Deprecate Aliases as orient Argument in DataFrame.to_dict (pandas-dev#32516)

Author: elmonsomiat

doc/source/whatsnew/v1.1.0.rst

@@ -206,7 +206,7 @@ Deprecations
 - Lookups on a :class:`Series` with a single-item list containing a slice (e.g. ``ser[[slice(0, 4)]]``) are deprecated, will raise in a future version.  Either convert the list to tuple, or pass the slice directly instead (:issue:`31333`)
 - :meth:`DataFrame.mean` and :meth:`DataFrame.median` with ``numeric_only=None`` will include datetime64 and datetime64tz columns in a future version (:issue:`29941`)
 - Setting values with ``.loc`` using a positional slice is deprecated and will raise in a future version.  Use ``.loc`` with labels or ``.iloc`` with positions instead (:issue:`31840`)
--
+- :meth:`DataFrame.to_dict` has deprecated accepting short names for ``orient`` in future versions (:issue:`32515`)
 
 .. ---------------------------------------------------------------------------
 

pandas/core/frame.py

@@ -1401,11 +1401,45 @@ def to_dict(self, orient="dict", into=dict):
             )
         # GH16122
         into_c = com.standardize_mapping(into)
-        if orient.lower().startswith("d"):
+
+        orient = orient.lower()
+        # GH32515
+        if orient.startswith(("d", "l", "s", "r", "i")) and orient not in {
+            "dict",
+            "list",
+            "series",
+            "split",
+            "records",
+            "index",
+        }:
+            warnings.warn(
+                "Using short name for 'orient' is deprecated. Only the "
+                "options: ('dict', list, 'series', 'split', 'records', 'index') "
+                "will be used in a future version. Use one of the above "
+                "to silence this warning.",
+                FutureWarning,
+            )
+
+            if orient.startswith("d"):
+                orient = "dict"
+            elif orient.startswith("l"):
+                orient = "list"
+            elif orient.startswith("sp"):
+                orient = "split"
+            elif orient.startswith("s"):
+                orient = "series"
+            elif orient.startswith("r"):
+                orient = "records"
+            elif orient.startswith("i"):
+                orient = "index"
+
+        if orient == "dict":
             return into_c((k, v.to_dict(into)) for k, v in self.items())
-        elif orient.lower().startswith("l"):
+
+        elif orient == "list":
             return into_c((k, v.tolist()) for k, v in self.items())
-        elif orient.lower().startswith("sp"):
+
+        elif orient == "split":
             return into_c(
                 (
                     ("index", self.index.tolist()),
@@ -1419,9 +1453,11 @@ def to_dict(self, orient="dict", into=dict):
                     ),
                 )
             )
-        elif orient.lower().startswith("s"):
+
+        elif orient == "series":
             return into_c((k, com.maybe_box_datetimelike(v)) for k, v in self.items())
-        elif orient.lower().startswith("r"):
+
+        elif orient == "records":
             columns = self.columns.tolist()
             rows = (
                 dict(zip(columns, row))
@@ -1431,13 +1467,15 @@ def to_dict(self, orient="dict", into=dict):
                 into_c((k, com.maybe_box_datetimelike(v)) for k, v in row.items())
                 for row in rows
             ]
-        elif orient.lower().startswith("i"):
+
+        elif orient == "index":
             if not self.index.is_unique:
                 raise ValueError("DataFrame index must be unique for orient='index'.")
             return into_c(
                 (t[0], dict(zip(self.columns, t[1:])))
                 for t in self.itertuples(name=None)
             )
+
         else:
             raise ValueError(f"orient '{orient}' not understood")
 

pandas/tests/frame/methods/test_to_dict.py

@@ -70,8 +70,17 @@ def test_to_dict_invalid_orient(self):
         with pytest.raises(ValueError, match=msg):
             df.to_dict(orient="xinvalid")
 
+    @pytest.mark.parametrize("orient", ["d", "l", "r", "sp", "s", "i"])
+    def test_to_dict_short_orient_warns(self, orient):
+        # GH#32515
+        df = DataFrame({"A": [0, 1]})
+        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+            df.to_dict(orient=orient)
+
     @pytest.mark.parametrize("mapping", [dict, defaultdict(list), OrderedDict])
     def test_to_dict(self, mapping):
+        # orient= should only take the listed options
+        # see GH#32515
         test_data = {"A": {"1": 1, "2": 2}, "B": {"1": "1", "2": "2", "3": "3"}}
 
         # GH#16122
@@ -81,27 +90,27 @@ def test_to_dict(self, mapping):
             for k2, v2 in v.items():
                 assert v2 == recons_data[k][k2]
 
-        recons_data = DataFrame(test_data).to_dict("l", mapping)
+        recons_data = DataFrame(test_data).to_dict("list", mapping)
 
         for k, v in test_data.items():
             for k2, v2 in v.items():
                 assert v2 == recons_data[k][int(k2) - 1]
 
-        recons_data = DataFrame(test_data).to_dict("s", mapping)
+        recons_data = DataFrame(test_data).to_dict("series", mapping)
 
         for k, v in test_data.items():
             for k2, v2 in v.items():
                 assert v2 == recons_data[k][k2]
 
-        recons_data = DataFrame(test_data).to_dict("sp", mapping)
+        recons_data = DataFrame(test_data).to_dict("split", mapping)
         expected_split = {
             "columns": ["A", "B"],
             "index": ["1", "2", "3"],
             "data": [[1.0, "1"], [2.0, "2"], [np.nan, "3"]],
         }
         tm.assert_dict_equal(recons_data, expected_split)
 
-        recons_data = DataFrame(test_data).to_dict("r", mapping)
+        recons_data = DataFrame(test_data).to_dict("records", mapping)
         expected_records = [
             {"A": 1.0, "B": "1"},
             {"A": 2.0, "B": "2"},
@@ -113,15 +122,15 @@ def test_to_dict(self, mapping):
             tm.assert_dict_equal(l, r)
 
         # GH#10844
-        recons_data = DataFrame(test_data).to_dict("i")
+        recons_data = DataFrame(test_data).to_dict("index")
 
         for k, v in test_data.items():
             for k2, v2 in v.items():
                 assert v2 == recons_data[k2][k]
 
         df = DataFrame(test_data)
         df["duped"] = df[df.columns[0]]
-        recons_data = df.to_dict("i")
+        recons_data = df.to_dict("index")
         comp_data = test_data.copy()
         comp_data["duped"] = comp_data[df.columns[0]]
         for k, v in comp_data.items():