sadpandajoe commented on code in PR #45439:
URL: https://github.com/apache/superset/pull/45439#discussion_r4325985562
##########
tests/unit_tests/charts/test_client_processing.py:
##########
@@ -2501,6 +2501,318 @@ def test_table_applies_si_number_format() -> None:
assert formatted["amount"].tolist() == ["1.23k"]
+def test_table_applies_small_number_format() -> None:
+ """
+ A cell below one in absolute value takes the column's small-number format.
+
+ ``formatColumnValue`` in the Table plugin picks the formatter per value, so
+ the report has to switch per cell rather than per column.
+
+ The switch point itself is covered on both sides and in both signs. The
+ browser's test is ``Math.abs(value) < 1``, so exactly one is a large cell
+ and a large negative stays large: widening the comparison to ``<= 1`` or
+ comparing the signed value would render one of these as a percentage.
+ """
+ df = pd.DataFrame.from_dict(
+ {
+ "ratio": {
+ 0: 0.0523,
+ 1: 12.5,
+ 2: -0.5,
+ 3: 0.0,
+ 4: 1.0,
+ 5: -1.0,
+ 6: -12.5,
+ }
+ }
+ )
+ form_data = {
+ "viz_type": "table",
+ "metrics": ["ratio"],
+ "column_config": {
+ "ratio": {"d3NumberFormat": ",.2f", "d3SmallNumberFormat": ".1%"}
+ },
+ }
+ formatted = table(df, form_data)
+ assert formatted["ratio"].tolist() == [
+ "5.2%",
+ "12.50",
+ "-50.0%",
+ "0.0%",
+ "1.00",
+ "-1.00",
+ "-12.50",
+ ]
+
+
+def test_table_ignores_blank_small_number_format() -> None:
+ """
+ A cleared Select leaves an empty string behind, which
+ ``getSmallNumberFormatter`` treats as unset.
+ """
+ df = pd.DataFrame.from_dict({"ratio": {0: 0.0523, 1: 12.5}})
+ form_data = {
+ "viz_type": "table",
+ "metrics": ["ratio"],
+ "column_config": {
+ "ratio": {"d3NumberFormat": ",.2f", "d3SmallNumberFormat": " "}
+ },
+ }
+ formatted = table(df, form_data)
+ assert formatted["ratio"].tolist() == ["0.05", "12.50"]
+
+
+def test_table_small_number_format_without_a_normal_format() -> None:
+ """
+ A small-number format on its own is enough to format the small cells.
+
+ The other cells follow the browser: a metric falls back to SMART_NUMBER,
+ while a numeric dimension gets no formatter at all and is printed as is.
+ """
+ df = pd.DataFrame.from_dict({"ratio": {0: 0.0523, 1: 1200.0}})
+ form_data = {
+ "viz_type": "table",
+ "metrics": ["ratio"],
+ "column_config": {"ratio": {"d3SmallNumberFormat": ".1%"}},
+ }
+ assert table(df, form_data)["ratio"].tolist() == ["5.2%", "1.2k"]
+
+ df = pd.DataFrame.from_dict({"ratio": {0: 0.0523, 1: 12.5}})
+ form_data = {
+ "viz_type": "table",
+ "metrics": [],
+ "column_config": {"ratio": {"d3SmallNumberFormat": ".1%"}},
+ }
+ assert table(df, form_data)["ratio"].tolist() == ["5.2%", "12.5"]
Review Comment:
The numeric-dimension half of this test uses `12.5`, which SMART_NUMBER also
renders as `12.5`. If someone drops the new `column in metric_labels` condition
so a plain numeric dimension with only a small-number format picks up
SMART_NUMBER, this assertion still passes, even though the PR describes that
case as left unformatted. Would a larger value such as `1200.0` (expected
`'1200'`, versus `'1.2k'` for the metric case above) pin that condition?
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