Practical guides on working with data formats — JSON, CSV, Excel, YAML, and XML. Written for developers, analysts, and anyone who works with data.
Convert JSON to CSV in Python using the standard library, pandas, or an online tool. Covers flat JSON, nested objects, arrays, and real API data — with working code for each approach.
Read articleCopy-pasting JSON into Excel doesn't work. This guide covers the correct ways to convert JSON to Excel—including how to handle nested objects and arrays.
Read articleConverting nested JSON to CSV requires a flattening strategy. This guide explains how to handle nested objects, arrays of objects, and mixed structures—with code examples.
Read articleCSV file not opening correctly? This guide covers the 7 most common CSV problems—encoding issues, wrong delimiters, unquoted commas, and more—with step-by-step fixes.
Read articleLearn how to flatten deeply nested JSON into flat key-value pairs. Includes Python and JavaScript examples, plus when to use dot notation vs bracket notation for arrays.
Read articleA practical comparison of JSON and CSV. Learn exactly when to use each format, with real examples from APIs, databases, and analytics pipelines.
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