Remove unnecessary lambda expression

monai/data/utils.py

@@ -1093,7 +1093,7 @@ def convert_tables_to_dicts(
         kwargs: additional arguments for `pandas.merge()` API to join tables.
 
     """
-    df = reduce(lambda l, r: pd.merge(l, r, **kwargs), ensure_tuple(dfs))
+    df = reduce(pd.merge, ensure_tuple(dfs))
     # parse row indices
     rows: List[Union[int, str]] = []
     if row_indices is None:

monai/handlers/utils.py

@@ -130,12 +130,12 @@ class    mean    median    max    5percentile 95percentile  notnans
             if summary_ops is not None:
                 supported_ops = OrderedDict(
                     {
-                        "mean": lambda x: np.nanmean(x),
-                        "median": lambda x: np.nanmedian(x),
-                        "max": lambda x: np.nanmax(x),
-                        "min": lambda x: np.nanmin(x),
+                        "mean": np.nanmean,
+                        "median": np.nanmedian,
+                        "max": np.nanmax,
+                        "min": np.nanmin,
                         "90percentile": lambda x: np.nanpercentile(x[0], x[1]),
-                        "std": lambda x: np.nanstd(x),
+                        "std": np.nanstd,
                         "notnans": lambda x: (~np.isnan(x)).sum(),
                     }
                 )

monai/transforms/intensity/array.py

@@ -1457,7 +1457,7 @@ def __call__(self, img: Union[np.ndarray, torch.Tensor]) -> Union[torch.Tensor,
             raise RuntimeError("Image needs a channel direction.")
         if isinstance(self.loc[0], int) and len(img.shape) == 4 and len(self.loc) == 2:
             raise RuntimeError("Input images of dimension 4 need location tuple to be length 3 or 4")
-        if isinstance(self.loc[0], Sequence) and len(img.shape) == 4 and min(map(lambda x: len(x), self.loc)) == 2:
+        if isinstance(self.loc[0], Sequence) and len(img.shape) == 4 and min(map(len, self.loc)) == 2:
             raise RuntimeError("Input images of dimension 4 need location tuple to be length 3 or 4")
 
         n_dims = len(img.shape[1:])

monai/transforms/utility/array.py

@@ -1091,11 +1091,11 @@ def __call__(
             img_ = img[mask]
 
         supported_ops = {
-            "mean": lambda x: np.nanmean(x),
-            "median": lambda x: np.nanmedian(x),
-            "max": lambda x: np.nanmax(x),
-            "min": lambda x: np.nanmin(x),
-            "std": lambda x: np.nanstd(x),
+            "mean": np.nanmean,
+            "median": np.nanmedian,
+            "max": np.nanmax,
+            "min": np.nanmin,
+            "std": np.nanstd,
         }
 
         def _compute(op: Callable, data: np.ndarray):

tests/test_intensity_stats.py

@@ -31,7 +31,7 @@
 ]
 
 TEST_CASE_3 = [
-    {"ops": [lambda x: np.mean(x), "max", lambda x: np.min(x)], "key_prefix": "orig"},
+    {"ops": [np.mean, "max", np.min], "key_prefix": "orig"},
     np.array([[[0.0, 1.0], [2.0, 3.0]]]),
     None,
     {"orig_custom_0": 1.5, "orig_max": 3.0, "orig_custom_1": 0.0},

tests/test_intensity_statsd.py

@@ -34,7 +34,7 @@
 ]
 
 TEST_CASE_3 = [
-    {"keys": "img", "ops": [lambda x: np.mean(x), "max", lambda x: np.min(x)], "key_prefix": "orig"},
+    {"keys": "img", "ops": [np.mean, "max", np.min], "key_prefix": "orig"},
     {"img": np.array([[[0.0, 1.0], [2.0, 3.0]]])},
     "img_meta_dict",
     {"orig_custom_0": 1.5, "orig_max": 3.0, "orig_custom_1": 0.0},