diff --git a/analyze_acv.py b/analyze_acv.py
index e75909f..d598594 100644
--- a/analyze_acv.py
+++ b/analyze_acv.py
@@ -14,57 +14,63 @@ def refine_content(df):
         df[col] = df[col].apply(lambda x: "其他" if strip_character(x, strip_character_list) == "" else strip_character(x, strip_character_list))
     return df
 
+def calc_acv_mean(df, acv_name, group_by_column):
+    df_grouped_mean = df.groupby(group_by_column)[acv_name].mean().fillna(0).astype(int).reset_index()
+    df_grouped_mean = df_grouped_mean.sort_values(by=acv_name, ascending=False)
+    df_grouped_mean[acv_name] = df_grouped_mean[acv_name].apply(lambda x: '{:,}'.format(x))
+    return df_grouped_mean
+
+def calc_acv_sum(df, acv_name, group_by_column):
+    df_grouped_sum = df.groupby(group_by_column)[acv_name].sum().astype(int).reset_index()
+    df_grouped_sum = df_grouped_sum.sort_values(by=acv_name, ascending=False)
+    df_grouped_sum[acv_name] = df_grouped_sum[acv_name].apply(lambda x: '{:,}'.format(x))
+    return df_grouped_sum
+
+
 # 读取赢单Excel文件
 df_win = pd.read_excel('./data_src/pingcap_won.xlsx')
 acv_name = 'ACV'
 
 # ACV by 客户分类
-df_win_grouped_by_industry = df_win.groupby('客户分类')[acv_name].sum().astype(int).reset_index()
-df_win_grouped_by_industry[acv_name] = df_win_grouped_by_industry[acv_name].apply(lambda x: '{:,}'.format(x))
 print("------ACV by 行业------")
-print(refine_content(df_win_grouped_by_industry))
+df_win_grouped_by_industry_sum = calc_acv_sum(df_win, acv_name, '客户分类')
+print(refine_content(df_win_grouped_by_industry_sum))
 
 # Group by customer industry and calculate the average ACV for each group
-won_average_acv_by_industry = df_win.groupby('客户分类')[acv_name].mean().astype(int).reset_index()
-won_average_acv_by_industry[acv_name] = won_average_acv_by_industry[acv_name].apply(lambda x: '{:,}'.format(x))
 print("------平均ACV by 行业------")
-print(refine_content(won_average_acv_by_industry))
+df_win_grouped_by_industry_mean = calc_acv_mean(df_win, acv_name, '客户分类')
+print(refine_content(df_win_grouped_by_industry_mean))
 
-
-
-df_win_grouped_by_sub_industry = df_win.groupby('客户行业')[acv_name].sum().astype(int).reset_index()
-df_win_grouped_by_sub_industry[acv_name] = df_win_grouped_by_sub_industry[acv_name].apply(lambda x: '{:,}'.format(x))
 print("------ACV by 子行业------")
-print(refine_content(df_win_grouped_by_sub_industry))
+df_win_grouped_by_sub_industry_sum = calc_acv_sum(df_win, acv_name, '客户行业')
+print(refine_content(df_win_grouped_by_sub_industry_sum))
 
-won_average_acv_by_sub_industry = df_win.groupby('客户行业')[acv_name].mean().astype(int).reset_index()
-won_average_acv_by_sub_industry[acv_name] = won_average_acv_by_sub_industry[acv_name].apply(lambda x: '{:,}'.format(x))
 print("------平均ACV by 子行业------")
-print(refine_content(won_average_acv_by_sub_industry))  
+df_win_grouped_by_sub_industry_mean = calc_acv_mean(df_win, acv_name, '客户行业')
+print(refine_content(df_win_grouped_by_sub_industry_mean))  
 
 # 读取Excel文件
 df = pd.read_excel('./data_src/pingcap_pipeline.xlsx')
 
 # 按照"客户分类"列分组，并计算ACV列的和
 acv_name = '预估 ACV'
-df_pipeline_grouped_by_industry = df.groupby('负责人所属行业')[acv_name].sum().astype(int).reset_index()
-df_pipeline_grouped_by_industry = df_pipeline_grouped_by_industry.sort_values(by=acv_name, ascending=False)
-df_pipeline_grouped_by_industry[acv_name] = df_pipeline_grouped_by_industry[acv_name].apply(lambda x: '{:,}'.format(x))
+
 print("------预估ACV by 行业------")
-print(refine_content(df_pipeline_grouped_by_industry))
+df_pipeline_grouped_by_industry_sum = calc_acv_sum(df, acv_name, '负责人所属行业')
+print(refine_content(df_pipeline_grouped_by_industry_sum))
+
 
-pipeline_average_acv_by_industry = df.groupby('负责人所属行业')[acv_name].mean().astype(int).reset_index()
-pipeline_average_acv_by_industry[acv_name] = pipeline_average_acv_by_industry[acv_name].apply(lambda x: '{:,}'.format(x))
 print("------平均预估ACV by 行业------")
-print(refine_content(pipeline_average_acv_by_industry))
+df_pipeline_grouped_by_industry_mean = calc_acv_mean(df, acv_name, '负责人所属行业')
+print(refine_content(df_pipeline_grouped_by_industry_mean))
+
 
-df_pipeline_grouped_by_sub_industry = df.groupby('客户行业')[acv_name].sum().astype(int).reset_index()
-df_pipeline_grouped_by_sub_industry[acv_name] = df_pipeline_grouped_by_sub_industry[acv_name].apply(lambda x: '{:,}'.format(x))
 print("------预估ACV by 子行业------")
-print(refine_content(df_pipeline_grouped_by_sub_industry))
+df_pipeline_grouped_by_sub_industry_sum = calc_acv_sum(df, acv_name, '客户行业')
+print(refine_content(df_pipeline_grouped_by_sub_industry_sum))
+
 
 
-pipeline_average_acv_by_sub_industry = df.groupby('客户行业')[acv_name].mean().fillna(0).astype(int).reset_index()
-pipeline_average_acv_by_sub_industry[acv_name] = pipeline_average_acv_by_sub_industry[acv_name].apply(lambda x: '{:,}'.format(x))
 print("------平均预估ACV by 子行业------")
-print(refine_content(pipeline_average_acv_by_sub_industry))
\ No newline at end of file
+df_pipeline_grouped_by_sub_industry_mean = calc_acv_mean(df, acv_name, '客户行业')
+print(refine_content(df_pipeline_grouped_by_sub_industry_mean))
\ No newline at end of file