70 lines
3.6 KiB
Python
70 lines
3.6 KiB
Python
import pandas as pd
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from typing import List
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def strip_character(column_name, characters: List[str]):
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new_col_name = column_name
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for character in characters:
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new_col_name = new_col_name.replace(character, '')
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new_col_name = new_col_name.strip()
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return new_col_name
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def refine_content(df):
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strip_character_list = [' ', '\n', ':', '：','其他']
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for col in df.columns:
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df[col] = df[col].apply(lambda x: "其他" if strip_character(x, strip_character_list) == "" else strip_character(x, strip_character_list))
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return df
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# 读取赢单Excel文件
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df_win = pd.read_excel('./data_src/pingcap_won.xlsx')
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acv_name = 'ACV'
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# ACV by 客户分类
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df_win_grouped_by_industry = df_win.groupby('客户分类')[acv_name].sum().astype(int).reset_index()
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df_win_grouped_by_industry[acv_name] = df_win_grouped_by_industry[acv_name].apply(lambda x: '{:,}'.format(x))
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print("------ACV by 行业------")
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print(refine_content(df_win_grouped_by_industry))
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# Group by customer industry and calculate the average ACV for each group
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won_average_acv_by_industry = df_win.groupby('客户分类')[acv_name].mean().astype(int).reset_index()
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won_average_acv_by_industry[acv_name] = won_average_acv_by_industry[acv_name].apply(lambda x: '{:,}'.format(x))
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print("------平均ACV by 行业------")
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print(refine_content(won_average_acv_by_industry))
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df_win_grouped_by_sub_industry = df_win.groupby('客户行业')[acv_name].sum().astype(int).reset_index()
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df_win_grouped_by_sub_industry[acv_name] = df_win_grouped_by_sub_industry[acv_name].apply(lambda x: '{:,}'.format(x))
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print("------ACV by 子行业------")
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print(refine_content(df_win_grouped_by_sub_industry))
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won_average_acv_by_sub_industry = df_win.groupby('客户行业')[acv_name].mean().astype(int).reset_index()
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won_average_acv_by_sub_industry[acv_name] = won_average_acv_by_sub_industry[acv_name].apply(lambda x: '{:,}'.format(x))
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print("------平均ACV by 子行业------")
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print(refine_content(won_average_acv_by_sub_industry))
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# 读取Excel文件
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df = pd.read_excel('./data_src/pingcap_pipeline.xlsx')
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# 按照"客户分类"列分组，并计算ACV列的和
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acv_name = '预估 ACV'
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df_pipeline_grouped_by_industry = df.groupby('负责人所属行业')[acv_name].sum().astype(int).reset_index()
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df_pipeline_grouped_by_industry = df_pipeline_grouped_by_industry.sort_values(by=acv_name, ascending=False)
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df_pipeline_grouped_by_industry[acv_name] = df_pipeline_grouped_by_industry[acv_name].apply(lambda x: '{:,}'.format(x))
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print("------预估ACV by 行业------")
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print(refine_content(df_pipeline_grouped_by_industry))
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pipeline_average_acv_by_industry = df.groupby('负责人所属行业')[acv_name].mean().astype(int).reset_index()
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pipeline_average_acv_by_industry[acv_name] = pipeline_average_acv_by_industry[acv_name].apply(lambda x: '{:,}'.format(x))
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print("------平均预估ACV by 行业------")
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print(refine_content(pipeline_average_acv_by_industry))
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df_pipeline_grouped_by_sub_industry = df.groupby('客户行业')[acv_name].sum().astype(int).reset_index()
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df_pipeline_grouped_by_sub_industry[acv_name] = df_pipeline_grouped_by_sub_industry[acv_name].apply(lambda x: '{:,}'.format(x))
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print("------预估ACV by 子行业------")
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print(refine_content(df_pipeline_grouped_by_sub_industry))
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pipeline_average_acv_by_sub_industry = df.groupby('客户行业')[acv_name].mean().fillna(0).astype(int).reset_index()
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pipeline_average_acv_by_sub_industry[acv_name] = pipeline_average_acv_by_sub_industry[acv_name].apply(lambda x: '{:,}'.format(x))
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print("------平均预估ACV by 子行业------")
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print(refine_content(pipeline_average_acv_by_sub_industry)) |