diff --git a/bindings/pyroot/pythonizations/python/ROOT/_pythonization/_rdataframe.py b/bindings/pyroot/pythonizations/python/ROOT/_pythonization/_rdataframe.py
index f9bb2771a73a8..c784c9ca1957c 100644
--- a/bindings/pyroot/pythonizations/python/ROOT/_pythonization/_rdataframe.py
+++ b/bindings/pyroot/pythonizations/python/ROOT/_pythonization/_rdataframe.py
@@ -296,10 +296,6 @@ def RDataFrameAsNumpy(
     result_ptrs = {}
     for column in columns:
         column_type = df.GetColumnType(column)
-        # bool columns should be taken as unsigned chars, because NumPy stores
-        # bools in bytes - different from the std::vector<bool> returned by the
-        # action, which might do some space optimization
-        column_type = "unsigned char" if column_type == "bool" else column_type
 
         # If the column type is a class, make sure cling knows about it
         tclass = ROOT.TClass.GetClass(column_type)
@@ -307,8 +303,10 @@ def RDataFrameAsNumpy(
             raise RuntimeError(
                 f'The column named "{column}" is of type "{column_type}", which is not known to the ROOT interpreter. Please load the corresponding header files or dictionaries.'
             )
-
-        result_ptrs[column] = df.Take[column_type](column)
+        # We take the values via ROOT::RVec to avoid having to deal with std::vector<bool>
+        # This uses one single data structure for all array types, which exposes the array interface
+        # allowing zero-copy conversion to numpy array
+        result_ptrs[column] = df.Take[f"{column_type}, ROOT::RVec<{column_type}>"](column)
 
     result = AsNumpyResult(result_ptrs, columns)
 
diff --git a/bindings/pyroot/pythonizations/python/ROOT/_pythonization/_rvec.py b/bindings/pyroot/pythonizations/python/ROOT/_pythonization/_rvec.py
index d92b0f2c3ab57..7e5f8c42e1ad6 100644
--- a/bindings/pyroot/pythonizations/python/ROOT/_pythonization/_rvec.py
+++ b/bindings/pyroot/pythonizations/python/ROOT/_pythonization/_rvec.py
@@ -62,16 +62,73 @@
 
 from . import pythonization
 
+# This map includes all relevant C++ fundamental types found at
+# https://en.cppreference.com/w/cpp/language/types.html and the associated
+# ROOT portable types when available.
 _array_interface_dtype_map = {
-    "Long64_t": "i",
-    "ULong64_t": "u",
-    "double": "f",
-    "float": "f",
+    # Integral types
+    # C++ standard integer types
+    "short": "i",
+    "short int": "i",
+    "signed short": "i",
+    "signed short int": "i",
+    "unsigned short": "u",
+    "unsigned short int": "u",
     "int": "i",
-    "long": "i",
-    "unsigned char": "b",
+    "signed": "i",
+    "signed int": "i",
+    "unsigned": "u",
     "unsigned int": "u",
+    "long": "i",
+    "long int": "i",
+    "signed long": "i",
+    "signed long int": "i",
     "unsigned long": "u",
+    "unsigned long int": "u",
+    "long long": "i",
+    "long long int": "i",
+    "signed long long": "i",
+    "signed long long int": "i",
+    "unsigned long long": "u",
+    "unsigned long long int": "u",
+    "std::size_t": "i",
+    # Extended standard integer types
+    "std::int8_t": "i",
+    "std::int16_t": "i",
+    "std::int32_t": "i",
+    "std::int64_t": "i",
+    "std::uint8_t": "u",
+    "std::uint16_t": "u",
+    "std::uint32_t": "u",
+    "std::uint64_t": "u",
+    # ROOT integer types
+    "Int_t": "i",
+    "UInt_t": "u",
+    "Short_t": "i",
+    "UShort_t": "u",
+    "Long_t": "i",
+    "ULong_t": "u",
+    "Long64_t": "i",
+    "ULong64_t": "u",
+    # Boolean type
+    "bool": "b",
+    "Bool_t": "b",
+    # Character types
+    "char": "i",
+    "Char_t": "i",
+    "signed char": "i",
+    "unsigned char": "u",
+    "UChar_t": "u",
+    "char16_t": "i",
+    "char32_t": "i",
+    # Floating-point types
+    # C++ standard floating-point types
+    "float": "f",
+    "double": "f",
+    "long double": "f",
+    # ROOT floating-point types
+    "Float_t": "f",
+    "Double_t": "f",
 }
 
 
diff --git a/bindings/pyroot/pythonizations/test/rdataframe_asnumpy.py b/bindings/pyroot/pythonizations/test/rdataframe_asnumpy.py
index dd9dd87f64507..0bc84e84b5b41 100644
--- a/bindings/pyroot/pythonizations/test/rdataframe_asnumpy.py
+++ b/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):
     """
@@ -90,10 +90,22 @@ def test_branch_bool(self):
         Test bool data-type as a special case since we cannot adopt
         the std::vector<bool> with numpy arrays
         """
-        df = ROOT.RDataFrame(2).Define("x", "bool(rdfentry_)")
+        treename = "test_branch_bool"
+        filename = "test_branch_bool.root"
+        # Snapshot a TTree so that column 'x' will be of type 'Bool_t'
+        ROOT.RDataFrame(2).Define("x", "bool(rdfentry_)").Snapshot(treename, filename)
+        # The column 'y' will instead have type 'bool'
+        df = ROOT.RDataFrame(treename, filename).Define("y", "bool(rdfentry_)")
+        self.assertEqual(df.GetColumnType("x"), "Bool_t")
+        self.assertEqual(df.GetColumnType("y"), "bool")
         npy = df.AsNumpy()
-        self.assertFalse(bool(npy["x"][0]))
-        self.assertTrue(bool(npy["x"][1]))
+        # Both numpy arrays should have dtype bool
+        self.assertEqual(npy["x"].dtype, bool)
+        self.assertEqual(npy["y"].dtype, bool)
+        self.assertFalse(npy["x"][0])
+        self.assertTrue(npy["x"][1])
+        self.assertFalse(npy["y"][0])
+        self.assertTrue(npy["y"][1])
 
     def test_read_array(self):
         """