BUG-19214 int categoricals are formatted as ints (pandas-dev#24494)

Author: JustinZhengBC

doc/source/whatsnew/v0.24.0.rst

@@ -1142,6 +1142,40 @@ cast from integer dtype to floating dtype (:issue:`22019`)
        ...:                    'c': [1, 1, np.nan, 1, 1]})
     In [4]: pd.crosstab(df.a, df.b, normalize='columns')
 
+.. _whatsnew_0240.api.concat_categorical:
+
+Concatenation Changes
+^^^^^^^^^^^^^^^^^^^^^
+
+Calling :func:`pandas.concat` on a ``Categorical`` of ints with NA values now
+causes them to be processed as objects when concatenating with anything
+other than another ``Categorical`` of ints (:issue:`19214`)
+
+.. ipython:: python
+
+    s = pd.Series([0, 1, np.nan])
+    c = pd.Series([0, 1, np.nan], dtype="category")
+
+*Previous Behavior*
+
+.. code-block:: ipython
+
+    In [3]: pd.concat([s, c])
+    Out[3]:
+    0    0.0
+    1    1.0
+    2    NaN
+    0    0.0
+    1    1.0
+    2    NaN
+    dtype: float64
+
+*New Behavior*
+
+.. ipython:: python
+
+    pd.concat([s, c])
+
 Datetimelike API Changes
 ^^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -1623,6 +1657,9 @@ MultiIndex
 I/O
 ^^^
 
+- Bug where integer categorical data would be formatted as floats if ``NaN`` values were present (:issue:`19214`)
+
+
 .. _whatsnew_0240.bug_fixes.nan_with_str_dtype:
 
 Proper handling of `np.NaN` in a string data-typed column with the Python engine

pandas/core/arrays/categorical.py

@@ -1491,6 +1491,9 @@ def get_values(self):
         # if we are a datetime and period index, return Index to keep metadata
         if is_datetimelike(self.categories):
             return self.categories.take(self._codes, fill_value=np.nan)
+        elif is_integer_dtype(self.categories) and -1 in self._codes:
+            return self.categories.astype("object").take(self._codes,
+                                                         fill_value=np.nan)
         return np.array(self)
 
     def check_for_ordered(self, op):

pandas/tests/arrays/categorical/test_repr.py

@@ -240,6 +240,17 @@ def test_categorical_repr_datetime_ordered(self):
 
         assert repr(c) == exp
 
+    def test_categorical_repr_int_with_nan(self):
+        c = Categorical([1, 2, np.nan])
+        c_exp = """[1, 2, NaN]\nCategories (2, int64): [1, 2]"""
+        assert repr(c) == c_exp
+
+        s = Series([1, 2, np.nan], dtype="object").astype("category")
+        s_exp = """0      1\n1      2\n2    NaN
+dtype: category
+Categories (2, int64): [1, 2]"""
+        assert repr(s) == s_exp
+
     def test_categorical_repr_period(self):
         idx = period_range('2011-01-01 09:00', freq='H', periods=5)
         c = Categorical(idx)

pandas/tests/reshape/test_concat.py

@@ -495,7 +495,7 @@ def test_concat_categorical(self):
         s1 = pd.Series([10, 11, np.nan], dtype='category')
         s2 = pd.Series([np.nan, 1, 3, 2], dtype='category')
 
-        exp = pd.Series([10, 11, np.nan, np.nan, 1, 3, 2])
+        exp = pd.Series([10, 11, np.nan, np.nan, 1, 3, 2], dtype='object')
         tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
         tm.assert_series_equal(s1.append(s2, ignore_index=True), exp)
 
@@ -515,12 +515,12 @@ def test_concat_categorical_coercion(self):
         s1 = pd.Series([1, 2, np.nan], dtype='category')
         s2 = pd.Series([2, 1, 2])
 
-        exp = pd.Series([1, 2, np.nan, 2, 1, 2])
+        exp = pd.Series([1, 2, np.nan, 2, 1, 2], dtype='object')
         tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
         tm.assert_series_equal(s1.append(s2, ignore_index=True), exp)
 
         # result shouldn't be affected by 1st elem dtype
-        exp = pd.Series([2, 1, 2, 1, 2, np.nan])
+        exp = pd.Series([2, 1, 2, 1, 2, np.nan], dtype='object')
         tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
         tm.assert_series_equal(s2.append(s1, ignore_index=True), exp)
 
@@ -540,11 +540,11 @@ def test_concat_categorical_coercion(self):
         s1 = pd.Series([10, 11, np.nan], dtype='category')
         s2 = pd.Series([1, 3, 2])
 
-        exp = pd.Series([10, 11, np.nan, 1, 3, 2])
+        exp = pd.Series([10, 11, np.nan, 1, 3, 2], dtype='object')
         tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
         tm.assert_series_equal(s1.append(s2, ignore_index=True), exp)
 
-        exp = pd.Series([1, 3, 2, 10, 11, np.nan])
+        exp = pd.Series([1, 3, 2, 10, 11, np.nan], dtype='object')
         tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
         tm.assert_series_equal(s2.append(s1, ignore_index=True), exp)
 
@@ -580,11 +580,13 @@ def test_concat_categorical_3elem_coercion(self):
         s2 = pd.Series([2, 1, 2], dtype='category')
         s3 = pd.Series([1, 2, 1, 2, np.nan])
 
-        exp = pd.Series([1, 2, np.nan, 2, 1, 2, 1, 2, 1, 2, np.nan])
+        exp = pd.Series([1, 2, np.nan, 2, 1, 2, 1, 2, 1, 2, np.nan],
+                        dtype='object')
         tm.assert_series_equal(pd.concat([s1, s2, s3], ignore_index=True), exp)
         tm.assert_series_equal(s1.append([s2, s3], ignore_index=True), exp)
 
-        exp = pd.Series([1, 2, 1, 2, np.nan, 1, 2, np.nan, 2, 1, 2])
+        exp = pd.Series([1, 2, 1, 2, np.nan, 1, 2, np.nan, 2, 1, 2],
+                        dtype='object')
         tm.assert_series_equal(pd.concat([s3, s1, s2], ignore_index=True), exp)
         tm.assert_series_equal(s3.append([s1, s2], ignore_index=True), exp)
 
@@ -668,7 +670,7 @@ def test_concat_categorical_coercion_nan(self):
         s1 = pd.Series([1, np.nan], dtype='category')
         s2 = pd.Series([np.nan, np.nan])
 
-        exp = pd.Series([1, np.nan, np.nan, np.nan])
+        exp = pd.Series([1, np.nan, np.nan, np.nan], dtype='object')
         tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
         tm.assert_series_equal(s1.append(s2, ignore_index=True), exp)