# -*- coding: utf-8 -*-

"""
脚本说明：
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
"""

import pandas

import prettytable


print("1 打开并读取Excel文件...", end="")

try:
    dataset = pandas.read_excel(io="KANO模型客户调研问卷.xlsx", sheet_name="问卷结果")

    # 选项范围
    alternatives = ["非常满意", "理应如此", "无所谓", "勉强接受", "不满意"]

    # 数据清洗
    dataset = (
        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
        .loc[lambda dataframe: dataframe.nunique(axis=1) != 1]  # 删除相同选项的样本
        .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
    )

    # 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("")