BUG: Categorical data fails to load from hdf when all columns are NaN

.gitignore

@@ -106,3 +106,4 @@ doc/build/html/index.html
 doc/tmp.sv
 doc/source/styled.xlsx
 doc/source/templates/
+env/

doc/source/whatsnew/v0.21.1.txt

@@ -126,6 +126,7 @@ I/O
 - Bug in :func:`read_json` not decoding when reading line deliminted JSON from S3 (:issue:`17200`)
 - Bug in :func:`pandas.io.json.json_normalize` to avoid modification of ``meta`` (:issue:`18610`)
 - Bug in :func:`to_latex` where repeated multi-index values were not printed even though a higher level index differed from the previous row (:issue:`14484`)
+- Bug when reading NaN-only categorical columns in :class:`HDFStore` (:issue:`18413`)
 
 Plotting
 ^^^^^^^^

pandas/io/pytables.py

@@ -2137,10 +2137,17 @@ def convert(self, values, nan_rep, encoding):
                 # if we have stored a NaN in the categories
                 # then strip it; in theory we could have BOTH
                 # -1s in the codes and nulls :<
-                mask = isna(categories)
-                if mask.any():
-                    categories = categories[~mask]
-                    codes[codes != -1] -= mask.astype(int).cumsum().values
+                if categories is None:
+                    # Handle case of NaN-only categorical columns in which case
+                    # the categories are an empty array; when this is stored,
+                    # pytables cannot write a zero-len array, so on readback
+                    # the categories would be None and `read_hdf()` would fail.
+                    categories = Index([], dtype=np.float64)
+                else:
+                    mask = isna(categories)
+                    if mask.any():
+                        categories = categories[~mask]
+                        codes[codes != -1] -= mask.astype(int).cumsum().values
 
                 self.data = Categorical.from_codes(codes,
                                                    categories=categories,

pandas/tests/io/test_pytables.py

@@ -4928,6 +4928,25 @@ def test_categorical_conversion(self):
             result = read_hdf(path, 'df', where='obsids=B')
             tm.assert_frame_equal(result, expected)
 
+    def test_categorical_nan_only_columns(self):
+        # GH18413
+        # Check that read_hdf with categorical columns with NaN-only values can
+        # be read back.
+        df = pd.DataFrame({
+            'a': ['a', 'b', 'c', np.nan],
+            'b': [np.nan, np.nan, np.nan, np.nan],
+            'c': [1, 2, 3, 4],
+            'd': pd.Series([None] * 4, dtype=object)
+        })
+        df['a'] = df.a.astype('category')
+        df['b'] = df.b.astype('category')
+        df['d'] = df.b.astype('category')
+        expected = df
+        with ensure_clean_path(self.path) as path:
+            df.to_hdf(path, 'df', format='table', data_columns=True)
+            result = read_hdf(path, 'df')
+            tm.assert_frame_equal(result, expected)
+
     def test_duplicate_column_name(self):
         df = DataFrame(columns=["a", "a"], data=[[0, 0]])