251111更新
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KANO/main.py
557
KANO/main.py
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@ -5,9 +5,12 @@
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本脚本用于KANO需求分析，能够处理问卷数据并输出需求分类结果
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"""
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import re
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import numpy
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import pandas
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import prettytable
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from utils.pandas_extension import save_as_workbook
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print("1 打开并读取Excel文件...", end="")
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@ -37,9 +40,9 @@ try:
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raise Exception("样本数为0")
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# 统计列数
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columns_counts = dataset.shape[1]
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columns = dataset.shape[1]
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# 若列数非奇数则抛出异常
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if columns_counts % 2 != 0:
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if columns % 2 != 0:
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raise Exception("列数为奇数")
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print(f"已完成，样本数为{samples_size}")
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@ -48,451 +51,133 @@ except Exception as exception:
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print(f"发生异常：{str(exception)}")
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exit()
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# 读取问卷题目和答案（从第7列开始为题目或答案）
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DataSet = DataSet.iloc[:, 6:]
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print("2 生成KANO评价结果...", end="")
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# 统计数据集中样本数量和题目数量
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Sample_Size, Question_Amount = DataSet.shape
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# KANO评价结果
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kano = pandas.DataFrame()
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# 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
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if Question_Amount % 2 == 0:
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for column in range(columns):
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if column % 2 != 0:
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continue
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# 计算问卷中涉及需求数量
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Requirement_Amount = int(Question_Amount / 2)
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# 功能名称
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feature = re.search(
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pattern="【(?P<feature>.*?)】", string=dataset.columns[column]
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).group("feature")
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else:
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# 生成某功能的选项分布
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distribution = pandas.crosstab(
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index=dataset.iloc[:, column], # 行索引为如果该产品有{{功能名称}}的选项
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columns=dataset.iloc[:, column + 1], # 列索引为如果该产品没有{{功能名称}}的选项
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rownames=[""],
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colnames=[""],
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normalize=True, # 转为占比
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).reindex(index=alternatives, columns=alternatives, fill_value=0)
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print("题目数量为奇数，请检查")
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print("")
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exit()
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print(
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"数据集中包含 %d 份样本， %d 个问题（涉及 %d 个需求）"
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% (Sample_Size, Question_Amount, Requirement_Amount)
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)
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print("")
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print("*" * 100)
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print("")
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print("2 数据预处理")
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print("")
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print("2.1 检查并删除不规范样本")
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print("")
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# 定义问卷中备选答案列表
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Alternatives = ["我很喜欢", "理所应当", "无所谓", "勉强接受", "我很不喜欢"]
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# 检查答案是否在指定范围，若否则将该答案定义为空值
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DataSet = DataSet.applymap(lambda x: x if x in Alternatives else None)
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# 删除包含缺失值的样本
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DataSet.dropna(axis="index", how="any", inplace=True)
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# 删除答案全部相同的样本
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DataSet = DataSet[DataSet.apply(pandas.Series.nunique, axis="columns") != 1]
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# 统计有效样本数量
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Sample_Size = DataSet.shape[0]
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print("处理后，有效样本数量为 %d 份" % (Sample_Size))
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print("")
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print("*" * 100)
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print("")
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print("3 数据处理")
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print("")
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Requirement_Labels = DataSet.columns.tolist()
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# 通过问题截取需求名称（截取'有'右侧、'，'左侧字符串）
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Requirement_Labels = [
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x[x.find("有") + 1 : x.find("，")]
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for x in Requirement_Labels
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if isinstance(x, str) and "具有" in x
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]
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print("3.1 绘制KANO评价结果分类对照表")
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print("")
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for Question_Number in range(Requirement_Amount):
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# 创建KANO评价结果分类对照表
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KANO = pandas.DataFrame(data=[], index=Alternatives, columns=Alternatives)
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for Column_Label in Alternatives:
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for Index_Label in Alternatives:
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# 统计并赋值
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KANO.loc[Index_Label, Column_Label] = DataSet.loc[
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(DataSet.iloc[:, Question_Number].isin([Index_Label]))
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& (DataSet.iloc[:, Question_Number + 1].isin([Column_Label])),
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:,
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].shape[0]
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# 修改行名
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KANO.index = [
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"Provide_Like",
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"Provide_Should",
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"Provide_Indifferent",
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"Provide_Grudging",
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"Provide_Hate",
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]
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# 重置索引
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KANO.reset_index(inplace=True)
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# 修改列名
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KANO.columns = [
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"",
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"Not_Provide_Like",
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"Not_Provide_Should",
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"Not_Provide_Indifferent",
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"Not_Provide_Grudging",
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"Not_Provide_Hate",
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]
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# 打印表格
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PrintTable = prettytable.PrettyTable()
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PrintTable.field_names = KANO.columns.tolist()
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for Index in KANO.index.tolist():
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PrintTable.add_row(KANO.loc[Index])
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PrintTable.align = "r"
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PrintTable.align[""] = "l"
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PrintTable.float_format = "."
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print(
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"附表 需求%d：%s的KANO评价结果分类对照表："
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% (Question_Number + 1, Requirement_Labels[Question_Number])
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)
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print(PrintTable)
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print("")
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print("字段说明：")
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print(
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"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”。"
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)
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print(
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"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”。"
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)
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print("")
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print("3.2 计算KANO评价维度")
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print("")
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# 创建KANO各维度分数表
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KANO_Report = pandas.DataFrame(
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data=[],
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columns=[
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"Requirement_Label",
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"A_Score",
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"O_Score",
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"M_Score",
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"I_Score",
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"R_Score",
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"Q_Score",
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],
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dtype="float",
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)
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KANO_Report["Requirement_Label"] = Requirement_Labels
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for Question_Number in range(Requirement_Amount):
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# 计算兴奋型需求分数
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A_Score = round(
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DataSet.loc[
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(DataSet.iloc[:, Question_Number].isin(["我很喜欢"]))
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& (
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DataSet.iloc[:, Question_Number + 1].isin(
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["理所应当", "无所谓", "勉强接受"]
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)
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kano = pandas.concat(
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objs=[
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kano,
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pandas.DataFrame(
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[
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{
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"功能名称": feature,
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"魅力属性": distribution.loc[
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"非常满意", ["理应如此", "无所谓", "勉强接受"]
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].sum(),
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"期望属性": distribution.loc["非常满意", "不满意"].sum(),
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"必备属性": distribution.loc[
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["理应如此", "无所谓", "勉强接受"], "不满意"
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].sum(),
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"无差异属性": distribution.loc[
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["理应如此", "无所谓", "勉强接受"],
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["理应如此", "无所谓", "勉强接受"],
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]
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.sum()
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.sum(),
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"反向属性": distribution.loc[
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["理应如此", "无所谓", "勉强接受", "不满意"], "非常满意"
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].sum()
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+ distribution.loc[
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("不满意", ["理应如此", "无所谓", "勉强接受"])
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].sum(),
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"可疑结果": distribution.loc["非常满意", "非常满意"]
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+ distribution.loc["不满意", "不满意"],
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}
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]
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),
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:,
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].shape[0]
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/ Sample_Size
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* 100,
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2,
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],
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ignore_index=True,
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)
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KANO_Report.loc[Question_Number, "A_Score"] = A_Score
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# 计算期望型需求分数
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O_Score = round(
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DataSet.loc[
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(DataSet.iloc[:, Question_Number].isin(["我很喜欢"]))
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& (DataSet.iloc[:, Question_Number + 1].isin(["我很不喜欢"])),
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:,
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].shape[0]
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/ Sample_Size
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* 100,
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2,
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# 计算better-worse分数，并贴标签
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kano = (
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kano.assign(
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满意系数=lambda dataframe: (dataframe["魅力属性"] + dataframe["期望属性"])
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/ (
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dataframe["魅力属性"]
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+ dataframe["期望属性"]
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+ dataframe["必备属性"]
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+ dataframe["无差异属性"]
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),
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不满意系数=lambda dataframe: (dataframe["必备属性"] + dataframe["期望属性"])
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/ (
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dataframe["魅力属性"]
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+ dataframe["期望属性"]
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+ dataframe["必备属性"]
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+ dataframe["无差异属性"]
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),
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)
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KANO_Report.loc[Question_Number, "O_Score"] = O_Score
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# 计算必备型需求分数
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M_Score = round(
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DataSet.loc[
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(DataSet.iloc[:, Question_Number].isin(["理所应当", "无所谓", "勉强接受"]))
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& (DataSet.iloc[:, Question_Number + 1].isin(["我很不喜欢"])),
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:,
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].shape[0]
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/ Sample_Size
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* 100,
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2,
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)
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KANO_Report.loc[Question_Number, "M_Score"] = M_Score
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# 计算无差型需求分数
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I_Score = round(
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DataSet.loc[
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(DataSet.iloc[:, Question_Number].isin(["理所应当", "无所谓", "勉强接受"]))
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& (
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DataSet.iloc[:, Question_Number + 1].isin(
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["理所应当", "无所谓", "勉强接受"]
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)
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.pipe(
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lambda dataframe: dataframe.assign(
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需求标签=numpy.select(
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condlist=[
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(dataframe["满意系数"] >= dataframe["满意系数"].mean())
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& (dataframe["不满意系数"] >= dataframe["不满意系数"].mean()),
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(dataframe["满意系数"] >= dataframe["满意系数"].mean())
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& (dataframe["不满意系数"] < dataframe["不满意系数"].mean()),
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(dataframe["满意系数"] < dataframe["满意系数"].mean())
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& (dataframe["不满意系数"] < dataframe["不满意系数"].mean()),
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(dataframe["满意系数"] < dataframe["满意系数"].mean())
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& (dataframe["不满意系数"] >= dataframe["不满意系数"].mean()),
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],
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choicelist=[
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"期望型需求(P2)",
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"魅力型需求(P3)",
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"无差异需求(P4)",
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"必备型需求(P1)",
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],
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default="未分类",
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),
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:,
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].shape[0]
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/ Sample_Size
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* 100,
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2,
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)
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)
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KANO_Report.loc[Question_Number, "I_Score"] = I_Score
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# 计算反向型需求分数
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R_Score = round(
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DataSet.loc[
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(
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DataSet.iloc[:, Question_Number].isin(
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["理所应当", "无所谓", "勉强接受", "我很不喜欢"]
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)
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)
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& (
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DataSet.iloc[:, Question_Number + 1].isin(
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["我很喜欢", "理所应当", "无所谓", "勉强接受"]
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)
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),
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:,
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].shape[0]
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/ Sample_Size
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* 100
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- I_Score,
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2,
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)
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KANO_Report.loc[Question_Number, "R_Score"] = R_Score
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# 计算可疑型需求分数
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Q_Score = round(
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DataSet.loc[
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(DataSet.iloc[:, Question_Number].isin(["我很喜欢"]))
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& (DataSet.iloc[:, Question_Number + 1].isin(["我很喜欢"])),
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:,
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].shape[0]
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/ Sample_Size
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* 100
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+ DataSet.loc[
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(DataSet.iloc[:, Question_Number].isin(["我很不喜欢"]))
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& (DataSet.iloc[:, Question_Number + 1].isin(["我很不喜欢"])),
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:,
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].shape[0]
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/ Sample_Size
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* 100,
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2,
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)
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KANO_Report.loc[Question_Number, "Q_Score"] = Q_Score
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# 打印表格
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PrintTable = prettytable.PrettyTable()
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PrintTable.field_names = KANO_Report.columns.tolist()
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for Index in KANO_Report.index.tolist():
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PrintTable.add_row(KANO_Report.loc[Index])
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PrintTable.align["Requirement_Label"] = "l"
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PrintTable.align["A_Score"] = "r"
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PrintTable.align["O_Score"] = "r"
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PrintTable.align["M_Score"] = "r"
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PrintTable.align["I_Score"] = "r"
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PrintTable.align["R_Score"] = "r"
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PrintTable.align["Q_Score"] = "r"
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PrintTable.align["Better_Score"] = "r"
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PrintTable.align["Worse_Score"] = "r"
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PrintTable.float_format["A_Score"] = ".2"
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PrintTable.float_format["O_Score"] = ".2"
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PrintTable.float_format["M_Score"] = ".2"
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PrintTable.float_format["I_Score"] = ".2"
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PrintTable.float_format["R_Score"] = ".2"
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PrintTable.float_format["Q_Score"] = ".2"
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PrintTable.float_format["Better_Score"] = ".2"
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PrintTable.float_format["Worse_Score"] = ".2"
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print("附表 各需求的KANO评价维度计算结果：")
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print(
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PrintTable.get_string(
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fields=[
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"Requirement_Label",
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"A_Score",
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"O_Score",
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"M_Score",
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"I_Score",
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"R_Score",
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"Q_Score",
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]
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.assign(
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魅力属性=lambda dataframe: dataframe["魅力属性"].apply(
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lambda cell: f"{cell:.2f}"
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),
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期望属性=lambda dataframe: dataframe["期望属性"].apply(
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lambda cell: f"{cell:.2f}"
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),
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必备属性=lambda dataframe: dataframe["必备属性"].apply(
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||||
lambda cell: f"{cell:.2f}"
|
||||
),
|
||||
无差异属性=lambda dataframe: dataframe["无差异属性"].apply(
|
||||
lambda cell: f"{cell:.2f}"
|
||||
),
|
||||
反向属性=lambda dataframe: dataframe["反向属性"].apply(
|
||||
lambda cell: f"{cell:.2f}"
|
||||
),
|
||||
可疑结果=lambda dataframe: dataframe["可疑结果"].apply(
|
||||
lambda cell: f"{cell:.2f}"
|
||||
),
|
||||
满意系数=lambda dataframe: dataframe["满意系数"].apply(
|
||||
lambda cell: f"{cell:.2f}"
|
||||
),
|
||||
不满意系数=lambda dataframe: dataframe["不满意系数"].apply(
|
||||
lambda cell: f"{cell:.2f}"
|
||||
),
|
||||
)
|
||||
)
|
||||
|
||||
print("字段说明：")
|
||||
save_as_workbook(worksheets=[("Sheet1", kano)], workbook_name="result.xlsx")
|
||||
|
||||
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("")
|
||||
print("已完成")
|
||||
|
|
|
|||
Binary file not shown.
Loading…
Reference in New Issue