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暂存analyze_acv相关代码

This commit is contained in:
Tiger Ren 2024-08-23 16:51:35 +08:00
parent e05fdc538e
commit 2049baa5f8
5 changed files with 156 additions and 34 deletions
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19
ExcelHelper.py
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@ -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')

15
analysis.py
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@ -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)

86
analyze_acv.py Normal file
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@ -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))

70
analyze_acv_dist.py Normal file
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@ -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))

0
data_src/pingcap.xlsx → data_src/pingcap_won.xlsx
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