Data Transform Tools

Choose a transform by the result you need

This category covers six different data-shaping tasks rather than one shared conversion workflow. Use Find and Replace in CSV to change selected CSV cell values, or Rename Columns to change header names while keeping the column order and cell values. For nested JSON, Flatten JSON turns nested structures into path-keyed or flat-table output.

For tabular data, choose Pivot Table Generator when you need grouped records and an aggregation, Transpose Table when you need to swap rows and columns, and Unpivot Table when wide columns should become attribute/value records.

Edit CSV values or column names

Start by deciding whether the change belongs inside cells or in the header row. Find and Replace in CSV searches selected CSV columns and supports literal, case-sensitive, whole-cell, and regular-expression matching. It is the focused choice for changing matching values.

Rename Columns uses an explicit old-to-new header mapping. Use it when the labels need to change but the cell values and column order should remain as they are. Neither tool is a general CSV-structure repair workflow.

Reshape tabular data without losing sight of the output

These three tools produce different shapes, so choose based on the result you need:

• Pivot Table Generator assigns row, column, and value fields, then groups records with count, sum, average, minimum, or maximum aggregation.
• Transpose Table swaps table rows and columns. Its visible options cover header treatment, row-label preservation, uneven rows, and blank-cell display; it does not aggregate records.
• Unpivot Table keeps identifier columns with each source row and turns selected wide columns into attribute/value records. Blank-value handling controls whether empty records are retained.

Flatten nested JSON into usable paths or a table

Flatten JSON is the JSON-specific choice. It accepts nested JSON and offers dot notation, bracket notation, or flat-table output. Its controls cover array handling, nulls, empty containers, separator escaping, and key-collision reporting. This restructures JSON nesting; it is not a CSV value edit, table pivot, or row-transposition workflow.

A practical selection and review workflow

1. Identify whether your input is CSV, another tabular dataset, or nested JSON.
2. For CSV, decide whether you are changing cell values or only column headers.
3. For tabular data, decide whether the output should aggregate groups, swap axes, or create one attribute/value record per selected wide column.
4. Open the matching tool, review its visible options and preview, then export the result using the output controls provided there. Parsing, output formats, and limits differ across these tools.