Question 1

What JSON structures are supported?

Accepted Answer

The tool supports JSON arrays of objects (recommended) and single JSON objects. For JSON → CSV, each object becomes a row and object keys become columns; for CSV → JSON, each row becomes an object in a JSON array.

Question 2

How are nested objects handled during conversion?

Accepted Answer

Nested JSON can be flattened into a single level using a dotted path style (for example, "address.city"). When converting CSV back to JSON, those flattened column names can be kept as plain keys, or interpreted as paths if your workflow expects nested reconstruction.

Question 3

How does the tool manage missing fields across rows?

Accepted Answer

When converting to CSV, the tool builds a unified set of columns and fills missing values with a consistent placeholder (commonly an empty cell) so every row has the same shape. When converting to JSON, missing or empty cells can be represented consistently (for example, as empty strings or null) based on your settings.

Question 4

Can I control the column order in the CSV output?

Accepted Answer

Yes. You can select which columns to include and define their order, so the CSV output matches your spreadsheet, database import, or API schema requirements.

Question 5

How does type inference work when converting CSV to JSON?

Accepted Answer

The tool can infer common types from CSV cell values, such as numbers and booleans, while leaving non-matching values as strings. If you need strict control, you can keep everything as strings or adjust settings so values like leading-zero IDs remain unchanged.

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