For analysts preparing a spreadsheet or table for reporting, analysis, or modeling, get checks for missing values, duplicate records, inconsistent labels, and formatting problems. Describe your columns and sample rows to get Python, R, SQL, or pseudocode for your cleaning rules. You leave with a quality checklist, code with its assumptions, and a plan for unresolved data decisions.
Data Cleaning Tools
Plan data-cleaning checks and code for missing values, duplicates, and inconsistent records.
Use Data Cleaning Tools
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What it writes
- Data-quality checks for your columns
- Cleaning code with assumptions noted
- Preprocessing steps for your analysis
How to use it
In your first message, include column names, a few anonymized rows showing each problem, your intended output, and your preferred tool. State how to handle ambiguous values so the checks or code reflect your rules.
What you bring
- Column names and anonymized sample rows
- Cleaning rules and analysis goal
- Preferred language or data tool
- Remove personal or sensitive data from examples
A good fit for
- Checking duplicate customer IDs and missing dates before a monthly report
- Standardizing inconsistent product labels before sales analysis
- Planning preprocessing steps for a model built from survey data
Ways to start
A few requests this Agent can handle.
My sales table has order_id, date, region, and amount. Some dates use MM/DD/YYYY, and region names vary between “NE” and “Northeast.” What checks should I run?
Write Python code for a table with customer_id, email, and signup_date. Flag duplicate IDs, blank emails, and dates later than today; keep the original rows unchanged.
Review this rule for survey data: replace “N/A” and blank responses with missing values, but keep “Prefer not to say” as a response. What edge cases should I check?
Good to know
This workspace provides text assistance.
- Cleaning or validating dataset files in chat
- Choosing correct values without domain rules
- Cannot open, inspect, or clean your dataset file
- Code suggestions need testing against your actual data
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Can I use the response in my work?
Use it as a draft to review and adapt. Verify calculations, facts, rights, and suitability before relying on it. Do not provide information you do not have permission to share.
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