Mailchimp Audience Export Sample Dataset
This dataset contains 12,000 synthetic Mailchimp subscriber records with exact production export columns: Email Address, First Name, Last Name, Date Added, OPTIN TIME, CONFIRM TIME, and Member Rating. It represents a mixed-source audience combining native double opt-in subscribers with API-imported contacts from third-party lead generation tools. The Mailchimp-specific quirk is that OPTIN TIME is frequently blank for imported contacts because they never went through Mailchimp's native double opt-in confirmation flow — they were added directly via API or CSV import. This causes pandas to_datetime() and SQL date-parsing functions to throw NaT (Not a Time) or NULL errors when calculating subscriber age or engagement cohorts. The dirty data inventory includes: 4,800 rows with completely blank OPTIN TIME values, inconsistent date formats mixing MM/DD/YYYY and YYYY-MM-DD across the Date Added column, and trailing spaces in 1,100 Email Address cells that break engagement rate calculations. After standardizing date formats and handling null opt-in timestamps, this yields 12,000 clean subscriber records with accurate engagement metadata. Ideal for: ESP migration dry runs, deliverability analysis, cohort segmentation testing, and email marketing analytics. Feed this file into the format-cleaner tool to normalize the mixed date formats.
This dummy dataset simulates a standard export from Other, containing realistic yet fully anonymized records that mirror the structure and common data quality issues found in real production environments. The CSV file includes typical problems such as inconsistent formatting, missing values, duplicate entries, and non-standardized categorical fields.
It is designed to be used as a safe sandbox for testing data cleaning workflows directly in your browser — no uploads, no server round-trips, no third-party data exposure. Whether you are validating a transformation pipeline, benchmarking a cleaning tool, or simply exploring common data quality patterns, this sample provides a representative starting point without risking any sensitive business data.
Data Schema
| Column Name | Data Type | Description |
|---|---|---|
| id | string | Unique record identifier |
| created_at | timestamp | Record creation date and time |
| status | string | Current status of the record |
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