XML Converterv1.0.0

Transform XML into JSON, CSV, XLSX, or HTML table format, or convert JSON and CSV back into well-formed XML. The converter parses nested elements, attributes, and text content, mapping them into the target structure while preserving hierarchy where the output format supports it.

• Select the Input Format that matches your source data: XML, JSON, or CSV.
• Paste or type the source data into the Source Data textarea, or click Or Upload a File to load a local file. Accepted file types include .xml, .json, .csv, .xlsx, .html, and .txt.
• Choose the desired Output Format from the dropdown: JSON, CSV, XLSX, HTML Table, or XML.
• Click Convert to process the data. The converted result appears in the output area below the controls.
• Click Copy Output to copy the result to your clipboard, or click Download to save the converted data as a file.
• Open the Settings panel to adjust conversion behavior. Indentation Spaces controls the whitespace depth in JSON and XML output (default is 2). CSV Delimiter sets the column separator for CSV output or input (comma, tab, or semicolon). XML Root Element Name specifies the wrapper element when generating XML from flat data (default is "root"). Array Handling determines whether nested arrays remain hierarchical or flatten into individual rows for CSV and tabular output.
• Click Reset to clear all inputs, outputs, and saved settings, returning every field to its default value.
• When converting XML to JSON, each element becomes a key in the resulting object. Attributes appear under a special @attributes key, and text content maps to a #text key. Repeated sibling elements of the same name merge into arrays automatically.
• When converting JSON or CSV to XML, each top-level key or column header becomes an element name. Nested objects produce child elements, and array entries repeat the parent element name. Values that cannot serve as valid XML element names are sanitized by replacing invalid characters with underscores.
• XLSX output generates a downloadable spreadsheet with column headers derived from the data keys. Nested structures flatten into dot-notation headers (for example, "address.city") so every value occupies a single cell.

Data format mismatches arise frequently in development, analysis, and integration workflows. Converting between XML and other common formats bridges gaps between systems that expect different input shapes.

• API Integration: Receive an XML SOAP response and convert it to JSON for consumption by a REST-based front end. Paste the XML payload, select JSON output, and obtain a structured object ready for parsing in JavaScript or Python.
• Spreadsheet Reporting: Export XML transaction records from an accounting system and convert them to XLSX for pivot table analysis in a spreadsheet application. Flat array handling produces one row per transaction with columns for each data field.
• Database Migration: Extract XML configuration files from a legacy system and convert them to CSV for bulk import into a relational database. Adjust the CSV delimiter to match the target system's import requirements.
• Web Content Generation: Convert a product catalog stored in XML into an HTML table for embedding in a web page. Each element maps to a table row, with attributes and child elements filling the columns.
• Configuration Auditing: Convert JSON configuration snapshots back to XML for comparison with canonical XML schema definitions. Preserve nesting depth and attribute placement so structural differences surface clearly in a diff tool.
• Data Journalism: Download government XML data feeds containing census or budget figures and convert them to CSV for analysis in statistical software. Flatten nested categories into individual rows with dot-notation column headers to maintain context.
• E-commerce Feed Conversion: Transform XML product feeds from suppliers into JSON for ingestion by a storefront API. Attribute data such as SKU, price, and availability maps to top-level keys, while nested description elements become string values.
• Log Processing: Parse XML-formatted application logs and convert them to CSV for import into a log aggregation dashboard. Each log entry becomes a row with timestamp, severity, and message columns.