Reshape a wide CSV into tidy records without losing meaning

Produce a deterministic transformation from repeated measure columns to tidy rows with explicit keys, types and missing-value rules.

PLPromptByte Library@promptbytePublished Sep 8

CodeData & Analysis #csv #data transformation #pandas #tidy data

Version 1 of 1 · published Sep 8, 2026Version 1 · only version

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Version 1 · 3 variables · 3 required

Fill in the values below — the customized prompt updates as you type. marks a required value.

Wide data{{wide_data}}

Source columns and representative rows. Default: Illustrative CSV: store_id,sales_jan,sales_feb 001,120,130 002,90,

Tidy rules{{tidy_rules}}

Target grain, field mapping and missingness rules. Default: Output columns store_id:string, month:string, sales:number|null. One row for every store-month, including missing sales. Strip the sales_ prefix from month names.

Transform stack{{transform_stack}}

Installed tools and file names. Default: Python 3.12 with pandas; read input.csv and write tidy.csv.

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Resolved user prompt15 lines

Transform this wide dataset into the requested tidy structure.

Input schema and sample: Illustrative CSV:
store_id,sales_jan,sales_feb
001,120,130
002,90,

Target schema and transformation rules: Output columns store_id:string, month:string, sales:number|null. One row for every store-month, including missing sales. Strip the sales_ prefix from month names.
Implementation environment: Python 3.12 with pandas; read input.csv and write tidy.csv.

Explain the identifier columns, columns to unpivot, parsed variable names and resulting row grain. Flag any naming ambiguity before choosing a mapping.

Provide a complete transformation script for the stated environment. Preserve identifiers as strings when needed, handle missing values exactly as specified, and avoid silently coercing invalid cells or dropping rows. Keep the original input unchanged.

Include validation checks for key preservation, expected row counts, type conversion failures and a round-trip comparison where meaningful. Label any sample output as illustrative until executed. Do not claim the script was run or that a sample establishes whole-file quality.

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code · sql
```
**Assumptions**

- `order_items.order_id` joins to `orders.id`, and `products.category_id` joins to `categories.id`. These are inferred from the naming convention, since no foreign keys were listed.
- "Last 90 days" is measured against `orders.order_date` relative to today, inclusive of today.
- Revenue is `quantity * unit_price` summed over line items; there is no discount or tax column in the schema, so none is applied.
- Cancelled orders are excluded. If `status` uses different values than `'
```
Code preview: **Assumptions** - `order_items.order_id` joins to `orders.id`, and `products.category_id` joins to `categories.id`. These are inferred from the naming convention, since no foreign keys were listed. - "Last 90 days" is measured against `orders.order_date` relative to today, inclusive of today. - Revenue is `quantity * unit_price` summed over line items; there is no discount or tax column in the schema, so none is applied. - Cancelled orders are excluded. If `status` uses different values than `'
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