Data AnalystData CleaningBeginnerSingle prompt

Column Renaming Plan AI Prompt

Column Renaming Plan is a beginner 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 remain approachable and easy to review, even for someone with limited analytical background.

Prompt text
Review all column names in this dataset and produce a renaming plan:

1. Identify columns that violate snake_case convention (spaces, camelCase, PascalCase, hyphens, special characters)
2. Identify columns with unclear or ambiguous abbreviations (e.g. 'qty', 'amt', 'dt', 'flg')
3. Identify columns where the name doesn't match the apparent content based on sample values
4. Propose a clear, descriptive snake_case name for each column that needs renaming

Return a table: original_name | issue | proposed_name | reason
Also return a Python code snippet using df.rename() to apply all changes at once.

When to use this prompt

Use case 01

When the dataset contains missing values, inconsistent formatting, or suspicious records.

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 Column Renaming Plan 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 beginner, so it works well as a guided starting point for that level of experience.

What type of prompt is this?+

Column Renaming Plan 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 Automated Cleaning Code Generator, Data Quality Score Chain, Data Type Fixer.