diff --git a/docs/user-guide/storage.rst b/docs/user-guide/storage.rst
index 908ef2b2..96721256 100644
--- a/docs/user-guide/storage.rst
+++ b/docs/user-guide/storage.rst
@@ -84,7 +84,7 @@ Mean
 
 This storage tracks a "Profile", that is, the mean value of the accumulation instead of the sum.
 It stores the count (as a double), the mean, and a term that is used to compute the variance. When
-filling, you can add a ``sample=`` term.
+filling, you must add a ``sample=`` term.
 
 
 WeightedMean
diff --git a/include/bh_python/fill.hpp b/include/bh_python/fill.hpp
index d2262f62..acdfb139 100644
--- a/include/bh_python/fill.hpp
+++ b/include/bh_python/fill.hpp
@@ -20,6 +20,7 @@
 #include <boost/mp11.hpp>
 #include <boost/variant2/variant.hpp>
 
+#include <cmath>
 #include <stdexcept>
 #include <type_traits>
 #include <vector>
@@ -95,6 +96,25 @@ inline decltype(auto) special_cast<c_array_t<std::string>>(py::handle x) {
     return py::cast<B>(x);
 }
 
+// Allow single floats to be integers
+template <>
+inline decltype(auto) special_cast<int>(py::handle x) {
+    try {
+        return static_cast<int>(std::floor(py::cast<double>(x)));
+    } catch(const py::cast_error&) {
+        return py::cast<int>(x);
+    }
+}
+
+// Allow float arrays to be integers
+template <>
+inline decltype(auto) special_cast<c_array_t<int>>(py::handle x) {
+    auto np     = py::module_::import("numpy");
+    auto to_int = np.attr("floor")(x);
+
+    return py::cast<c_array_t<int>>(to_int);
+}
+
 using arg_t = variant::variant<c_array_t<double>,
                                double,
                                c_array_t<int>,
diff --git a/tests/test_histogram.py b/tests/test_histogram.py
index 30a608ff..607a7b95 100644
--- a/tests/test_histogram.py
+++ b/tests/test_histogram.py
@@ -117,6 +117,19 @@ def test_fill_int_1d():
         h[-3]
 
 
+def test_fill_int_with_float_single_1d():
+    h = bh.Histogram(bh.axis.Integer(-1, 2))
+    h.fill(0.3)
+    h.fill(-0.3)
+    assert h.values() == approx(np.array([1, 1, 0]))
+
+
+def test_fill_int_with_float_array_1d():
+    h = bh.Histogram(bh.axis.Integer(-1, 2))
+    h.fill([-0.3, 0.3])
+    assert h.values() == approx(np.array([1, 1, 0]))
+
+
 def test_fill_1d(flow):
     h = bh.Histogram(bh.axis.Regular(3, -1, 2, underflow=flow, overflow=flow))
     with pytest.raises(ValueError):