Group JSON Arrays
by Field

Grouped JSON in four steps

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Common questions answered

What is JSON group-by?

JSON group-by organizes an array of objects into groups based on a specified field value. The result is an object where each unique value becomes a key, and its value is an array of matching objects. This is equivalent to SQL GROUP BY and is essential for data analysis, reporting, and building hierarchical views from flat datasets.

This tool groups JSON arrays by any field, including nested fields via dot notation. For example, grouping orders by "status" produces separate arrays for "pending," "shipped," and "delivered." The output is a valid JSON object ready to use in visualizations, table views, or further processing.

[
  {"name":"Alice","dept":"eng"},
  {"name":"Bob","dept":"sales"},
  {"name":"Charlie","dept":"eng"}
]

{
  "eng": [
    {"name":"Alice","dept":"eng"},
    {"name":"Charlie","dept":"eng"}
  ],
  "sales": [
    {"name":"Bob","dept":"sales"}
  ]
}

Get the most out of this tool

• Group by a category or status field to quickly see how many records fall into each bucket.
• Combine group-by with array sorting to rank items within each group by a secondary field.
• Use dot notation to group by nested fields — "address.city" groups records by their city value.

JSON grouping for data analysis

Group-by is one of the most fundamental data aggregation operations, familiar from SQL's GROUP BY clause and Python's itertools.groupby() function. Applied to a JSON array, group-by takes a field name as the grouping key and produces an object where each unique value of that field becomes a key, and the corresponding value is an array of all elements that had that key value. This transforms flat list data into categorized, summarized form.

The transformation from an array to a grouped object changes the data structure fundamentally. An input of [{status: "active", name: "Alice"}, {status: "inactive", name: "Bob"}, {status: "active", name: "Carol"}] grouped by "status" produces {active: [{name: "Alice"}, {name: "Carol"}], inactive: [{name: "Bob"}]}. The groups are immediately usable for rendering categorized UI, computing counts per category, or passing to chart libraries.

Counting elements per group is the natural follow-on to grouping. After grouping, each group's length gives the count for that category. This is the equivalent of SELECT status, COUNT(*) FROM users GROUP BY status in SQL. The count-per-group pattern is ubiquitous in dashboards, analytics tools, and reporting systems — and it is trivially achievable from the grouped JSON output by mapping each group array to its length.

Multi-level grouping (GROUP BY two fields) produces nested grouped objects. Grouping orders first by year, then by month, produces {2024: {January: [...], February: [...]}, 2025: {January: [...], ...}}. This hierarchical structure maps naturally to multi-level UI components like accordion trees or nested tabs. Each level of nesting corresponds to one grouping dimension.

Aggregations within groups — sum, average, min, max of a numeric field — are common needs alongside grouping. GROUP BY status followed by SUM(amount) gives the total amount per status. While this tool provides the grouping operation, computing aggregations from the grouped result is straightforward: each group array can be reduced to its sum with array.reduce((acc, item) => acc + item.amount, 0) in JavaScript or similar in any language.

JSON grouping is particularly useful when you receive flat API responses that need hierarchical rendering. A list of log entries grouped by date produces a timeline view. A list of products grouped by category produces a catalog view. A list of transactions grouped by merchant produces a spending analysis view. These UI patterns all start with grouping the flat API response by a categorical field.

When developers use this tool

Additional frequently asked questions