CLN: Replace fstring in tests/groupby/*.py files

pandas/tests/groupby/aggregate/test_other.py

@@ -473,8 +473,7 @@ def test_agg_timezone_round_trip():
     assert result3 == ts
 
     dates = [
-        pd.Timestamp("2016-01-0{i:d} 12:00:00".format(i=i), tz="US/Pacific")
-        for i in range(1, 5)
+        pd.Timestamp(f"2016-01-0{i:d} 12:00:00", tz="US/Pacific") for i in range(1, 5)
     ]
     df = pd.DataFrame({"A": ["a", "b"] * 2, "B": dates})
     grouped = df.groupby("A")

pandas/tests/groupby/test_apply.py

@@ -265,7 +265,7 @@ def desc3(group):
         result = group.describe()
 
         # names are different
-        result.index.name = "stat_{:d}".format(len(group))
+        result.index.name = f"stat_{len(group):d}"
 
         result = result[: len(group)]
         # weirdo

pandas/tests/groupby/test_bin_groupby.py

@@ -87,7 +87,7 @@ def _check(dtype):
         counts = np.zeros(len(out), dtype=np.int64)
         labels = ensure_int64(np.repeat(np.arange(3), np.diff(np.r_[0, bins])))
 
-        func = getattr(groupby, "group_ohlc_{dtype}".format(dtype=dtype))
+        func = getattr(groupby, f"group_ohlc_{dtype}")
         func(out, counts, obj[:, None], labels)
 
         def _ohlc(group):

pandas/tests/groupby/test_categorical.py

@@ -497,10 +497,10 @@ def test_dataframe_categorical_ordered_observed_sort(ordered, observed, sort):
         aggr[aggr.isna()] = "missing"
     if not all(label == aggr):
         msg = (
-            "Labels and aggregation results not consistently sorted\n"
-            + "for (ordered={}, observed={}, sort={})\n"
-            + "Result:\n{}"
-        ).format(ordered, observed, sort, result)
+            f"Labels and aggregation results not consistently sorted\n"
+            + "for (ordered={ordered}, observed={observed}, sort={sort})\n"
+            + "Result:\n{result}"
+        )
         assert False, msg
 
 
@@ -805,7 +805,7 @@ def test_sort():
     # self.cat.groupby(['value_group'])['value_group'].count().plot(kind='bar')
 
     df = DataFrame({"value": np.random.randint(0, 10000, 100)})
-    labels = ["{0} - {1}".format(i, i + 499) for i in range(0, 10000, 500)]
+    labels = [f"{i} - {i+499}" for i in range(0, 10000, 500)]
     cat_labels = Categorical(labels, labels)
 
     df = df.sort_values(by=["value"], ascending=True)

pandas/tests/groupby/test_counting.py

@@ -197,11 +197,8 @@ def test_ngroup_respects_groupby_order(self):
     @pytest.mark.parametrize(
         "datetimelike",
         [
-            [
-                Timestamp("2016-05-{i:02d} 20:09:25+00:00".format(i=i))
-                for i in range(1, 4)
-            ],
-            [Timestamp("2016-05-{i:02d} 20:09:25".format(i=i)) for i in range(1, 4)],
+            [Timestamp(f"2016-05-{i:02d} 20:09:25+00:00") for i in range(1, 4)],
+            [Timestamp(f"2016-05-{i:02d} 20:09:25") for i in range(1, 4)],
             [Timedelta(x, unit="h") for x in range(1, 4)],
             [Period(freq="2W", year=2017, month=x) for x in range(1, 4)],
         ],

pandas/tests/groupby/test_function.py

@@ -103,9 +103,7 @@ def test_builtins_apply(keys, f):
     result = df.groupby(keys).apply(f)
     ngroups = len(df.drop_duplicates(subset=keys))
 
-    assert_msg = "invalid frame shape: {} (expected ({}, 3))".format(
-        result.shape, ngroups
-    )
+    assert_msg = f"invalid frame shape: {result.shape} (expected ({ngroups}, 3))"
     assert result.shape == (ngroups, 3), assert_msg
 
     tm.assert_frame_equal(

pandas/tests/groupby/test_groupby.py

@@ -935,7 +935,7 @@ def test_mutate_groups():
             + ["c"] * 2
             + ["d"] * 2
             + ["e"] * 2,
-            "cat3": ["g{}".format(x) for x in range(1, 15)],
+            "cat3": [f"g{x}" for x in range(1, 15)],
             "val": np.random.randint(100, size=14),
         }
     )

pandas/tests/groupby/test_transform.py

@@ -962,9 +962,7 @@ def demean_rename(x):
         if isinstance(x, pd.Series):
             return result
 
-        result = result.rename(
-            columns={c: "{}_demeaned".format(c) for c in result.columns}
-        )
+        result = result.rename(columns={c: "{c}_demeaned" for c in result.columns})
 
         return result
 

pandas/tests/groupby/test_value_counts.py

@@ -47,7 +47,7 @@ def seed_df(seed_nans, n, m):
         keys = "1st", "2nd", ["1st", "2nd"]
         for k, b in product(keys, bins):
             binned.append((df, k, b, n, m))
-            ids.append("{}-{}-{}".format(k, n, m))
+            ids.append(f"{k}-{n}-{m}")
 
 
 @pytest.mark.slow

pandas/tests/groupby/test_whitelist.py

@@ -404,7 +404,7 @@ def test_all_methods_categorized(mframe):
 
     # new public method?
     if new_names:
-        msg = """
+        msg = f"""
 There are uncatgeorized methods defined on the Grouper class:
 {names}.
 
@@ -418,19 +418,19 @@ def test_all_methods_categorized(mframe):
 see the comments in pandas/core/groupby/base.py for guidance on
 how to fix this test.
         """
-        raise AssertionError(msg.format(names=names))
+        raise AssertionError(msg)
 
     # removed a public method?
     all_categorized = reduction_kernels | transformation_kernels | groupby_other_methods
     print(names)
     print(all_categorized)
     if not (names == all_categorized):
-        msg = """
+        msg = f"""
 Some methods which are supposed to be on the Grouper class
 are missing:
-{names}.
+{all_categorized - names}.
 
 They're still defined in one of the lists that live in pandas/core/groupby/base.py.
 If you removed a method, you should update them
 """
-        raise AssertionError(msg.format(names=all_categorized - names))
+        raise AssertionError(msg)