Add type support for serialize_numpy_dtype(); Determine default sorting direction

Author: schloerke

shiny/render/_data_frame.py

@@ -939,9 +939,8 @@ async def update_sort(
         Parameters
         ----------
         sort
-            A list of column sorting information. If `None`, sorting will be removed.
+            A list of column sorting information. If `None`, sorting will be removed. `int` values will be upgraded to `{"col": int, "desc": <DESC>}` where `<DESC>` is `True` if the column is number like and `False` otherwise.
         """
-        print("update_sort", sort)
         if sort is None:
             sort = ()
         elif isinstance(sort, int):
@@ -960,6 +959,12 @@ async def update_sort(
             ncol = len(data.columns)
 
             for val in sort:
+                val_dict: ColumnSort
+                if isinstance(val, int):
+
+                    col: pd.Series[Any] = data[val]
+                    desc = serialize_numpy_dtype(col)["type"] == "numeric"
+                    val_dict = {"col": val, "desc": desc}
                 val_dict: ColumnSort = (
                     val if isinstance(val, dict) else {"col": val, "desc": True}
                 )

shiny/render/_data_frame_utils/_unsafe.py

@@ -6,11 +6,11 @@
 
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Any, Protocol, runtime_checkable
+from typing import TYPE_CHECKING, Any, Literal, Protocol, runtime_checkable
 
 from htmltools import HTML, MetadataNode, Tagifiable
 
-from ..._typing_extensions import TypeGuard
+from ..._typing_extensions import TypedDict, TypeGuard
 
 if TYPE_CHECKING:
     import pandas as pd
@@ -22,7 +22,25 @@
 # pyright: reportUnknownVariableType=false
 
 
-def serialize_numpy_dtypes(df: "pd.DataFrame") -> list[dict[str, Any]]:
+class DataFrameDtypeOriginal(TypedDict):
+    type: str
+
+
+class DataFrameDtypeSubset(TypedDict):
+    type: Literal["numeric", "string", "html", "unknown"]
+
+
+class DataFrameDtypeCategories(TypedDict):
+    type: Literal["categorical"]
+    categories: list[str]
+
+
+DataFrameDtype = (
+    DataFrameDtypeOriginal | DataFrameDtypeSubset | DataFrameDtypeCategories
+)
+
+
+def serialize_numpy_dtypes(df: "pd.DataFrame") -> list[DataFrameDtype]:
     return [serialize_numpy_dtype(col) for _, col in df.items()]
 
 
@@ -31,17 +49,15 @@ def col_contains_shiny_html(col: "pd.Series") -> bool:
 
 
 def serialize_numpy_dtype(
-    col: "pd.Series",
-) -> dict[str, Any]:
+    col: "pd.Series[Any]",
+) -> DataFrameDtypeOriginal | DataFrameDtypeSubset | DataFrameDtypeCategories:
     import pandas as pd
 
     t = pd.api.types.infer_dtype(col)
     # t can be any of: string, bytes, floating, integer, mixed-integer,
     #     mixed-integer-float, decimal, complex, categorical, boolean, datetime64,
     #     datetime, date, timedelta64, timedelta, time, period, mixed, unknown-array
 
-    res: dict[str, Any] = {}
-
     if t == "string":
         if col_contains_shiny_html(col):
             t = "html"
@@ -51,16 +67,17 @@ def serialize_numpy_dtype(
     elif t in ["bytes", "floating", "integer", "decimal", "mixed-integer-float"]:
         t = "numeric"
     elif t == "categorical":
-        res["categories"] = [str(x) for x in col.cat.categories.to_list()]
+        return {
+            "type": "categorical",
+            "categories": [str(x) for x in col.cat.categories.to_list()],
+        }
     else:
         if col_contains_shiny_html(col):
             t = "html"
         else:
             t = "unknown"
 
-    res["type"] = t
-
-    return res
+    return {"type": t}
 
 
 # TODO-future; Replace this class with `htmltools.ReprHtml` when it is publically available. Even better... there should be a "is tag-like" method in htmltools that determines if the object could be enhanced by rendering