diff --git a/CHANGES.rst b/CHANGES.rst
index 39fda8f..fc01b45 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -2,6 +2,10 @@
 Release notes
 =============
 
+Version 1.1.1, Aug 2024
+-----------------------
+* Compatibility with numpy v2.3: converting np.number to a dtype no longer allowed.
+
 Version 1.1.0, Dec 2024
 -----------------------
 * Removed all ROOT, cpp and cuda code, it was no longer supported.
diff --git a/README.rst b/README.rst
index 3f52ceb..f65a832 100644
--- a/README.rst
+++ b/README.rst
@@ -17,8 +17,8 @@ more quickly via Numpy commands, rather than Python for loops.
 
 This Python implementation of histogrammar been tested to guarantee compatibility with its Scala implementation.
 
-Latest Python release: v1.1.0 (Feb 2025).
-Latest update: Feb 2025.
+Latest Python release: v1.1.1 (Aug 2025).
+Latest update: Aug 2025.
 
 References
 ==========
diff --git a/histogrammar/util.py b/histogrammar/util.py
index d13c7ad..25987eb 100644
--- a/histogrammar/util.py
+++ b/histogrammar/util.py
@@ -533,8 +533,8 @@ def get_datatype(hist, itr=0):
         return []
     if isinstance(hist, histogrammar.Bag):
         if hist.dimension > 0:
-            return [np.number] * hist.dimension
-        return [str] if hist.range == "S" else [np.number]
+            return [float] * hist.dimension
+        return [str] if hist.range == "S" else [float]
     if isinstance(
         hist,
         (
@@ -550,7 +550,7 @@ def get_datatype(hist, itr=0):
             return []
         # else: no parent histogram.
         # input datatype must be a number. Let's also make an educated guess if it's a converted timestamp
-        datatype = [np.number]
+        datatype = [float]
         if isinstance(hist, histogrammar.Maximize):
             value = hist.max
         elif isinstance(hist, histogrammar.Minimize):
@@ -582,7 +582,7 @@ def get_datatype(hist, itr=0):
     else:
         # input datatype must be a number.
         # let's make an educated guess if it's a converted timestamp
-        datatype = [np.number]
+        datatype = [float]
         if isinstance(hist, (histogrammar.Bin, histogrammar.SparselyBin)):
             values = [hist.low, hist.high]
         elif isinstance(hist, histogrammar.CentrallyBin):
diff --git a/histogrammar/version.py b/histogrammar/version.py
index e0be64f..54c25e9 100644
--- a/histogrammar/version.py
+++ b/histogrammar/version.py
@@ -2,7 +2,7 @@
 
 import re
 
-version = "1.1.0"
+version = "1.1.1"
 
 
 def split_version_string(version_string: str) -> tuple[int, int]:
diff --git a/pyproject.toml b/pyproject.toml
index dd0d4a2..97da278 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -40,7 +40,7 @@ pandas = [
     "pandas"
 ]
 spark = [
-    "pyspark>=3.1; python_version <= '3.11'",
+    "pyspark<4; python_version <= '3.11'",
 ]
 test = [
     "ipykernel>=5.1.3",
diff --git a/tests/test_numpy.py b/tests/test_numpy.py
index 2bea87b..cb1fbc4 100644
--- a/tests/test_numpy.py
+++ b/tests/test_numpy.py
@@ -1111,8 +1111,8 @@ def test_datatype(self):
             df, hist1, hist2, hist3 = get_test_histograms1()
 
             assert hist1.datatype == str
-            np.testing.assert_array_equal(hist2.datatype, [np.number, str])
-            np.testing.assert_array_equal(hist3.datatype, [np.datetime64, np.number, str])
+            np.testing.assert_array_equal(hist2.datatype, [float, str])
+            np.testing.assert_array_equal(hist3.datatype, [np.datetime64, float, str])
 
     def test_n_bins(self):
         """Test getting the number of allocated bins"""