Fixed a Styler.bar bug of missing value

Author: ccccjone

pandas/io/formats/style.py

@@ -4081,9 +4081,43 @@ def css_calc(x, left: float, right: float, align: str, color: str | list | tuple
         else:
             return ret
 
+    def replace_pd_NA_with_np_nan(data_structure):
+        """
+        Recursively replace pd.NA with np.nan in a nested list or array.
+
+        This function traverses a nested structure (like lists or numpy arrays)
+        and replaces occurrences of Pandas' NA values (pd.NA) with NumPy's NaN values (np.nan).
+        It handles nested lists and arrays, applying the replacement recursively.
+
+        Parameters:
+        data_structure (list, np.ndarray, or any): A nested list, numpy array, or any other object.
+                                                   If it's a list or numpy array, the function will
+                                                   process its elements recursively.
+
+        Returns:
+        list, np.ndarray, or any: A new object with the same structure as `data_structure`,
+                                  where all pd.NA values have been replaced with np.nan.
+                                  The type of the returned object is the same as the input.
+
+        Example:
+        >>> data = [1, pd.NA, [3, pd.NA, [pd.NA, 5]], pd.NA]
+        >>> replace_pd_NA_with_np_nan(data)
+        [1, nan, [3, nan, [nan, 5]], nan]
+        """
+        if isinstance(data_structure, list):
+            # Process each item in the list recursively
+            return [replace_pd_NA_with_np_nan(element) for element in data_structure]
+        elif isinstance(data_structure, np.ndarray):
+            # Convert numpy array elements recursively
+            return np.array([replace_pd_NA_with_np_nan(element) for element in data_structure])
+        else:
+            # Replace pd.NA with np.nan for individual elements
+            return np.nan if pd.isna(data_structure) else data_structure
+
     values = data.to_numpy()
-    left = np.nanmin(values) if vmin is None else vmin
-    right = np.nanmax(values) if vmax is None else vmax
+    np_values = replace_pd_NA_with_np_nan(values)
+    left = np.nanmin(np_values) if vmin is None else vmin
+    right = np.nanmax(np_values) if vmax is None else vmax
     z: float = 0  # adjustment to translate data
 
     if align == "mid":

pandas/tests/io/formats/style/test_bar.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pytest
 
-from pandas import DataFrame
+from pandas import DataFrame, NA
 
 pytest.importorskip("jinja2")
 
@@ -328,3 +328,10 @@ def test_bar_invalid_color_type_error_raises():
     # Test that providing a color list with more than two elements raises a ValueError
     with pytest.raises(ValueError, match=msg):
         df.style.bar(color=['#d65f5f', '#5fba7d', '#abcdef']).to_html()
+
+
+def test_styler_bar_with_missing_values():
+    df = DataFrame({"A": [1, 2, NA, 4]})
+    expected_substring = "linear-gradient"
+    html_output = df.style.bar(subset='A').to_html()
+    assert expected_substring in html_output