From 9930e6fee14a92513eda30e3de443c563eaf173d Mon Sep 17 00:00:00 2001 From: marslbr Date: Mon, 10 Nov 2025 18:59:16 +0800 Subject: [PATCH] =?UTF-8?q?251110=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- KANO/KANO模型客户调研问卷.xlsx | Bin 0 -> 10179 bytes KANO/main.py | 498 +++++++++++++++++++++++++++++++++ 2 files changed, 498 insertions(+) create mode 100644 KANO/KANO模型客户调研问卷.xlsx create mode 100644 KANO/main.py diff --git a/KANO/KANO模型客户调研问卷.xlsx b/KANO/KANO模型客户调研问卷.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..c2b72aed25deaa36cd8f51385bb9ebb452a9bf1d GIT binary patch literal 10179 zcma)i1ymi&vNrCvaZ9j`ySux4aChCfy95aC1P>Z45H!KvonRrjLvSa^56QhJ=j5ID z*57Ndp1o#Dy1$yPo~|k-Sx6`Zu;&)0uqO0;{cnSR`eO<*QE~z~I5R3ekzqdFfcr@{ zp{ZQO2Mz|t3jqd(_IENP2L}cZJKL=I5gABk%+RZ#BeZJJ6gGhREoMhsmY&)bvvV=> zFp%UkO2yI2U}y&ECp6v9t|YC|7u)p1!vPA@$xY7ADBFIi#}m!{((nS7Ht1iEE~&ut 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dataset.iloc[ + :, 3: + ] # 原始数据第一列为编号、第二列为提交人、第三列为提交时间，从第四列到最后一列为选项，删除第一列至第三列 + .map( + lambda cell: cell if cell in alternatives else pandas.NA + ) # 检查是否在选项范围，若不在选项范围则置为缺失值 + .dropna(axis="index", how="any") # 删除缺失值 + .loc[lambda dataframe: dataframe.nunique(axis=1) != 1] # 删除相同选项的样本 + ) + + # 统计样本数 + samples_size = dataset.shape[0] + # 若样本数为0则抛出异常 + if samples_size == 0: + raise Exception("样本数为0") + + # 统计列数 + columns_counts = dataset.shape[1] + # 若列数非奇数则抛出异常 + if columns_counts % 2 != 0: + raise Exception("列数为奇数") + + print(f"已完成，样本数为{samples_size}") + +except Exception as exception: + print(f"发生异常：{str(exception)}") + exit() + +# 读取问卷题目和答案（从第7列开始为题目或答案） +DataSet = DataSet.iloc[:, 6:] + +# 统计数据集中样本数量和题目数量 +Sample_Size, Question_Amount = DataSet.shape + +# 判断题目数量是否为偶数，若为偶数则计算问卷中涉及需求数量，若为奇数则终止脚本 +if Question_Amount % 2 == 0: + + # 计算问卷中涉及需求数量 + Requirement_Amount = int(Question_Amount / 2) + +else: + + print("题目数量为奇数，请检查") + print("") + + exit() + +print( + "数据集中包含 %d 份样本， %d 个问题（涉及 %d 个需求）" + % (Sample_Size, Question_Amount, Requirement_Amount) +) +print("") + +print("*" * 100) +print("") + +print("2 数据预处理") +print("") + +print("2.1 检查并删除不规范样本") +print("") + +# 定义问卷中备选答案列表 +Alternatives = ["我很喜欢", "理所应当", "无所谓", "勉强接受", "我很不喜欢"] + +# 检查答案是否在指定范围，若否则将该答案定义为空值 +DataSet = DataSet.applymap(lambda x: x if x in Alternatives else None) + +# 删除包含缺失值的样本 +DataSet.dropna(axis="index", how="any", inplace=True) + +# 删除答案全部相同的样本 +DataSet = DataSet[DataSet.apply(pandas.Series.nunique, axis="columns") != 1] + +# 统计有效样本数量 +Sample_Size = DataSet.shape[0] + +print("处理后，有效样本数量为 %d 份" % (Sample_Size)) +print("") + +print("*" * 100) +print("") + +print("3 数据处理") +print("") + +Requirement_Labels = DataSet.columns.tolist() + +# 通过问题截取需求名称（截取'有'右侧、'，'左侧字符串） +Requirement_Labels = [ + x[x.find("有") + 1 : x.find("，")] + for x in Requirement_Labels + if isinstance(x, str) and "具有" in x +] + +print("3.1 绘制KANO评价结果分类对照表") +print("") + +for Question_Number in range(Requirement_Amount): + + # 创建KANO评价结果分类对照表 + KANO = pandas.DataFrame(data=[], index=Alternatives, columns=Alternatives) + + for Column_Label in Alternatives: + + for Index_Label in Alternatives: + + # 统计并赋值 + KANO.loc[Index_Label, Column_Label] = DataSet.loc[ + (DataSet.iloc[:, Question_Number].isin([Index_Label])) + & (DataSet.iloc[:, Question_Number + 1].isin([Column_Label])), + :, + ].shape[0] + + # 修改行名 + KANO.index = [ + "Provide_Like", + "Provide_Should", + "Provide_Indifferent", + "Provide_Grudging", + "Provide_Hate", + ] + + # 重置索引 + KANO.reset_index(inplace=True) + + # 修改列名 + KANO.columns = [ + "", + "Not_Provide_Like", + "Not_Provide_Should", + "Not_Provide_Indifferent", + "Not_Provide_Grudging", + "Not_Provide_Hate", + ] + + # 打印表格 + + PrintTable = prettytable.PrettyTable() + + PrintTable.field_names = KANO.columns.tolist() + + for Index in KANO.index.tolist(): + + PrintTable.add_row(KANO.loc[Index]) + + PrintTable.align = "r" + + PrintTable.align[""] = "l" + + PrintTable.float_format = "." + + print( + "附表 需求%d：%s的KANO评价结果分类对照表：" + % (Question_Number + 1, Requirement_Labels[Question_Number]) + ) + + print(PrintTable) + + print("") + +print("字段说明：") + +print( + "1）Not_Provide_Like为不提供该需求、用户表示“我很喜欢”，Not_Provide_Should为不提供该需求、用户表示“理所应当”，Not_Provide_Indifferent为不提供该需求、用户表示“无所谓”，Not_Provide_Grudging为不提供该需求、用户表示“勉强接受”，Not_Provide_Hate为不提供该需求、用户表示“我很不喜欢”。" +) + +print( + "1）Provide_Like为提供该需求、用户表示“我很喜欢”，Provide_Should为提供该需求、用户表示“理所应当”，Provide_Indifferent为提供该需求、用户表示“无所谓”，Provide_Grudging为提供该需求、用户表示“勉强接受”，Provide_Hate为不提供该需求、用户表示“我很不喜欢”。" +) + +print("") + +print("3.2 计算KANO评价维度") +print("") + +# 创建KANO各维度分数表 +KANO_Report = pandas.DataFrame( + data=[], + columns=[ + "Requirement_Label", + "A_Score", + "O_Score", + "M_Score", + "I_Score", + "R_Score", + "Q_Score", + ], + dtype="float", +) + +KANO_Report["Requirement_Label"] = Requirement_Labels + +for Question_Number in range(Requirement_Amount): + + # 计算兴奋型需求分数 + A_Score = round( + DataSet.loc[ + (DataSet.iloc[:, Question_Number].isin(["我很喜欢"])) + & ( + DataSet.iloc[:, Question_Number + 1].isin( + ["理所应当", "无所谓", "勉强接受"] + ) + ), + :, + ].shape[0] + / Sample_Size + * 100, + 2, + ) + + KANO_Report.loc[Question_Number, "A_Score"] = A_Score + + # 计算期望型需求分数 + O_Score = round( + DataSet.loc[ + (DataSet.iloc[:, Question_Number].isin(["我很喜欢"])) + & (DataSet.iloc[:, Question_Number + 1].isin(["我很不喜欢"])), + :, + ].shape[0] + / Sample_Size + * 100, + 2, + ) + + KANO_Report.loc[Question_Number, "O_Score"] = O_Score + + # 计算必备型需求分数 + M_Score = round( + DataSet.loc[ + (DataSet.iloc[:, Question_Number].isin(["理所应当", "无所谓", "勉强接受"])) + & (DataSet.iloc[:, Question_Number + 1].isin(["我很不喜欢"])), + :, + ].shape[0] + / Sample_Size + * 100, + 2, + ) + + KANO_Report.loc[Question_Number, "M_Score"] = M_Score + + # 计算无差型需求分数 + I_Score = round( + DataSet.loc[ + (DataSet.iloc[:, Question_Number].isin(["理所应当", "无所谓", "勉强接受"])) + & ( + DataSet.iloc[:, Question_Number + 1].isin( + ["理所应当", "无所谓", "勉强接受"] + ) + ), + :, + ].shape[0] + / Sample_Size + * 100, + 2, + ) + + KANO_Report.loc[Question_Number, "I_Score"] = I_Score + + # 计算反向型需求分数 + R_Score = round( + DataSet.loc[ + ( + DataSet.iloc[:, Question_Number].isin( + ["理所应当", "无所谓", "勉强接受", "我很不喜欢"] + ) + ) + & ( + DataSet.iloc[:, Question_Number + 1].isin( + ["我很喜欢", "理所应当", "无所谓", "勉强接受"] + ) + ), + :, + ].shape[0] + / Sample_Size + * 100 + - I_Score, + 2, + ) + + KANO_Report.loc[Question_Number, "R_Score"] = R_Score + + # 计算可疑型需求分数 + Q_Score = round( + DataSet.loc[ + (DataSet.iloc[:, Question_Number].isin(["我很喜欢"])) + & (DataSet.iloc[:, Question_Number + 1].isin(["我很喜欢"])), + :, + ].shape[0] + / Sample_Size + * 100 + + DataSet.loc[ + (DataSet.iloc[:, Question_Number].isin(["我很不喜欢"])) + & (DataSet.iloc[:, Question_Number + 1].isin(["我很不喜欢"])), + :, + ].shape[0] + / Sample_Size + * 100, + 2, + ) + + KANO_Report.loc[Question_Number, "Q_Score"] = Q_Score + +# 打印表格 + +PrintTable = prettytable.PrettyTable() + +PrintTable.field_names = KANO_Report.columns.tolist() + +for Index in KANO_Report.index.tolist(): + + PrintTable.add_row(KANO_Report.loc[Index]) + +PrintTable.align["Requirement_Label"] = "l" + +PrintTable.align["A_Score"] = "r" + +PrintTable.align["O_Score"] = "r" + +PrintTable.align["M_Score"] = "r" + +PrintTable.align["I_Score"] = "r" + +PrintTable.align["R_Score"] = "r" + +PrintTable.align["Q_Score"] = "r" + +PrintTable.align["Better_Score"] = "r" + +PrintTable.align["Worse_Score"] = "r" + +PrintTable.float_format["A_Score"] = ".2" + +PrintTable.float_format["O_Score"] = ".2" + +PrintTable.float_format["M_Score"] = ".2" + +PrintTable.float_format["I_Score"] = ".2" + +PrintTable.float_format["R_Score"] = ".2" + +PrintTable.float_format["Q_Score"] = ".2" + +PrintTable.float_format["Better_Score"] = ".2" + +PrintTable.float_format["Worse_Score"] = ".2" + +print("附表 各需求的KANO评价维度计算结果：") + +print( + PrintTable.get_string( + fields=[ + "Requirement_Label", + "A_Score", + "O_Score", + "M_Score", + "I_Score", + "R_Score", + "Q_Score", + ] + ) +) + +print("字段说明：") + +print( + "1）Requirement_Label为需求名称，A_Score为兴奋型需求分数，O_Score为期望型需求分数，M_Score为必备型需求分数，I_Score为无差型需求分数，R_Score为反向型需求分数，Q_Score为可疑型需求分数。" +) + +print("") + +print("3.3 定义需求类型和Better-Worse分数") +print("") + +# 以KANO评价维度中最高分定义需求类型 +Requirement_Types = list( + KANO_Report[ + ["A_Score", "O_Score", "M_Score", "I_Score", "R_Score", "Q_Score"] + ].idxmax(axis="columns") +) + +# 通过列名截取需求类型（第一位、'_'左侧字符串） +Requirement_Types = [ + x[0 : x.find("_")] for x in Requirement_Types if isinstance(x, str) +] + +KANO_Report["Requirement_Type"] = Requirement_Types + +# 计算Better分数 +KANO_Report["Better_Score"] = ( + (KANO_Report["A_Score"] + KANO_Report["O_Score"]) + / ( + KANO_Report["A_Score"] + + KANO_Report["O_Score"] + + KANO_Report["M_Score"] + + KANO_Report["I_Score"] + ) + * 100 +) + +# 计算Worse分数 +KANO_Report["Worse_Score"] = ( + -1 + * (KANO_Report["O_Score"] + KANO_Report["M_Score"]) + / ( + KANO_Report["A_Score"] + + KANO_Report["O_Score"] + + KANO_Report["M_Score"] + + KANO_Report["I_Score"] + ) + * 100 +) + +# 打印表格 + +PrintTable = prettytable.PrettyTable() + +PrintTable.field_names = KANO_Report.columns.tolist() + +for Index in KANO_Report.index.tolist(): + + PrintTable.add_row(KANO_Report.loc[Index]) + +PrintTable.align["Requirement_Label"] = "l" + +PrintTable.align["Requirement_Type"] = "r" + +PrintTable.align["Better_Score"] = "r" + +PrintTable.align["Worse_Score"] = "r" + +PrintTable.float_format["Better_Score"] = ".2" + +PrintTable.float_format["Worse_Score"] = ".2" + +print("附表 各需求的KANO评价维度计算结果：") + +print( + PrintTable.get_string( + fields=["Requirement_Label", "Requirement_Type", "Better_Score", "Worse_Score"] + ) +) + +print("字段说明：") + +print("1）Requirement_Label为需求名称，Requirement_Type为需求类型。") + +print( + "2）A为兴奋型需求：表示产品具有该种需求，则用户满意度会提高；没有该种需求，则用户满意度不会降低。建议给予P3关注。" +) + +print( + "3）O为期望型需求：表示产品具有该种需求，则用户满意度会提高；没有该种需求，则用户满意度会降低。建议给予P1关注。" +) + +print( + "4）M为必备型需求：表示产品具有该种需求，则用户满意度不会提高；没有该种需求，则用户满意度会降低。建议给予P2关注。" +) + +print( + "5）I为无差型需求：表示产品具有该种需求，则用户满意度不会提高；没有该种需求，则用户满意度不会降低。建议给予P4关注。" +) + +print("6）R为反向型需求：建议给予关注。") + +print("7）Q为可疑型需求：建议给予关注。") + +print( + "8）Better_Score为Better分数。表示如果产品具有某种需求，则用户满意度会提高，数值越大提高越大。" +) + +print( + "9）Worse_Score为Worse分数。表示如果产品没有某种需求，则用户满意度会下降，数值越小下降越大。" +) + +print("")