暂存analyze_acv相关代码

ExcelHelper.py

@@ -59,22 +59,3 @@ class ExcelHelper:
         print(f"Extracted columns saved to {new_filename}")
 
 
-
-
-# # 示例数据
-# data = [
-#     [
-#         {'title': 'Title 1', 'content': 'Content 1'},
-#         {'title': 'Title 2', 'content': 'Content 2'},
-#         {'title': 'Title 3', 'content': 'Content 3'}
-#     ],
-#     [
-#         {'title': 'Title 4', 'content': 'Content 4'},
-#         {'title': 'Title 5', 'content': 'Content 5'},
-#         {'title': 'Title 6', 'content': 'Content 6'}
-#     ]
-# ]
-
-# # 创建 ExcelHelper 实例并生成 Excel 文件
-# excel_helper = ExcelHelper(data)
-# excel_helper.create_excel('output.xlsx')

analysis.py

@@ -1,15 +0,0 @@
-import pandas as pd
-
-# 读取Excel文件
-df = pd.read_excel('pingcap_pipeline.xlsx')
-
-# 按照"客户分类"列分组，并计算ACV列的和
-acv_name = '预估 ACV'
-grouped_df = df.groupby('负责人所属行业')[acv_name].sum().astype(int).reset_index()
-grouped_df = grouped_df.sort_values(by=acv_name, ascending=False)
-grouped_df[acv_name] = grouped_df[acv_name].apply(lambda x: '{:,}'.format(x))
-
-
-# 打印结果
-print(grouped_df)
-

analyze_acv.py

@@ -0,0 +1,86 @@
+import pandas as pd
+from typing import List
+def strip_character(column_name, characters: List[str]):
+
+    new_col_name = column_name
+    for character in characters:
+        new_col_name = new_col_name.replace(character, '')
+    new_col_name = new_col_name.strip()
+    return new_col_name
+
+def refine_content(df):
+    strip_character_list = [' ', '\n', ':', '：','其他']
+    for col in df.columns:
+        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[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[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------")
+df_win_grouped_by_industry = calc_acv_sum(df_win, acv_name, '客户分类')
+print(refine_content(df_win_grouped_by_industry))
+
+# 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------")
+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------")
+df_win_grouped_by_sub_industry = calc_acv_sum(df_win, acv_name, '客户行业')
+print(refine_content(df_win_grouped_by_sub_industry))
+
+# 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------")
+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)
+print("------预估ACV by 行业------")
+df_pipeline_grouped_by_industry_sum = calc_acv_sum(df, acv_name, '负责人所属行业')
+df_pipeline_grouped_by_industry_sum[acv_name] = df_pipeline_grouped_by_industry_sum[acv_name].apply(lambda x: '{:,}'.format(x))
+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_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))
+
+
+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))

analyze_acv_dist.py

@@ -0,0 +1,70 @@
+import pandas as pd
+from typing import List
+def strip_character(column_name, characters: List[str]):
+
+    new_col_name = column_name
+    for character in characters:
+        new_col_name = new_col_name.replace(character, '')
+    new_col_name = new_col_name.strip()
+    return new_col_name
+
+def refine_content(df):
+    strip_character_list = [' ', '\n', ':', '：','其他']
+    for col in df.columns:
+        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
+
+# 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))
+
+# 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_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))
+
+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))  
+
+# 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))
+
+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_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))
+
+
+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))