How to Open & Browse a JSON File: Step-by-Step Tutorial

By FinancialDataTools.com Team  ·  March 2026  ·  8 min read  ·  Last updated March 14, 2026

This tutorial walks you through opening and exploring a JSON or JSON Lines file using the free FinancialDataTools.com JSON Viewer. The tool parses your file using native JavaScript entirely inside your browser — nothing is sent to any server.

Step 1: Locate Your JSON File

Find the .json, .jsonl, or .ndjson file you want to inspect. JSON files are used widely in financial data workflows:

• API responses from financial data providers (Alpha Vantage, Polygon.io, FRED, IEX Cloud)
• Transaction records and trade logs exported from trading platforms
• Portfolio and position snapshots from brokerage APIs
• Financial model outputs and scenario analysis results
• Event logs and audit trails stored as JSON Lines (.jsonl)

The viewer supports three JSON structures:

• Standard JSON — a file containing an array of objects: [{"id":1}, {"id":2}, ...]
• JSON object with array property — a file like {"data": [{"id":1}, ...]} — the viewer uses the first array-valued property as rows
• JSON Lines / NDJSON — one JSON object per line (common in log exports and streaming APIs)

Step 2: Open the JSON Viewer

Navigate to financialdatatools.com/viewers/json-viewer/ in any modern desktop browser. No login, account, or installation is required.

Step 3: Load Your File

There are two ways to open your file:

• Click the green "Open File" button in the toolbar and select your file using the system file picker.
• Drag and drop your file anywhere onto the viewer window.

The viewer parses the file using JSON.parse() (for standard JSON) or a line-by-line parser (for JSON Lines). For most files this is nearly instantaneous.

The format badge in the stats bar shows JSON or JSONL to confirm the detected format. The total row count, visible row count, and column count also appear in the stats bar.

Step 4: Understand the Table Layout

The viewer converts your JSON data to a spreadsheet-style table:

• Each JSON object (or JSON Lines entry) becomes one row
• The union of all keys across all objects becomes the set of columns — so if some objects have extra keys that others lack, those columns still appear with null for the rows missing that key
• Nested objects and arrays within a row are shown as their JSON string representation in the cell

Column headers show the key name and a type badge:

• NUM (blue) — column is predominantly numeric; values right-aligned
• BOOL (purple) — column is predominantly boolean
• STR (green) — general string column
• OBJ (amber) — column contains nested JSON objects or arrays

Step 5: Sort Columns

Click any column header to sort rows by that column:

• First click: ascending (A → Z, smallest → largest)
• Second click: descending
• Third click: original file order

Sorting applies to the entire dataset, not just the current page, so a sort on a paginated file correctly shows the globally smallest or largest values first.

Step 6: Search and Filter Rows

Global search — type in the search box in the toolbar to search across all columns simultaneously. Any row where no column contains the search term is hidden. Results update as you type.

Column filters — click the filter icon (funnel) in any column header. Two modes are available:

• Values mode: A checklist of all distinct values in that column. Uncheck values to hide rows with those values. Use the search box inside the panel to find specific values in long lists.
• Conditions mode: Apply a custom condition such as "contains", "equals", "greater than", or "is empty". You can combine two conditions with AND or OR logic for more precise filtering.

Multiple column filters stack with AND logic. The pink badge in the stats bar shows the number of active filters; click it to clear them all at once.

Step 7: Inspect Nested Values

Click any cell to open the Cell Detail Panel on the right side of the viewer. For nested JSON cells — those containing an object {} or array [] — the detail panel automatically pretty-prints the JSON with indentation, making it easy to read complex structures at full length.

For example, if a cell in your table contains {"bid": 150.25, "ask": 150.30, "mid": 150.275}, clicking that cell shows:

{
  "bid": 150.25,
  "ask": 150.30,
  "mid": 150.275
}

Use the Copy value button in the detail panel to copy the raw value to your clipboard — useful when you need to pass a nested value to another tool.

Step 8: View File Info

Click the Info button in the toolbar to open the file info modal. It shows:

• The loaded file name
• Total rows and columns
• Detected format (JSON or JSONL)
• A column overview table listing each key and its inferred type

Click Copy Column List to copy the full column overview as plain text — useful when documenting an API response schema, building a target table definition, or sharing the data structure with a colleague.

Step 9: Export Your Data

Click the Export button in the toolbar to open the export dialog. Four formats are available:

• CSV — flat comma-separated file; nested objects serialized as JSON strings; ideal for pandas or spreadsheets
• JSON — re-export as a clean JSON array of objects; useful for exporting only a filtered subset
• Excel (.xlsx) — workbook with frozen header row, auto-sized columns, and attribution sheet
• TSV — tab-separated; useful when values may contain commas

Two export scopes let you control what gets exported:

• Filtered view — exports only the rows visible after applying your current search and column filters
• Full file — exports all rows ignoring any active filters

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