Question 1

How do you convert nested JSON arrays to CSV?

Accepted Answer

Converting nested arrays requires choosing a specific flattening strategy because flat CSV rows cannot natively hold lists. The most common approach is the Relational Method, which duplicates the parent data for every item in the array, creating multiple rows (e.g., one order with three items becomes three rows). Alternatively, you can use the Stringification Method, which converts the array back into a raw text string and places it inside a single CSV cell. The choice depends entirely on whether you need to perform mathematical calculations on the array data or simply store it.

Question 2

Why is my CSV file missing columns from some JSON objects?

Accepted Answer

This almost always occurs when using a basic streaming converter on a dataset with a highly variable schema. If the converter only checks the very first JSON object to generate the CSV header row, it will permanently ignore any new keys that appear in subsequent objects. To fix this, you must use a converter that performs a preliminary full-file scan to extract a master list of all possible keys before it begins writing the CSV data.

Question 3

Can I convert a CSV file back to the exact same JSON file later?

Accepted Answer

In most cases, no. The conversion from JSON to CSV is typically a "lossy" process regarding structure. When you flatten a deeply nested JSON hierarchy into a flat CSV, the explicit boundaries of the original objects and arrays are destroyed. While you can convert the CSV back into a flat JSON array of objects, reconstructing the exact multi-level nesting of the original file is impossible without writing a custom script that specifically knows how to re-nest the flattened dot-notation keys.

Question 4

What is the maximum file size for a JSON to CSV conversion?

Accepted Answer

There is no theoretical maximum file size if you utilize a streaming converter and NDJSON (Newline Delimited JSON). A streaming converter processes the file one line at a time, meaning it only requires a few megabytes of RAM regardless of whether the file is 10 Megabytes or 10 Terabytes. However, if you are using an in-memory converter, the maximum file size is strictly dictated by your computer's available RAM. Attempting to load a file larger than your RAM will result in a system crash.

Question 5

How do I handle commas and newlines inside my JSON data values?

Accepted Answer

You do not need to alter your JSON data; the responsibility lies with the converter to apply standard RFC 4180 escaping rules. When the converter encounters a string value containing a comma or a newline (like a paragraph of text), it must wrap that entire value in double-quotes before writing it to the CSV. Spreadsheet programs like Excel recognize these quotes and will correctly display the comma or newline inside a single cell without breaking the tabular structure of the file.

Question 6

Why are large ID numbers in my JSON changing to scientific notation in the CSV?

Accepted Answer

The converter is likely writing the ID correctly, but the spreadsheet application (like Excel) is corrupting it upon opening. Excel automatically attempts to format long strings of digits as numbers, and it has a strict 15-digit precision limit. If your JSON contains a 19-digit Twitter ID, Excel will truncate the last four digits. To prevent this, configure your converter to force quote-wrapping on large numbers, or explicitly import the CSV into Excel using the Data Import wizard and set the column type to "Text" rather than "General".

JSON to CSV Converter

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