From f7945a311bb339045af9736216c5d42d77ecdf91 Mon Sep 17 00:00:00 2001
From: Deepak Cherian <deepak@cherian.net>
Date: Fri, 21 Jun 2024 14:38:03 -0600
Subject: [PATCH] cleanup

---
 xarray/core/dataset.py | 25 ++++++++++---------------
 1 file changed, 10 insertions(+), 15 deletions(-)

diff --git a/xarray/core/dataset.py b/xarray/core/dataset.py
index 0e16d723eb4..34acac1514f 100644
--- a/xarray/core/dataset.py
+++ b/xarray/core/dataset.py
@@ -2986,14 +2986,6 @@ def isel(
         all_keys = set(indexers.keys())
 
         for name, var in self._variables.items():
-            # preserve variable order
-            if name in index_variables:
-                var = index_variables[name]
-                dims.update(zip(var.dims, var.shape))
-            # Fastpath, skip all this metadata analysis for variables
-            # with no dimensions
-            # Keep the result of var.dims cached for future accesss to it
-            #
             # Optimization Note from hmaarrfk - 2024/06
             #     https://github.com/pydata/xarray/pull/9003#discussion_r1592767493
             # It was found that accessing var.dims is faster than
@@ -3002,21 +2994,24 @@ def isel(
             # By using var.dims, we can avoid the cost of resolving the
             # underlying array's shape and ndim since the dims are already
             # cached by the Variable
-            elif len(var_dims := var.dims):
-                # Large datasets with alot of metadata may have many scalars
-                # without any relevant dimensions for slicing.
-                # Pick those out quickly and avoid paying the cost below
-                # of resolving the var_indexers variables
-                if var_indexer_keys := all_keys.intersection(var_dims):
+            var_dims = var.dims
+            # Fastpath, skip all this metadata analysis for variables
+            # with no dimensions
+            if var_dims:
+                # preserve variable order
+                if name in index_variables:
+                    var = index_variables[name]
+                elif var_indexer_keys := all_keys.intersection(var_dims):
                     var_indexers = {k: indexers[k] for k in var_indexer_keys}
                     var = var.isel(var_indexers)
                     if drop and var.ndim == 0 and name in coord_names:
                         coord_names.remove(name)
                         continue
                     # Update our reference to `var_dims` after the call to isel
+                    # to reflect any dropped dimensions
                     var_dims = var.dims
-                dims.update(zip(var_dims, var.shape))
             variables[name] = var
+            dims.update(zip(var_dims, var.shape))
 
         return self._construct_direct(
             variables=variables,