REF: rename 'labels' to 'codes' in algorithms.safe_sort and ._factorize

pandas/core/algorithms.py

@@ -448,9 +448,11 @@ def isin(comps, values) -> np.ndarray:
     return f(comps, values)
 
 
-def _factorize_array(values, na_sentinel: int = -1, size_hint=None, na_value=None):
+def _factorize_array(
+    values, na_sentinel: int = -1, size_hint=None, na_value=None
+) -> Tuple[np.ndarray, np.ndarray]:
     """
-    Factorize an array-like to labels and uniques.
+    Factorize an array-like to codes and uniques.
 
     This doesn't do any coercion of types or unboxing before factorization.
 
@@ -468,18 +470,16 @@ def _factorize_array(values, na_sentinel: int = -1, size_hint=None, na_value=Non
 
     Returns
     -------
-    labels : ndarray
+    codes : ndarray
     uniques : ndarray
     """
     hash_klass, values = _get_data_algo(values)
 
     table = hash_klass(size_hint or len(values))
-    uniques, labels = table.factorize(
-        values, na_sentinel=na_sentinel, na_value=na_value
-    )
+    uniques, codes = table.factorize(values, na_sentinel=na_sentinel, na_value=na_value)
 
-    labels = ensure_platform_int(labels)
-    return labels, uniques
+    codes = ensure_platform_int(codes)
+    return codes, uniques
 
 
 _shared_docs[
@@ -1924,51 +1924,52 @@ def diff(arr, n: int, axis: int = 0):
 #  this module.
 def safe_sort(
     values,
-    labels=None,
+    codes=None,
     na_sentinel: int = -1,
     assume_unique: bool = False,
     verify: bool = True,
-):
+) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:
     """
-    Sort ``values`` and reorder corresponding ``labels``.
-    ``values`` should be unique if ``labels`` is not None.
+    Sort ``values`` and reorder corresponding ``codes``.
+
+    ``values`` should be unique if ``codes`` is not None.
     Safe for use with mixed types (int, str), orders ints before strs.
 
     Parameters
     ----------
     values : list-like
-        Sequence; must be unique if ``labels`` is not None.
-    labels : list_like
+        Sequence; must be unique if ``codes`` is not None.
+    codes : list_like, optional
         Indices to ``values``. All out of bound indices are treated as
         "not found" and will be masked with ``na_sentinel``.
     na_sentinel : int, default -1
-        Value in ``labels`` to mark "not found".
-        Ignored when ``labels`` is None.
+        Value in ``codes`` to mark "not found".
+        Ignored when ``codes`` is None.
     assume_unique : bool, default False
         When True, ``values`` are assumed to be unique, which can speed up
-        the calculation. Ignored when ``labels`` is None.
+        the calculation. Ignored when ``codes`` is None.
     verify : bool, default True
-        Check if labels are out of bound for the values and put out of bound
-        labels equal to na_sentinel. If ``verify=False``, it is assumed there
-        are no out of bound labels. Ignored when ``labels`` is None.
+        Check if codes are out of bound for the values and put out of bound
+        codes equal to na_sentinel. If ``verify=False``, it is assumed there
+        are no out of bound codes. Ignored when ``codes`` is None.
 
         .. versionadded:: 0.25.0
 
     Returns
     -------
     ordered : ndarray
         Sorted ``values``
-    new_labels : ndarray
-        Reordered ``labels``; returned when ``labels`` is not None.
+    new_codes : ndarray
+        Reordered ``codes``; returned when ``codes`` is not None.
 
     Raises
     ------
     TypeError
-        * If ``values`` is not list-like or if ``labels`` is neither None
+        * If ``values`` is not list-like or if ``codes`` is neither None
         nor list-like
         * If ``values`` cannot be sorted
     ValueError
-        * If ``labels`` is not None and ``values`` contain duplicates.
+        * If ``codes`` is not None and ``values`` contain duplicates.
     """
     if not is_list_like(values):
         raise TypeError(
@@ -2002,22 +2003,22 @@ def sort_mixed(values):
             # try this anyway
             ordered = sort_mixed(values)
 
-    # labels:
+    # codes:
 
-    if labels is None:
+    if codes is None:
         return ordered
 
-    if not is_list_like(labels):
+    if not is_list_like(codes):
         raise TypeError(
             "Only list-like objects or None are allowed to be"
-            "passed to safe_sort as labels"
+            "passed to safe_sort as codes"
         )
-    labels = ensure_platform_int(np.asarray(labels))
+    codes = ensure_platform_int(np.asarray(codes))
 
     from pandas import Index
 
     if not assume_unique and not Index(values).is_unique:
-        raise ValueError("values should be unique if labels is not None")
+        raise ValueError("values should be unique if codes is not None")
 
     if sorter is None:
         # mixed types
@@ -2029,23 +2030,23 @@ def sort_mixed(values):
     if na_sentinel == -1:
         # take_1d is faster, but only works for na_sentinels of -1
         order2 = sorter.argsort()
-        new_labels = take_1d(order2, labels, fill_value=-1)
+        new_codes = take_1d(order2, codes, fill_value=-1)
         if verify:
-            mask = (labels < -len(values)) | (labels >= len(values))
+            mask = (codes < -len(values)) | (codes >= len(values))
         else:
             mask = None
     else:
         reverse_indexer = np.empty(len(sorter), dtype=np.int_)
         reverse_indexer.put(sorter, np.arange(len(sorter)))
         # Out of bound indices will be masked with `na_sentinel` next, so we
         # may deal with them here without performance loss using `mode='wrap'`
-        new_labels = reverse_indexer.take(labels, mode="wrap")
+        new_codes = reverse_indexer.take(codes, mode="wrap")
 
-        mask = labels == na_sentinel
+        mask = codes == na_sentinel
         if verify:
-            mask = mask | (labels < -len(values)) | (labels >= len(values))
+            mask = mask | (codes < -len(values)) | (codes >= len(values))
 
     if mask is not None:
-        np.putmask(new_labels, mask, na_sentinel)
+        np.putmask(new_codes, mask, na_sentinel)
 
-    return ordered, ensure_platform_int(new_labels)
+    return ordered, ensure_platform_int(new_codes)

pandas/tests/test_sorting.py

@@ -314,27 +314,27 @@ def verify_order(df):
 
 
 def test_decons():
-    def testit(label_list, shape):
-        group_index = get_group_index(label_list, shape, sort=True, xnull=True)
-        label_list2 = decons_group_index(group_index, shape)
+    def testit(codes_list, shape):
+        group_index = get_group_index(codes_list, shape, sort=True, xnull=True)
+        codes_list2 = decons_group_index(group_index, shape)
 
-        for a, b in zip(label_list, label_list2):
+        for a, b in zip(codes_list, codes_list2):
             tm.assert_numpy_array_equal(a, b)
 
     shape = (4, 5, 6)
-    label_list = [
+    codes_list = [
         np.tile([0, 1, 2, 3, 0, 1, 2, 3], 100).astype(np.int64),
         np.tile([0, 2, 4, 3, 0, 1, 2, 3], 100).astype(np.int64),
         np.tile([5, 1, 0, 2, 3, 0, 5, 4], 100).astype(np.int64),
     ]
-    testit(label_list, shape)
+    testit(codes_list, shape)
 
     shape = (10000, 10000)
-    label_list = [
+    codes_list = [
         np.tile(np.arange(10000, dtype=np.int64), 5),
         np.tile(np.arange(10000, dtype=np.int64), 5),
     ]
-    testit(label_list, shape)
+    testit(codes_list, shape)
 
 
 class TestSafeSort:
@@ -355,42 +355,42 @@ def test_basic_sort(self):
         tm.assert_numpy_array_equal(result, expected)
 
     @pytest.mark.parametrize("verify", [True, False])
-    def test_labels(self, verify):
+    def test_codes(self, verify):
         values = [3, 1, 2, 0, 4]
         expected = np.array([0, 1, 2, 3, 4])
 
-        labels = [0, 1, 1, 2, 3, 0, -1, 4]
-        result, result_labels = safe_sort(values, labels, verify=verify)
-        expected_labels = np.array([3, 1, 1, 2, 0, 3, -1, 4], dtype=np.intp)
+        codes = [0, 1, 1, 2, 3, 0, -1, 4]
+        result, result_codes = safe_sort(values, codes, verify=verify)
+        expected_codes = np.array([3, 1, 1, 2, 0, 3, -1, 4], dtype=np.intp)
         tm.assert_numpy_array_equal(result, expected)
-        tm.assert_numpy_array_equal(result_labels, expected_labels)
+        tm.assert_numpy_array_equal(result_codes, expected_codes)
 
         # na_sentinel
-        labels = [0, 1, 1, 2, 3, 0, 99, 4]
-        result, result_labels = safe_sort(values, labels, na_sentinel=99, verify=verify)
-        expected_labels = np.array([3, 1, 1, 2, 0, 3, 99, 4], dtype=np.intp)
+        codes = [0, 1, 1, 2, 3, 0, 99, 4]
+        result, result_codes = safe_sort(values, codes, na_sentinel=99, verify=verify)
+        expected_codes = np.array([3, 1, 1, 2, 0, 3, 99, 4], dtype=np.intp)
         tm.assert_numpy_array_equal(result, expected)
-        tm.assert_numpy_array_equal(result_labels, expected_labels)
+        tm.assert_numpy_array_equal(result_codes, expected_codes)
 
-        labels = []
-        result, result_labels = safe_sort(values, labels, verify=verify)
-        expected_labels = np.array([], dtype=np.intp)
+        codes = []
+        result, result_codes = safe_sort(values, codes, verify=verify)
+        expected_codes = np.array([], dtype=np.intp)
         tm.assert_numpy_array_equal(result, expected)
-        tm.assert_numpy_array_equal(result_labels, expected_labels)
+        tm.assert_numpy_array_equal(result_codes, expected_codes)
 
     @pytest.mark.parametrize("na_sentinel", [-1, 99])
-    def test_labels_out_of_bound(self, na_sentinel):
+    def test_codes_out_of_bound(self, na_sentinel):
         values = [3, 1, 2, 0, 4]
         expected = np.array([0, 1, 2, 3, 4])
 
         # out of bound indices
-        labels = [0, 101, 102, 2, 3, 0, 99, 4]
-        result, result_labels = safe_sort(values, labels, na_sentinel=na_sentinel)
-        expected_labels = np.array(
+        codes = [0, 101, 102, 2, 3, 0, 99, 4]
+        result, result_codes = safe_sort(values, codes, na_sentinel=na_sentinel)
+        expected_codes = np.array(
             [3, na_sentinel, na_sentinel, 2, 0, 3, na_sentinel, 4], dtype=np.intp
         )
         tm.assert_numpy_array_equal(result, expected)
-        tm.assert_numpy_array_equal(result_labels, expected_labels)
+        tm.assert_numpy_array_equal(result_codes, expected_codes)
 
     def test_mixed_integer(self):
         values = np.array(["b", 1, 0, "a", 0, "b"], dtype=object)
@@ -399,12 +399,12 @@ def test_mixed_integer(self):
         tm.assert_numpy_array_equal(result, expected)
 
         values = np.array(["b", 1, 0, "a"], dtype=object)
-        labels = [0, 1, 2, 3, 0, -1, 1]
-        result, result_labels = safe_sort(values, labels)
+        codes = [0, 1, 2, 3, 0, -1, 1]
+        result, result_codes = safe_sort(values, codes)
         expected = np.array([0, 1, "a", "b"], dtype=object)
-        expected_labels = np.array([3, 1, 0, 2, 3, -1, 1], dtype=np.intp)
+        expected_codes = np.array([3, 1, 0, 2, 3, -1, 1], dtype=np.intp)
         tm.assert_numpy_array_equal(result, expected)
-        tm.assert_numpy_array_equal(result_labels, expected_labels)
+        tm.assert_numpy_array_equal(result_codes, expected_codes)
 
     def test_mixed_integer_from_list(self):
         values = ["b", 1, 0, "a", 0, "b"]
@@ -428,10 +428,10 @@ def test_exceptions(self):
             safe_sort(values=1)
 
         with pytest.raises(TypeError, match="Only list-like objects or None"):
-            safe_sort(values=[0, 1, 2], labels=1)
+            safe_sort(values=[0, 1, 2], codes=1)
 
         with pytest.raises(ValueError, match="values should be unique"):
-            safe_sort(values=[0, 1, 2, 1], labels=[0, 1])
+            safe_sort(values=[0, 1, 2, 1], codes=[0, 1])
 
     def test_extension_array(self):
         # a = array([1, 3, np.nan, 2], dtype='Int64')
@@ -443,12 +443,12 @@ def test_extension_array(self):
 
     @pytest.mark.parametrize("verify", [True, False])
     @pytest.mark.parametrize("na_sentinel", [-1, 99])
-    def test_extension_array_labels(self, verify, na_sentinel):
+    def test_extension_array_codes(self, verify, na_sentinel):
         a = array([1, 3, 2], dtype="Int64")
-        result, labels = safe_sort(
+        result, codes = safe_sort(
             a, [0, 1, na_sentinel, 2], na_sentinel=na_sentinel, verify=verify
         )
         expected_values = array([1, 2, 3], dtype="Int64")
-        expected_labels = np.array([0, 2, na_sentinel, 1], dtype=np.intp)
+        expected_codes = np.array([0, 2, na_sentinel, 1], dtype=np.intp)
         tm.assert_extension_array_equal(result, expected_values)
-        tm.assert_numpy_array_equal(labels, expected_labels)
+        tm.assert_numpy_array_equal(codes, expected_codes)