From 085f0dc880b0e50aad569b9f131336ef0f3a656e Mon Sep 17 00:00:00 2001
From: Tiger Ren <tigeren@live.com>
Date: Fri, 23 Aug 2024 19:50:35 +0800
Subject: [PATCH] =?UTF-8?q?=E8=AE=A1=E7=AE=97ACV=E5=88=86=E5=B8=83?=
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---
 analyze_acv_dist.py | 77 +++++++++++++++++----------------------------
 1 file changed, 29 insertions(+), 48 deletions(-)

diff --git a/analyze_acv_dist.py b/analyze_acv_dist.py
index e75909f..b116625 100644
--- a/analyze_acv_dist.py
+++ b/analyze_acv_dist.py
@@ -1,5 +1,6 @@
 import pandas as pd
 from typing import List
+
 def strip_character(column_name, characters: List[str]):
 
     new_col_name = column_name
@@ -14,57 +15,37 @@ 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
 
-# 读取赢单Excel文件
-df_win = pd.read_excel('./data_src/pingcap_won.xlsx')
-acv_name = 'ACV'
+def get_acv_distribution(df, acv_name, industry_col_name):
+    # Define the bins for ACV intervals
+    bins = [0, 1e6, 5e6, float('inf')]
+    labels = ['<100万', '100万-500万', '>500万']
+    # Create a new column 'ACV Interval' based on the bins
+    df['ACV Interval'] = pd.cut(df[acv_name], bins=bins, labels=labels, right=False)
+    industry_acv_distribution = df.groupby([industry_col_name, 'ACV Interval']).size().unstack(fill_value=0)
+    industry_acv_distribution.loc['Total'] = industry_acv_distribution.sum()
+    return industry_acv_distribution
 
-# 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))
+# Define the bins for ACV intervals
+df = pd.read_excel('./data_src/pingcap_won.xlsx')
 
 
+print('---------成单:ACV Distribution 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))
+print(get_acv_distribution(df, 'ACV', '客户分类'))
 
 
-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
+print('---------成单:ACV Distribution by sub-industry--------')
+
+print(get_acv_distribution(df, 'ACV', '客户行业'))
+
+
+df_pipeline = pd.read_excel('./data_src/pingcap_pipeline.xlsx')
+
+print('---------Pipeline:ACV Distribution by industry--------')
+
+print(get_acv_distribution(df_pipeline, '预估 ACV', '负责人所属行业'))
+
+
+print('---------Pipeline:ACV Distribution by sub-industry--------')
+
+print(get_acv_distribution(df_pipeline, '预估 ACV', '客户行业'))
\ No newline at end of file