diff --git a/.gitignore b/.gitignore
index 12e933d2b..b32bb6a90 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,3 +10,4 @@ bench/shakespeare.txt
 .idea/
 .ipynb_checkpoints/
 coverage.xml
+venv/
diff --git a/dask_expr/_collection.py b/dask_expr/_collection.py
index 326f1f496..9230b2903 100644
--- a/dask_expr/_collection.py
+++ b/dask_expr/_collection.py
@@ -3654,13 +3654,39 @@ def query(self, expr, **kwargs):
         return new_collection(Query(self, expr, kwargs))
 
     @derived_from(pd.DataFrame)
-    def mode(self, dropna=True, split_every=False, numeric_only=False):
-        modes = []
-        for _, col in self.items():
-            if numeric_only and not pd.api.types.is_numeric_dtype(col.dtype):
-                continue
-            modes.append(col.mode(dropna=dropna, split_every=split_every))
-        return concat(modes, axis=1)
+    # GH#11389 - Dask issue related to adding row-wise mode functionality
+    # GH#1136 - Dask-Expr specific implementation for row-wise mode functionality
+    # Contributor: @thyripian
+    def mode(self, axis=0, numeric_only=False, dropna=True, split_every=False):
+        if axis == 0:
+            # Existing logic for axis=0 (column-wise mode)
+            modes = []
+            for _, col in self.items():
+                if numeric_only and not pd.api.types.is_numeric_dtype(col.dtype):
+                    continue
+                modes.append(col.mode(dropna=dropna, split_every=split_every))
+            return concat(modes, axis=1)
+        elif axis == 1:
+            # Use self._meta_nonempty to generate meta
+            meta = self._meta_nonempty.mode(
+                axis=1, numeric_only=numeric_only, dropna=dropna
+            )
+
+            # Determine the maximum number of modes any row can have
+            max_modes = len(self.columns)
+
+            # Reindex meta to have the maximum number of columns
+            meta = meta.reindex(columns=range(max_modes))
+
+            # Apply map_partitions using pandas' mode function directly
+            return self.map_partitions(
+                lambda df: df.mode(
+                    axis=1, numeric_only=numeric_only, dropna=dropna
+                ).reindex(columns=range(max_modes)),
+                meta=meta,
+            )
+        else:
+            raise ValueError(f"No axis named {axis} for object type {type(self)}")
 
     @derived_from(pd.DataFrame)
     def add_prefix(self, prefix):
diff --git a/dask_expr/_expr.py b/dask_expr/_expr.py
index aa807eabd..ab2b8b74c 100644
--- a/dask_expr/_expr.py
+++ b/dask_expr/_expr.py
@@ -1269,9 +1269,11 @@ def _simplify_up(self, parent, dependents):
             columns = _convert_to_list(columns)
             frame_columns = set(self.frame.columns)
             columns = [
-                reverse_mapping[col]
-                if col in reverse_mapping and reverse_mapping[col] in frame_columns
-                else col
+                (
+                    reverse_mapping[col]
+                    if col in reverse_mapping and reverse_mapping[col] in frame_columns
+                    else col
+                )
                 for col in columns
             ]
             columns = [col for col in self.frame.columns if col in columns]
@@ -2342,9 +2344,11 @@ class AddPrefix(Elemwise):
     @functools.cached_property
     def unique_partition_mapping_columns_from_shuffle(self):
         return {
-            f"{self.prefix}{c}"
-            if not isinstance(c, tuple)
-            else tuple(self.prefix + t for t in c)
+            (
+                f"{self.prefix}{c}"
+                if not isinstance(c, tuple)
+                else tuple(self.prefix + t for t in c)
+            )
             for c in self.frame.unique_partition_mapping_columns_from_shuffle
         }
 
@@ -2373,9 +2377,11 @@ class AddSuffix(AddPrefix):
     @functools.cached_property
     def unique_partition_mapping_columns_from_shuffle(self):
         return {
-            f"{c}{self.suffix}"
-            if not isinstance(c, tuple)
-            else tuple(t + self.suffix for t in c)
+            (
+                f"{c}{self.suffix}"
+                if not isinstance(c, tuple)
+                else tuple(t + self.suffix for t in c)
+            )
             for c in self.frame.unique_partition_mapping_columns_from_shuffle
         }
 
@@ -2421,9 +2427,11 @@ def _task(self, index: int):
     def _simplify_down(self):
         if isinstance(self.frame, Elemwise):
             operands = [
-                Head(op, self.n, self.operand("npartitions"))
-                if isinstance(op, Expr) and not isinstance(op, _DelayedExpr)
-                else op
+                (
+                    Head(op, self.n, self.operand("npartitions"))
+                    if isinstance(op, Expr) and not isinstance(op, _DelayedExpr)
+                    else op
+                )
                 for op in self.frame.operands
             ]
             return type(self.frame)(*operands)