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))