diff --git a/pakistan.py b/pakistan.py
index f2720fce6bdb14803f8a317c60347ad58f19d43d..2907e2d1ea135e451ae254d9ed8a79973bd2f7f3 100644
--- a/pakistan.py
+++ b/pakistan.py
@@ -1,51 +1,90 @@
 import numpy as np
 import csv
+from datetime import datetime
+
 
 def load_data(file_path):
     """
     Load repository data and calculate activity metrics
     Columns: repo_name,owner,stars,forks,language,created_at,last_commit,description
     Return: 2D NumPy array of shape (repos, 3) containing [stars, forks, active_days]
-    
+
     加载仓库数据并计算活跃天数
     列：仓库名称,所有者,星标,分支,语言,创建时间,最后提交,描述
     返回：形状为(仓库数, 3)的数组，包含[星标数, 分支数, 活跃天数]
     """
-    pass
+    data = []
+    try:
+        with open(file_path, 'r', encoding='utf-8') as file:
+            reader = csv.reader(file)
+            next(reader)  # 跳过标题行
+            for row in reader:
+                stars = int(row[2])
+                forks = int(row[3])
+                created_at = datetime.strptime(row[5], '%Y-%m-%d')
+                last_commit = datetime.strptime(row[6], '%Y-%m-%d')
+                active_days = (last_commit - created_at).days
+                data.append([stars, forks, active_days])
+    except FileNotFoundError:
+        print(f"错误：未找到文件 {file_path}")
+        return np.array([])
+    except Exception as e:
+        print(f"发生未知错误：{e}")
+        return np.array([])
+    return np.array(data)
+
 
 def calculate_statistics(data):
     """
     Calculate repository metrics statistics
     Return: Dictionary containing {
         'means': [stars_mean, forks_mean, days_mean],
-        'medians': [stars_median, forks_median, days_median],
+      'medians': [stars_median, forks_median, days_median],
         'variances': [stars_var, forks_var, days_var],
         'stds': [stars_std, forks_std, days_std]
     }
-    
+
     计算仓库指标统计量
     返回：包含平均值、中位数、方差、标准差的字典
     """
-    pass
+    if data.size == 0:
+        return {
+            'means': [0, 0, 0],
+          'medians': [0, 0, 0],
+            'variances': [0, 0, 0],
+          'stds': [0, 0, 0]
+        }
+    means = np.mean(data, axis=0)
+    medians = np.median(data, axis=0)
+    variances = np.var(data, axis=0)
+    stds = np.std(data, axis=0)
+    return {
+        'means': means,
+      'medians': medians,
+        'variances': variances,
+      'stds': stds
+    }
+
 
 def print_results(stats):
     """
     Print formatted results with proper indentation
-    
+
     按严格格式打印结果，保持正确缩进
     """
     metrics = ['Stars', 'Forks', 'Active Days']
-    for metric, mean, med, var, std in zip(metrics, 
-                                         stats['means'],
-                                         stats['medians'],
-                                         stats['variances'],
-                                         stats['stds']):
+    for metric, mean, med, var, std in zip(metrics,
+                                           stats['means'],
+                                           stats['medians'],
+                                           stats['variances'],
+                                           stats['stds']):
         print(f"{metric}:")
         print(f"    Average: {mean:.1f}")
         print(f"    Median: {med:.1f}")
         print(f"    Variance: {var:.1f}")
         print(f"    Standard Deviation: {std:.1f}")
 
-repo_data = load_data('pakistan-repos.csv')
+
+repo_data = load_data('china-repos.csv')
 stats = calculate_statistics(repo_data)
 print_results(stats)