- "Month Day, Year"

Convert all to YYYY-MM-DD format.

Claude Code standardizes everything. No DATEVALUE formulas, no TEXT functions, no VBA. Just the result you need.

Name Parsing

Names are notoriously messy. "John Smith", "Smith, John", "John Q. Smith", "Dr. John Smith Jr." all in the same column:

Split this name column into:
- First name
- Last name
- Title (if present)
- Suffix (if present)

Handle formats like "Last, First" and "First Last"

This kind of parsing would take dozens of formulas to handle all edge cases. With plain English, it takes one request.

Missing Value Handling

Data comes with various representations of "nothing": blank cells, "N/A", "null", "-", "0", "None". Each might mean something different:

In this dataset:
- "N/A" means data was not available (leave blank)
- "0" is a real zero value (keep it)
- Blank cells should stay blank
- "-" was a data entry error (replace with blank)

Claude Code applies your specific business logic consistently across thousands of rows.

Data Transformation Made Simple

Reshaping Data

You have data in one shape, you need it in another. Wide to long, long to wide, pivoting, unpivoting—all describable in plain English:

Transform this data:
- Currently have columns: Product, Jan_Sales, Feb_Sales, Mar_Sales
- Need: Product, Month, Sales (one row per product-month)

This is called "melting" or "unpivoting"

Or the reverse:

Transform this data:
- Currently have: Product, Month, Sales (one row per product-month)
- Need: Product, Jan_Sales, Feb_Sales, Mar_Sales (one row per product)

Aggregation

Grouping and summarizing data:

From this transaction data, create a customer summary:
- Group by customer_id
- Calculate: total_orders, total_revenue, avg_order_value, first_order_date, last_order_date
- Include: customer name and email from the original data

Joining Data

Combining data from multiple sources:

I have two files:
1. orders.csv with: order_id, customer_id, amount, date
2. customers.csv with: customer_id, name, email, segment

Create a combined file with order data enriched with customer info.
Include all orders, even if customer data is missing.

Complex Transformations in One Request

The real power shows when you combine multiple operations:

Clean and transform this sales data:

1. Fix dates (currently mixed MM/DD/YY and YYYY-MM-DD)
2. Remove duplicate rows (same order_id)
3. Fill missing regions with "Unknown"
4. Convert revenue from string "$1,234.56" to number 1234.56
5. Add a column for quarter (Q1, Q2, Q3, Q4)
6. Add a column flagging orders over $10,000 as "Large"
7. Sort by date descending
8. Export to clean_sales.csv

What would take an hour of formula writing or a complex script takes one conversation turn.

Data Validation and Quality Checks

Cleaning is only half the battle. Verifying that your data is correct matters just as much:

Validate this dataset and report:
1. Any email addresses that look malformed
2. Phone numbers that don't match expected format (US: xxx-xxx-xxxx)
3. Zip codes that don't match the stated city/state
4. Revenue values that seem like outliers (more than 3x the median)
5. Dates that fall outside our business period (before 2020 or after today)

For each issue found, show the row number, the problem value, and a suggested fix.

Claude Code does not just clean your data—it can audit it. This kind of validation used to require custom scripts or expensive data quality tools. Now you describe what "good data" looks like and get a full report in seconds.

The Iteration Advantage

Here's what makes Claude Code different from other tools: you can iterate. First pass not quite right?

• "Actually, keep the dollar sign in the revenue column"
• "The quarter calculation is wrong for dates in late December"
• "Also flag negative values as 'Refund'"

Each refinement builds on the previous work. You're having a conversation, not starting over.

Building Reusable Cleaning Workflows

Once you've described a cleaning process, Claude Code remembers it. Next time:

Clean the weekly sales data using the same process as last time.

Your cleaning logic becomes a reusable workflow. No more recreating formulas every week. No more hoping you remember all the steps. The system knows.

When to Use Claude Code vs. Other Data Tools

If you are working with massive datasets (millions of rows) that require real-time pipelines, dedicated ETL tools like dbt or Fivetran are the right choice. If you need interactive dashboards built on top of your cleaned data, tools like Tableau or Power BI are purpose-built for that.

But for the everyday data work that fills most analysts' calendars—cleaning exports, reformatting spreadsheets, merging files, validating entries, preparing data for presentations—Claude Code is faster, more flexible, and requires zero learning curve. You just describe what you want.

Claude Code is available on Anthropic's Pro plan at $20/month. For analysts who deal with data cleaning daily, the Max plans ($100/month or $200/month) provide substantially more usage. Either way, the time savings pay for themselves within the first week.

Start Today

Pick your messiest dataset. The one you've been avoiding because cleaning it would take all afternoon. Describe what's wrong and what you want. You'll have clean data in minutes.