[df] Allow reading a char column into a numpy array

bindings/pyroot/pythonizations/python/ROOT/_pythonization/_rdataframe.py

@@ -229,6 +229,7 @@ def pypowarray(numpyvec, pow):
 
 from __future__ import annotations
 
+import warnings
 from typing import Iterable, Optional
 
 from . import pythonization
@@ -296,6 +297,15 @@ def RDataFrameAsNumpy(
     result_ptrs = {}
     for column in columns:
         column_type = df.GetColumnType(column)
+        if column_type == "char":
+            column_type = "unsigned char"
+            warnings.warn(
+                f"RDataFrame.AsNumpy: column '{column}' has type 'char', which would be automatically converted to a "
+                "Python string. Interpreting as 'unsigned char' instead, which results in a numpy array of dtype uint8. "
+                "If you use this column for numeric values, consider migrating this column to a type other than 'char', "
+                "which is usually used for text. For example, migrate to 'signed char' or 'unsigned char' or, "
+                "preferrably, 'std::int8_t' or 'std::uint8_t'."
+            )
 
         # If the column type is a class, make sure cling knows about it
         tclass = ROOT.TClass.GetClass(column_type)

bindings/pyroot/pythonizations/test/rdataframe_asnumpy.py

@@ -7,7 +7,7 @@
 import numpy as np
 import ROOT
 from ROOT._pythonization._rdataframe import _clone_asnumpyresult
-import os
+
 
 def make_tree(*dtypes):
     """
@@ -400,6 +400,26 @@ def test_rdataframe_as_numpy_array_jagged(self):
         self.assertTrue(all(isinstance(x, np.ndarray) for x in array))
         self.assertTrue(all(len(x) == i for i, x in enumerate(array)))
 
+    def test_rdataframe_as_numpy_char_col_as_uint8(self):
+        ROOT.gInterpreter.Declare(
+            r"""
+            #ifndef ROOT_TEST_RDataFrameAsNumpy_GH_22554
+            #define ROOT_TEST_RDataFrameAsNumpy_GH_22554
+            char make_char(ULong64_t i) {
+            return static_cast<char>(65 + (i % 26)); // A, B, C, ...
+            }
+            #endif
+            """
+        )
+
+        rdf = ROOT.RDataFrame(10).Define("mycol", "make_char(rdfentry_)")
+        with self.assertWarns(
+            UserWarning, msg="column 'mycol' has type 'char', which would be automatically converted"
+        ):
+            npy = rdf.AsNumpy(["mycol"])["mycol"]
+            self.assertEqual(npy.dtype, np.uint8)
+            self.assertTrue(all(npy == np.array([65 + (i % 26) for i in range(10)], dtype=np.uint8)))
+
 
 if __name__ == "__main__":
     unittest.main()

tree/dataframe/src/RDFUtils.cxx

@@ -513,9 +513,19 @@ unsigned int GetColumnWidth(const std::vector<std::string>& names, const unsigne
 void CheckReaderTypeMatches(const std::type_info &colType, const std::type_info &requestedType,
                             const std::string &colName)
 {
-   // We want to explicitly support the reading of bools as unsigned char, as
-   // this is quite common to circumvent the std::vector<bool> specialization.
-   const bool explicitlySupported = (colType == typeid(bool) && requestedType == typeid(unsigned char)) ? true : false;
+   // We explicitly support certain type conversions
+   const bool explicitlySupported = [&colType, &requestedType]() {
+      // bool as unsigned char is common to circumvent the std::vector<bool> specialization.
+      if (colType == typeid(bool) && requestedType == typeid(unsigned char))
+         return true;
+      // char as unsigned char allows reading a vector of char as a Python numpy array of integers, avoiding the
+      // automatic conversion of 'char *' to string in Python. For more info, see
+      // https://github.com/root-project/root/issues/22554
+      if (colType == typeid(char) && requestedType == typeid(unsigned char))
+         return true;
+
+      return false;
+   }();
 
    // Here we compare names and not typeinfos since they may come from two different contexts: a compiled
    // and a jitted one.