Python 3.x 基于Python中的两个数据帧计算加权总分

我在此excel文件中有两个表格，您可以在其中看到两张表格：

第1页：

第2页：

现在我需要根据

Input表中的
Input
值计算
总分
，计算逻辑如下：如果输入值为
1
，则根据该项目的
Level score*Weight
计算，如果为
0
，则忽略这些项目。最后，我们需要将加权分数相加得到总分

我们如何在Pandas或其他Python包中做到这一点

谢谢。
根据我的理解，您可以提取字段

表1（数据框1）->级别分数和权重
表2（数据框2）->输入值

并将两个数据帧合并到一个数据帧中，现在可以使用新的数据帧执行操作

如果您也需要相同的代码，请告诉我。
根据我的理解，您可以提取字段

表1（数据框1）->级别分数和权重
表2（数据框2）->输入值

并将两个数据帧合并到一个数据帧中，现在可以使用新的数据帧执行操作

如果您还需要相同的代码，请告诉我。
您可以使用以下工具阅读excel：

input_df = pd.read_excel('../../../../Downloads/test.xlsx', sheet_name='input').fillna(method='ffill')

valuate_df = pd.read_excel('../../../../Downloads/test.xlsx', sheet_name='valuate').fillna(method='ffill')

然后，您应该将它们合并到一个表中。如果您确定它们的顺序相同，请使用以下命令：

df = input_df.join(valuate_df[['Weight', 'Level score']])

它从评估表中选择两列['Weight'，'levelscore']，并将它们连接到第一个表。您将拥有如下所示的新表：

    Evaluation type Evaluation module   Evaluation index    Unnamed: 3  Evaluation content  Division level  Input   Weight  Level score
0   Business application security   User Management Ordinary account authentication NaN Evaluation item 1   Level 3 0   0.010490    3
1   Business application security   User Management Ordinary account authentication NaN Evaluation item 2   Level 3 1   0.010490    3
2   Business application security   User Management Ordinary account authentication NaN Evaluation item 3   Level 3 1   0.010490    3
3   Business application security   User Management Public account identity verification    NaN Evaluation item 1   Second level    0   0.006993    2
4   Business platform security  Business platform deployment    Platform equipment information record   NaN Evaluation item 1   Second level    1   0.006993    2
5   Business platform security  Business platform deployment    Facility distribution at home and abroad    NaN Evaluation item 1   Level 3 1   0.010490    3
6   Business platform security  Resource Scheduling Real-time resource monitoring   NaN Evaluation item 1   Second level    0   0.006993    2
7   Business platform security  Resource Scheduling Detection and processing of illegal information NaN Evaluation item 1   Second level    0   0.006993    2
8   Business platform security  Resource Scheduling System log retention    NaN Evaluation item 1   Level 3 0   0.010490    3

现在，您可以选择输入不是0的行：

df = df[df.Input!=0]

然后计算总分：

total_score = sum(df['Level score'] * df['Weight'])

因此，本例的总分为
0.1083915
，是吗？
您可以使用以下工具阅读excel：

input_df = pd.read_excel('../../../../Downloads/test.xlsx', sheet_name='input').fillna(method='ffill')

valuate_df = pd.read_excel('../../../../Downloads/test.xlsx', sheet_name='valuate').fillna(method='ffill')

然后，您应该将它们合并到一个表中。如果您确定它们的顺序相同，请使用以下命令：

df = input_df.join(valuate_df[['Weight', 'Level score']])

它从评估表中选择两列['Weight'，'levelscore']，并将它们连接到第一个表。您将拥有如下所示的新表：

    Evaluation type Evaluation module   Evaluation index    Unnamed: 3  Evaluation content  Division level  Input   Weight  Level score
0   Business application security   User Management Ordinary account authentication NaN Evaluation item 1   Level 3 0   0.010490    3
1   Business application security   User Management Ordinary account authentication NaN Evaluation item 2   Level 3 1   0.010490    3
2   Business application security   User Management Ordinary account authentication NaN Evaluation item 3   Level 3 1   0.010490    3
3   Business application security   User Management Public account identity verification    NaN Evaluation item 1   Second level    0   0.006993    2
4   Business platform security  Business platform deployment    Platform equipment information record   NaN Evaluation item 1   Second level    1   0.006993    2
5   Business platform security  Business platform deployment    Facility distribution at home and abroad    NaN Evaluation item 1   Level 3 1   0.010490    3
6   Business platform security  Resource Scheduling Real-time resource monitoring   NaN Evaluation item 1   Second level    0   0.006993    2
7   Business platform security  Resource Scheduling Detection and processing of illegal information NaN Evaluation item 1   Second level    0   0.006993    2
8   Business platform security  Resource Scheduling System log retention    NaN Evaluation item 1   Level 3 0   0.010490    3

现在，您可以选择输入不是0的行：

df = df[df.Input!=0]

然后计算总分：

total_score = sum(df['Level score'] * df['Weight'])

所以这个例子的总分是
0.1083915
，是吗