Use case 01
When the dataset contains missing values, inconsistent formatting, or suspicious records.
Data AnalystData CleaningAdvancedSingle prompt
Automated Cleaning Code Generator AI Prompt
Automated Cleaning Code Generator is a advanced prompt for data cleaning. This prompt focuses on identifying and resolving data quality problems that can distort analysis or break downstream workflows. It guides the AI to inspect the dataset systematically, explain the issues clearly, and recommend or apply practical fixes. It is useful when the data is messy, inconsistent, or not yet ready for reliable reporting or modeling. It is best suited for direct execution against a real dataset. The requested output should be comprehensive, methodical, and suitable for expert review or production-style work.
Prompt text
Analyze this dataset and generate a complete, production-ready Python cleaning script. The script must: 1. Load the data 2. Fix all data type issues (with explicit dtype mapping) 3. Handle all missing values with the appropriate strategy per column 4. Remove or flag duplicate rows 5. Apply all string standardizations needed 6. Fix date columns to ISO 8601 format 7. Clip or remove outliers where appropriate 8. Assert data quality at the end: row count within expected range, no nulls in key columns, all dates in valid range Code style requirements: - Use pandas - Add a comment above every transformation explaining why it is needed - Include a final print() summary: rows before, rows after, columns changed - Make the script idempotent — safe to run multiple times
When to use this prompt
Use case 02
When analysis results look unreliable and you need to validate the raw data first.
Use case 03
When you are preparing data for dashboards, machine learning, or SQL pipelines.
Use case 04
When you need a documented cleaning plan rather than ad hoc fixes.
What the AI should return
The AI should return a practical cleaning assessment with issue-by-issue recommendations or actions, ideally in tables and clearly labeled sections. It should explain what was found, why it matters, and what fix is recommended or applied for each column or record type. When code is requested, the code should be runnable and aligned with the decisions described in the narrative. The final output should make the cleaning process auditable and easy to implement.
How to use this prompt
1
Open your data context
Load your dataset, notebook, or working environment so the AI can operate on the actual project context.
2
Copy the prompt text
Use the copy button above and paste the prompt into the AI assistant or prompt input area.
3
Review the output critically
Check whether the result matches your data, assumptions, and desired format before moving on.
4
Chain into the next prompt
Once you have the first result, continue deeper with related prompts in Data Cleaning.
Frequently asked questions
What does the Automated Cleaning Code Generator prompt do?+
It gives you a structured data cleaning starting point for data analyst work and helps you move faster without starting from a blank page.
Who is this prompt for?+
It is designed for data analyst workflows and marked as advanced, so it works well as a guided starting point for that level of experience.
What type of prompt is this?+
Automated Cleaning Code Generator is a single prompt. You can copy it as-is, adapt it, or use it as one step inside a larger workflow.
Can I use this outside MLJAR Studio?+
Yes. The prompt text works in other AI tools too, but MLJAR Studio is the best fit when you want local execution, visible Python code, and reusable notebooks.
What should I open next?+
Natural next steps from here are Column Renaming Plan, Data Quality Score Chain, Data Type Fixer.