diff --git a/lerobot/scripts/merge.py b/lerobot/scripts/merge.py
index 6763ecf8..4e7ab3ca 100644
--- a/lerobot/scripts/merge.py
+++ b/lerobot/scripts/merge.py
@@ -2,6 +2,7 @@ import json
 import os
 import shutil
 import traceback
+import contextlib
 
 import numpy as np
 import pandas as pd
@@ -43,10 +44,8 @@ def load_jsonl(file_path):
             with open(file_path) as f:
                 for line in f:
                     if line.strip():
-                        try:
+                        with contextlib.suppress(json.JSONDecodeError):
                             data.append(json.loads(line))
-                        except json.JSONDecodeError:
-                            pass
         except Exception as e:
             print(f"Error loading {file_path} line by line: {e}")
     else:
@@ -54,10 +53,8 @@ def load_jsonl(file_path):
         with open(file_path) as f:
             for line in f:
                 if line.strip():
-                    try:
+                    with contextlib.suppress(json.JSONDecodeError):
                         data.append(json.loads(line))
-                    except json.JSONDecodeError:
-                        print(f"Warning: Could not parse line in {file_path}: {line[:100]}...")
 
     return data
 
@@ -97,7 +94,7 @@ def merge_stats(stats_list):
         common_features = common_features.intersection(set(stats.keys()))
 
     # Process features in the order they appear in the first stats file
-    for feature in stats_list[0].keys():
+    for feature in stats_list[0]:
         if feature not in common_features:
             continue
 
@@ -606,7 +603,7 @@ def copy_data_files(
                 for feature in ["observation.state", "action"]:
                     if feature in df.columns:
                         # 检查第一个非空值 (Check first non-null value)
-                        for idx, value in enumerate(df[feature]):
+                        for _idx, value in enumerate(df[feature]):
                             if value is not None and isinstance(value, (list, np.ndarray)):
                                 current_dim = len(value)
                                 if current_dim < max_dim:
@@ -704,7 +701,7 @@ def copy_data_files(
                             for feature in ["observation.state", "action"]:
                                 if feature in df.columns:
                                     # 检查第一个非空值 (Check first non-null value)
-                                    for idx, value in enumerate(df[feature]):
+                                    for _idx, value in enumerate(df[feature]):
                                         if value is not None and isinstance(value, (list, np.ndarray)):
                                             current_dim = len(value)
                                             if current_dim < max_dim:
@@ -997,7 +994,7 @@ def merge_datasets(
             folder_dim = max_dim  # 使用变量替代硬编码的18
 
             # Try to find a parquet file to determine dimensions
-            for root, dirs, files in os.walk(folder):
+            for root, _dirs, files in os.walk(folder):
                 for file in files:
                     if file.endswith(".parquet"):
                         try:
@@ -1141,7 +1138,7 @@ def merge_datasets(
         # Update merged stats with episode-specific stats if available
         if all_stats_data:
             # For each feature in the stats
-            for feature in merged_stats.keys():
+            for feature in merged_stats:
                 if feature in all_stats_data[0]:
                     # Recalculate statistics based on all episodes
                     values = [stat[feature] for stat in all_stats_data if feature in stat]
@@ -1262,16 +1259,14 @@ def merge_datasets(
     if "features" in info:
         # Find the maximum dimension across all folders
         actual_max_dim = max_dim  # 使用变量替代硬编码的18
-        for folder, dim in folder_dimensions.items():
+        for _folder, dim in folder_dimensions.items():
             actual_max_dim = max(actual_max_dim, dim)
 
         # Update observation.state and action dimensions
         for feature_name in ["observation.state", "action"]:
-            if feature_name in info["features"]:
-                # Update shape to the maximum dimension
-                if "shape" in info["features"][feature_name]:
-                    info["features"][feature_name]["shape"] = [actual_max_dim]
-                    print(f"Updated {feature_name} shape to {actual_max_dim}")
+            if feature_name in info["features"] and "shape" in info["features"][feature_name]:
+                info["features"][feature_name]["shape"] = [actual_max_dim]
+                print(f"Updated {feature_name} shape to {actual_max_dim}")
 
     # 更新视频总数 (Update total videos)
     info["total_videos"] = total_videos