95 lines
4.2 KiB
Python
95 lines
4.2 KiB
Python
import pandas as pd
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from typing import List
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from openpyxl import Workbook
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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 isinstance(x, str) and strip_character(x, strip_character_list) == ""
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else (strip_character(x, strip_character_list) if isinstance(x, str) else x))
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return df
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def calc_acv_mean(df, acv_name, group_by_column):
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df_grouped_mean = df.groupby(group_by_column)[acv_name].mean().fillna(0).astype(int).reset_index()
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df_grouped_mean = df_grouped_mean.sort_values(by=acv_name, ascending=False)
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df_grouped_mean[acv_name] = df_grouped_mean[acv_name].apply(lambda x: '{:,}'.format(x))
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return df_grouped_mean
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def calc_acv_sum(df, acv_name, group_by_column):
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df_grouped_sum = df.groupby(group_by_column)[acv_name].sum().astype(int).reset_index()
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df_grouped_sum = df_grouped_sum.sort_values(by=acv_name, ascending=False)
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df_grouped_sum[acv_name] = df_grouped_sum[acv_name].apply(lambda x: '{:,}'.format(x))
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return df_grouped_sum
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def calc_acv_sum_and_count(df, acv_name, group_by_column):
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df_grouped_sum = df.groupby(group_by_column)[acv_name].sum().astype(int).reset_index()
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df_grouped_count = df.groupby(group_by_column)[acv_name].count().reset_index()
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df_grouped_sum_count = pd.merge(df_grouped_sum, df_grouped_count, on=group_by_column, how='outer')
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return df_grouped_sum_count
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def save_to_excel(dataframes, sheet_names, output_file):
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with pd.ExcelWriter(output_file, engine='openpyxl') as writer:
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for df, sheet_name in zip(dataframes, sheet_names):
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df.to_excel(writer, sheet_name=sheet_name, index=False)
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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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# 创建一个列表来存储所有的数据帧和对应的sheet名称
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dataframes = []
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sheet_names = []
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# ACV by 客户分类
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df_win_grouped_by_industry_sum = calc_acv_sum_and_count(df_win, acv_name, '客户分类')
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dataframes.append(refine_content(df_win_grouped_by_industry_sum))
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sheet_names.append("ACV by 行业")
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# Group by customer industry and calculate the average ACV for each group
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df_win_grouped_by_industry_mean = calc_acv_mean(df_win, acv_name, '客户分类')
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dataframes.append(refine_content(df_win_grouped_by_industry_mean))
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sheet_names.append("平均ACV by 行业")
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df_win_grouped_by_sub_industry_sum = calc_acv_sum_and_count(df_win, acv_name, '客户行业')
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dataframes.append(refine_content(df_win_grouped_by_sub_industry_sum))
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sheet_names.append("ACV by 子行业")
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df_win_grouped_by_sub_industry_mean = calc_acv_mean(df_win, acv_name, '客户行业')
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dataframes.append(refine_content(df_win_grouped_by_sub_industry_mean))
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sheet_names.append("平均ACV by 子行业")
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# <20><><EFBFBD>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_sum = calc_acv_sum_and_count(df, acv_name, '负责人所属行业')
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dataframes.append(refine_content(df_pipeline_grouped_by_industry_sum))
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sheet_names.append("预估ACV by 行业")
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df_pipeline_grouped_by_industry_mean = calc_acv_mean(df, acv_name, '负责人所属行业')
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dataframes.append(refine_content(df_pipeline_grouped_by_industry_mean))
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sheet_names.append("平均预估ACV by 行业")
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df_pipeline_grouped_by_sub_industry_sum = calc_acv_sum_and_count(df, acv_name, '客户行业')
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dataframes.append(refine_content(df_pipeline_grouped_by_sub_industry_sum))
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sheet_names.append("预估ACV by 子行业")
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df_pipeline_grouped_by_sub_industry_mean = calc_acv_mean(df, acv_name, '客户行业')
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dataframes.append(refine_content(df_pipeline_grouped_by_sub_industry_mean))
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sheet_names.append("平均预估ACV by 子行业")
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# 保存所有数据帧到一个Excel文件中
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output_file = './output/acv_analysis_v2(with count).xlsx'
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save_to_excel(dataframes, sheet_names, output_file)
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print(f"Analysis results have been saved to {output_file}") |