diff --git a/superset/utils/pandas_postprocessing/histogram.py b/superset/utils/pandas_postprocessing/histogram.py
index 659eac7b42d1..72dbee855c31 100644
--- a/superset/utils/pandas_postprocessing/histogram.py
+++ b/superset/utils/pandas_postprocessing/histogram.py
@@ -48,6 +48,11 @@ def histogram(
     if groupby is None:
         groupby = []
 
+    # drop empty values from the target column
+    df.dropna(subset=[column], inplace=True)
+    if df.empty:
+        return df
+
     # convert to numeric, coercing errors to NaN
     df[column] = to_numeric(df[column], errors="coerce")
 
diff --git a/tests/unit_tests/pandas_postprocessing/test_histogram.py b/tests/unit_tests/pandas_postprocessing/test_histogram.py
index 9246a06df082..68ffdb0b7734 100644
--- a/tests/unit_tests/pandas_postprocessing/test_histogram.py
+++ b/tests/unit_tests/pandas_postprocessing/test_histogram.py
@@ -140,3 +140,70 @@ def test_histogram_with_some_non_numeric_values():
         histogram(data_with_non_numeric, "a", ["group"], bins)
     except ValueError as e:
         assert str(e) == "Column 'group' contains non-numeric values"  # noqa: PT017
+
+
+def test_histogram_with_groupby_and_some_null_values():
+    data_with_groupby_and_some_nulls = DataFrame(
+        {
+            "group": ["A", "A", "B", "B", "A", "A", "B", "B", "A", "A"],
+            "a": [1, 2, 3, 4, 5, None, 7, 8, 9, 10],
+            "b": [1, 2, 3, 4, 5, None, 7, 8, 9, 10],
+        }
+    )
+
+    result = histogram(data_with_groupby_and_some_nulls, "a", ["group"], bins)
+    assert result.shape == (2, bins + 1)
+    assert result.columns.tolist() == [
+        "group",
+        "1.0 - 2.8",
+        "2.8 - 4.6",
+        "4.6 - 6.4",
+        "6.4 - 8.2",
+        "8.2 - 10.0",
+    ]
+    assert result.values.tolist() == [["A", 2, 0, 1, 0, 2], ["B", 0, 2, 0, 2, 0]]
+
+
+def test_histogram_with_no_groupby_and_some_null_values():
+    data_with_no_groupby_and_some_nulls = DataFrame(
+        {
+            "a": [1, 2, 3, 4, 5, None, 7, 8, 9, 10],
+            "b": [1, 2, 3, 4, 5, None, 7, 8, 9, 10],
+        }
+    )
+
+    result = histogram(data_with_no_groupby_and_some_nulls, "a", [], bins)
+    assert result.shape == (1, bins)
+    assert result.columns.tolist() == [
+        "1.0 - 2.8",
+        "2.8 - 4.6",
+        "4.6 - 6.4",
+        "6.4 - 8.2",
+        "8.2 - 10.0",
+    ]
+    assert result.values.tolist() == [[2, 2, 1, 2, 2]]
+
+
+def test_histogram_with_groupby_and_all_null_values():
+    data_with_groupby_and_all_nulls = DataFrame(
+        {
+            "group": ["A", "A", "B", "B", "A", "A", "B", "B", "A", "A"],
+            "a": [None, None, None, None, None, None, None, None, None, None],
+            "b": [None, None, None, None, None, None, None, None, None, None],
+        }
+    )
+
+    result = histogram(data_with_groupby_and_all_nulls, "a", ["group"], bins)
+    assert result.empty
+
+
+def test_histogram_with_no_groupby_and_all_null_values():
+    data_with_no_groupby_and_all_nulls = DataFrame(
+        {
+            "a": [None, None, None, None, None, None, None, None, None, None],
+            "b": [None, None, None, None, None, None, None, None, None, None],
+        }
+    )
+
+    result = histogram(data_with_no_groupby_and_all_nulls, "a", [], bins)
+    assert result.empty