Add stats command for numeric field summaries

README.md

@@ -38,6 +38,7 @@ After install, the `data-transform` console script is on your `PATH`.
 | `tail` | Last N items of a list |
 | `group-by` | Group array of objects by top-level KEY |
 | `count` | Count items, per KEY value, or per group |
+| `stats` | count / sum / min / max / avg of a numeric FIELD |
 
 ### Options (most commands)
 
@@ -237,6 +238,19 @@ data-transform group-by team people.json | data-transform count --minify
 data-transform count status events.ndjson
 ```
 
+### Stats
+
+`stats FIELD` summarises a numeric top-level field and prints `count`, `sum`, `min`, `max`, and `avg`. On a list root it prints one summary; on `group-by` output it prints one summary per group. Numeric strings count as numbers, so CSV input works directly. Booleans, missing keys, and non-numeric values are skipped. If nothing numeric is found the summary is `count: 0`, `sum: 0`, and `null` for the rest. Whole-number results print as integers.
+
+```bash
+echo '[{"ms":120},{"ms":80},{"ms":"100"},{"ms":"n/a"}]' \
+  | data-transform stats ms -f json --minify
+# → {"count":3,"sum":300,"min":80,"max":120,"avg":100}
+
+# Per-group latency summary
+data-transform group-by service requests.csv | data-transform stats ms
+```
+
 ### Library use
 
 ```python

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "data-transform-cli"
-version = "0.5.0"
+version = "0.6.0"
 description = "CLI for YAML ↔ JSON ↔ CSV ↔ NDJSON transforms for pipelines and shell use"
 readme = "README.md"
 license = { text = "MIT" }

src/data_transform/__init__.py

@@ -13,6 +13,7 @@
     pick_keys,
     rename_keys,
     sort_rows,
+    stats_rows,
     tail_rows,
     unflatten_object,
     unique_rows,
@@ -41,6 +42,7 @@
     "tail_rows",
     "group_rows",
     "count_rows",
+    "stats_rows",
 ]
 
-__version__ = "0.5.0"
+__version__ = "0.6.0"

src/data_transform/cli.py

@@ -12,6 +12,7 @@
 from .convert import convert, detect_format, infer_format, parse, serialize
 from .shape import (
     count_rows,
+    stats_rows,
     filter_rows,
     flatten_object,
     get_path,
@@ -826,5 +827,46 @@ def count_cmd(
         sys.exit(1)
 
 
+@main.command("stats")
+@click.argument("tokens", nargs=-1, required=True)
+@click.option("-o", "--output", default=None, help="write to file instead of stdout")
+@click.option(
+    "-f",
+    "--from",
+    "from_format",
+    type=click.Choice(FORMAT_CHOICES, case_sensitive=False),
+    default=None,
+    help="input format: json | yaml | csv | ndjson",
+)
+@click.option("--minify", is_flag=True, default=False, help="emit compact JSON")
+def stats_cmd(
+    tokens: tuple[str, ...],
+    output: Optional[str],
+    from_format: Optional[str],
+    minify: bool,
+) -> None:
+    """Summarise a numeric FIELD: count, sum, min, max, avg.
+
+    Works on a list root (one summary) or group-by output (one summary per
+    group). Numeric strings count; non-numeric and missing values are skipped.
+
+    Usage: data-transform stats FIELD [file]
+    """
+    try:
+        parts, input_file = _split_tokens_and_file(tokens)
+        if len(parts) != 1:
+            raise ValueError("Usage: stats FIELD [file]")
+        text, name = load_input(input_file)
+        src = resolve_from(text, name, from_format)
+        data = parse(text, src)  # type: ignore[arg-type]
+        result = stats_rows(data, parts[0])
+        write_output(
+            serialize(result, "json", pretty=not minify, minify=minify), output
+        )
+    except Exception as err:  # noqa: BLE001
+        click.echo(f"error: {err}", err=True)
+        sys.exit(1)
+
+
 if __name__ == "__main__":
     main()

src/data_transform/shape.py

@@ -426,6 +426,49 @@ def count_rows(data: Any, key: str | None = None) -> int | dict[str, int]:
     return {label: len(items) for label, items in _group(rows, key).items()}
 
 
+def _stats_one(rows: list[Any], field: str) -> dict[str, Any]:
+    values = [
+        _as_number(item[field])
+        for item in rows
+        if isinstance(item, Mapping) and field in item and _looks_number(item[field])
+    ]
+    if not values:
+        return {"count": 0, "sum": 0, "min": None, "max": None, "avg": None}
+
+    def tidy(x: float) -> int | float:
+        return int(x) if x.is_integer() else x
+
+    total = sum(values)
+    return {
+        "count": len(values),
+        "sum": tidy(total),
+        "min": tidy(min(values)),
+        "max": tidy(max(values)),
+        "avg": tidy(total / len(values)),
+    }
+
+
+def stats_rows(data: Any, field: str) -> dict[str, Any]:
+    """
+    Summarise numeric top-level FIELD: count, sum, min, max, avg.
+
+    A list root returns one summary. An object whose values are all lists
+    (the output of :func:`group_rows`) returns one summary per group label.
+    Numeric strings count as numbers (handy for CSV); booleans, missing
+    keys, and non-numeric values are skipped. With no numeric values the
+    summary has count 0, sum 0, and null min/max/avg. Whole-number results
+    are emitted as integers.
+    """
+    if isinstance(data, list):
+        return _stats_one(data, field)
+    if _is_grouped(data):
+        return {str(label): _stats_one(items, field) for label, items in data.items()}
+    raise ValueError(
+        "stats: root must be a list (array) or an object of lists "
+        f"(group-by output), got {type(data).__name__}"
+    )
+
+
 def _require_count(n: Any, command: str) -> int:
     """Raise ValueError unless n is a non-negative integer (not bool)."""
     if isinstance(n, bool) or not isinstance(n, int) or n < 0:

tests/test_shape.py

@@ -166,14 +166,15 @@ def test_unique_requires_list():
 
 
 def test_public_exports_include_reshape_helpers():
-    assert data_transform.__version__ == "0.5.0"
+    assert data_transform.__version__ == "0.6.0"
     for name in (
         "rename_keys",
         "flatten_object",
         "head_rows",
         "tail_rows",
         "group_rows",
         "count_rows",
+        "stats_rows",
         "unflatten_object",
     ):
         assert getattr(data_transform, name) is not None

tests/test_stats.py

@@ -0,0 +1,48 @@
+import json
+
+import pytest
+
+from data_transform import group_rows, stats_rows
+from test_cli import run_cli
+
+
+def test_stats_list_mixed_values():
+    rows = [{"v": 1}, {"v": "2.5"}, {"v": True}, {"v": "x"}, {}, 3, {"v": 4}]
+    assert stats_rows(rows, "v") == {
+        "count": 3, "sum": 7.5, "min": 1, "max": 4, "avg": 2.5,
+    }
+
+
+def test_stats_empty_and_no_numbers():
+    empty = {"count": 0, "sum": 0, "min": None, "max": None, "avg": None}
+    assert stats_rows([], "v") == empty
+    assert stats_rows([{"v": "a"}], "v") == empty
+
+
+def test_stats_grouped():
+    rows = [{"t": "a", "v": 2}, {"t": "b", "v": 5}, {"t": "a", "v": 4}]
+    out = stats_rows(group_rows(rows, "t"), "v")
+    assert out["a"] == {"count": 2, "sum": 6, "min": 2, "max": 4, "avg": 3}
+    assert out["b"]["avg"] == 5
+
+
+def test_stats_bad_root():
+    with pytest.raises(ValueError, match="stats: root"):
+        stats_rows({"a": 1}, "v")
+
+
+def test_stats_cli_json_and_csv():
+    code, stdout, _ = run_cli(
+        ["stats", "ms", "-f", "json", "--minify"], '[{"ms":120},{"ms":80}]'
+    )
+    assert code == 0
+    assert json.loads(stdout) == {"count": 2, "sum": 200, "min": 80, "max": 120, "avg": 100}
+    code, stdout, _ = run_cli(["stats", "ms", "-f", "csv", "--minify"], "ms\n1\n2\n")
+    assert code == 0 and json.loads(stdout)["avg"] == 1.5
+
+
+def test_stats_cli_errors():
+    code, _, stderr = run_cli(["stats", "-f", "json"], "[]")
+    assert code != 0
+    code, _, stderr = run_cli(["stats", "a", "b", "-f", "json"], "[]")
+    assert code != 0 and "Usage" in stderr