TST: avoid chained assignment in tests outside of specific tests on chaining (pandas-dev#49474)

* TST: avoid chained assignment in tests outside of specific tests on chaining

* update to use .loc[index[slice], ..] pattern

Author: jorisvandenbossche

pandas/tests/apply/test_frame_apply.py

@@ -347,7 +347,7 @@ def test_apply_yield_list(float_frame):
 
 
 def test_apply_reduce_Series(float_frame):
-    float_frame["A"].iloc[::2] = np.nan
+    float_frame.iloc[::2, float_frame.columns.get_loc("A")] = np.nan
     expected = float_frame.mean(1)
     result = float_frame.apply(np.mean, axis=1)
     tm.assert_series_equal(result, expected)

pandas/tests/frame/indexing/test_setitem.py

@@ -1132,7 +1132,7 @@ def test_setitem_always_copy(self, float_frame):
         s = float_frame["A"].copy()
         float_frame["E"] = s
 
-        float_frame["E"][5:10] = np.nan
+        float_frame.iloc[5:10, float_frame.columns.get_loc("E")] = np.nan
         assert notna(s[5:10]).all()
 
     @pytest.mark.parametrize("consolidate", [True, False])

pandas/tests/frame/methods/test_cov_corr.py

@@ -107,9 +107,9 @@ class TestDataFrameCorr:
     @pytest.mark.parametrize("method", ["pearson", "kendall", "spearman"])
     @td.skip_if_no_scipy
     def test_corr_scipy_method(self, float_frame, method):
-        float_frame["A"][:5] = np.nan
-        float_frame["B"][5:10] = np.nan
-        float_frame["A"][:10] = float_frame["A"][10:20]
+        float_frame.loc[float_frame.index[:5], "A"] = np.nan
+        float_frame.loc[float_frame.index[5:10], "B"] = np.nan
+        float_frame.loc[float_frame.index[:10], "A"] = float_frame["A"][10:20]
 
         correls = float_frame.corr(method=method)
         expected = float_frame["A"].corr(float_frame["C"], method=method)

pandas/tests/frame/methods/test_fillna.py

@@ -392,16 +392,16 @@ def test_fillna_datetime_columns(self):
         tm.assert_frame_equal(result, expected)
 
     def test_ffill(self, datetime_frame):
-        datetime_frame["A"][:5] = np.nan
-        datetime_frame["A"][-5:] = np.nan
+        datetime_frame.loc[datetime_frame.index[:5], "A"] = np.nan
+        datetime_frame.loc[datetime_frame.index[-5:], "A"] = np.nan
 
         tm.assert_frame_equal(
             datetime_frame.ffill(), datetime_frame.fillna(method="ffill")
         )
 
     def test_bfill(self, datetime_frame):
-        datetime_frame["A"][:5] = np.nan
-        datetime_frame["A"][-5:] = np.nan
+        datetime_frame.loc[datetime_frame.index[:5], "A"] = np.nan
+        datetime_frame.loc[datetime_frame.index[-5:], "A"] = np.nan
 
         tm.assert_frame_equal(
             datetime_frame.bfill(), datetime_frame.fillna(method="bfill")
@@ -467,8 +467,8 @@ def test_fillna_integer_limit(self, type):
 
     def test_fillna_inplace(self):
         df = DataFrame(np.random.randn(10, 4))
-        df[1][:4] = np.nan
-        df[3][-4:] = np.nan
+        df.loc[:4, 1] = np.nan
+        df.loc[-4:, 3] = np.nan
 
         expected = df.fillna(value=0)
         assert expected is not df
@@ -479,8 +479,8 @@ def test_fillna_inplace(self):
         expected = df.fillna(value={0: 0}, inplace=True)
         assert expected is None
 
-        df[1][:4] = np.nan
-        df[3][-4:] = np.nan
+        df.loc[:4, 1] = np.nan
+        df.loc[-4:, 3] = np.nan
         expected = df.fillna(method="ffill")
         assert expected is not df
 

pandas/tests/frame/methods/test_rank.py

@@ -43,10 +43,10 @@ def test_rank(self, float_frame):
         import scipy.stats  # noqa:F401
         from scipy.stats import rankdata
 
-        float_frame["A"][::2] = np.nan
-        float_frame["B"][::3] = np.nan
-        float_frame["C"][::4] = np.nan
-        float_frame["D"][::5] = np.nan
+        float_frame.loc[::2, "A"] = np.nan
+        float_frame.loc[::3, "B"] = np.nan
+        float_frame.loc[::4, "C"] = np.nan
+        float_frame.loc[::5, "D"] = np.nan
 
         ranks0 = float_frame.rank()
         ranks1 = float_frame.rank(1)
@@ -148,10 +148,10 @@ def test_rank_na_option(self, float_frame):
         import scipy.stats  # noqa:F401
         from scipy.stats import rankdata
 
-        float_frame["A"][::2] = np.nan
-        float_frame["B"][::3] = np.nan
-        float_frame["C"][::4] = np.nan
-        float_frame["D"][::5] = np.nan
+        float_frame.loc[::2, "A"] = np.nan
+        float_frame.loc[::3, "B"] = np.nan
+        float_frame.loc[::4, "C"] = np.nan
+        float_frame.loc[::5, "D"] = np.nan
 
         # bottom
         ranks0 = float_frame.rank(na_option="bottom")

pandas/tests/frame/methods/test_replace.py

@@ -29,8 +29,8 @@ def mix_abc() -> dict[str, list[float | str]]:
 
 class TestDataFrameReplace:
     def test_replace_inplace(self, datetime_frame, float_string_frame):
-        datetime_frame["A"][:5] = np.nan
-        datetime_frame["A"][-5:] = np.nan
+        datetime_frame.loc[datetime_frame.index[:5], "A"] = np.nan
+        datetime_frame.loc[datetime_frame.index[-5:], "A"] = np.nan
 
         tsframe = datetime_frame.copy()
         return_value = tsframe.replace(np.nan, 0, inplace=True)
@@ -420,16 +420,16 @@ def test_regex_replace_string_types(
         tm.assert_equal(result, expected)
 
     def test_replace(self, datetime_frame):
-        datetime_frame["A"][:5] = np.nan
-        datetime_frame["A"][-5:] = np.nan
+        datetime_frame.loc[datetime_frame.index[:5], "A"] = np.nan
+        datetime_frame.loc[datetime_frame.index[-5:], "A"] = np.nan
 
         zero_filled = datetime_frame.replace(np.nan, -1e8)
         tm.assert_frame_equal(zero_filled, datetime_frame.fillna(-1e8))
         tm.assert_frame_equal(zero_filled.replace(-1e8, np.nan), datetime_frame)
 
-        datetime_frame["A"][:5] = np.nan
-        datetime_frame["A"][-5:] = np.nan
-        datetime_frame["B"][:5] = -1e8
+        datetime_frame.loc[datetime_frame.index[:5], "A"] = np.nan
+        datetime_frame.loc[datetime_frame.index[-5:], "A"] = np.nan
+        datetime_frame.loc[datetime_frame.index[:5], "B"] = -1e8
 
         # empty
         df = DataFrame(index=["a", "b"])
@@ -716,16 +716,16 @@ def test_replace_for_new_dtypes(self, datetime_frame):
 
         # dtypes
         tsframe = datetime_frame.copy().astype(np.float32)
-        tsframe["A"][:5] = np.nan
-        tsframe["A"][-5:] = np.nan
+        tsframe.loc[tsframe.index[:5], "A"] = np.nan
+        tsframe.loc[tsframe.index[-5:], "A"] = np.nan
 
         zero_filled = tsframe.replace(np.nan, -1e8)
         tm.assert_frame_equal(zero_filled, tsframe.fillna(-1e8))
         tm.assert_frame_equal(zero_filled.replace(-1e8, np.nan), tsframe)
 
-        tsframe["A"][:5] = np.nan
-        tsframe["A"][-5:] = np.nan
-        tsframe["B"][:5] = -1e8
+        tsframe.loc[tsframe.index[:5], "A"] = np.nan
+        tsframe.loc[tsframe.index[-5:], "A"] = np.nan
+        tsframe.loc[tsframe.index[:5], "B"] = -1e8
 
         b = tsframe["B"]
         b[b == -1e8] = np.nan

pandas/tests/frame/methods/test_to_csv.py

@@ -35,7 +35,7 @@ def read_csv(self, path, **kwargs):
     def test_to_csv_from_csv1(self, float_frame, datetime_frame):
 
         with tm.ensure_clean("__tmp_to_csv_from_csv1__") as path:
-            float_frame["A"][:5] = np.nan
+            float_frame.iloc[:5, float_frame.columns.get_loc("A")] = np.nan
 
             float_frame.to_csv(path)
             float_frame.to_csv(path, columns=["A", "B"])

pandas/tests/frame/test_constructors.py

@@ -718,7 +718,8 @@ def test_constructor_defaultdict(self, float_frame):
         from collections import defaultdict
 
         data = {}
-        float_frame["B"][:10] = np.nan
+        float_frame.loc[: float_frame.index[10], "B"] = np.nan
+
         for k, v in float_frame.items():
             dct = defaultdict(dict)
             dct.update(v.to_dict())
@@ -2203,7 +2204,9 @@ def test_constructor_series_copy(self, float_frame):
         series = float_frame._series
 
         df = DataFrame({"A": series["A"]}, copy=True)
-        df["A"][:] = 5
+        # TODO can be replaced with `df.loc[:, "A"] = 5` after deprecation about
+        # inplace mutation is enforced
+        df.loc[df.index[0] : df.index[-1], "A"] = 5
 
         assert not (series["A"] == 5).all()
 

pandas/tests/io/excel/test_writers.py

@@ -374,7 +374,7 @@ def test_excel_writer_context_manager(self, frame, path):
 
     def test_roundtrip(self, frame, path):
         frame = frame.copy()
-        frame["A"][:5] = np.nan
+        frame.iloc[:5, frame.columns.get_loc("A")] = np.nan
 
         frame.to_excel(path, "test1")
         frame.to_excel(path, "test1", columns=["A", "B"])
@@ -444,7 +444,7 @@ def test_ts_frame(self, tsframe, path):
 
     def test_basics_with_nan(self, frame, path):
         frame = frame.copy()
-        frame["A"][:5] = np.nan
+        frame.iloc[:5, frame.columns.get_loc("A")] = np.nan
         frame.to_excel(path, "test1")
         frame.to_excel(path, "test1", columns=["A", "B"])
         frame.to_excel(path, "test1", header=False)
@@ -508,7 +508,7 @@ def test_sheets(self, frame, tsframe, path):
         tsframe.index = index
 
         frame = frame.copy()
-        frame["A"][:5] = np.nan
+        frame.iloc[:5, frame.columns.get_loc("A")] = np.nan
 
         frame.to_excel(path, "test1")
         frame.to_excel(path, "test1", columns=["A", "B"])
@@ -530,7 +530,7 @@ def test_sheets(self, frame, tsframe, path):
 
     def test_colaliases(self, frame, path):
         frame = frame.copy()
-        frame["A"][:5] = np.nan
+        frame.iloc[:5, frame.columns.get_loc("A")] = np.nan
 
         frame.to_excel(path, "test1")
         frame.to_excel(path, "test1", columns=["A", "B"])
@@ -548,7 +548,7 @@ def test_colaliases(self, frame, path):
 
     def test_roundtrip_indexlabels(self, merge_cells, frame, path):
         frame = frame.copy()
-        frame["A"][:5] = np.nan
+        frame.iloc[:5, frame.columns.get_loc("A")] = np.nan
 
         frame.to_excel(path, "test1")
         frame.to_excel(path, "test1", columns=["A", "B"])

pandas/tests/series/methods/test_rank.py

@@ -44,7 +44,7 @@ def test_rank(self, datetime_series):
         from scipy.stats import rankdata
 
         datetime_series[::2] = np.nan
-        datetime_series[:10][::3] = 4.0
+        datetime_series[:10:3] = 4.0
 
         ranks = datetime_series.rank()
         oranks = datetime_series.astype("O").rank()