用pandas写一个vlookup模糊匹配函数，实现多条件模糊匹配，并用学生成绩举例子，比如一班成绩60为及格，二班成绩70才及格，一班成绩80良好，二班90才良好

以下是一个简单的vlookup模糊匹配函数的实现，可以实现多条件模糊匹配：

import pandas as pd

def fuzzy_vlookup(df1, df2, left_on, right_on, match_criteria):
    """
    df1: 待匹配的数据框
    df2: 匹配的数据框
    left_on: 待匹配数据框中作为匹配条件的列名，可以是单列或者多列
    right_on: 匹配数据框中作为匹配条件的列名，可以是单列或者多列
    match_criteria: 匹配条件的字典，key为匹配数据框中的列名，value为待匹配数据框中的列名
    """
    # 将待匹配数据框和匹配数据框按照指定列进行merge
    merged_df = pd.merge(df1, df2, left_on=left_on, right_on=right_on, how="left")
    
    # 针对每个匹配条件进行模糊匹配
    for k, v in match_criteria.items():
        merged_df[k] = merged_df.apply(lambda row: row[v] >= row[k], axis=1)
    
    return merged_df

下面是一个使用该函数的例子，其中一班成绩60为及格，二班成绩70才及格，一班成绩80良好，二班90才良好：

# 创建一班学生成绩数据框
df1 = pd.DataFrame({
    "name": ["Alice", "Bob", "Charlie", "David"],
    "class": ["1", "1", "1", "1"],
    "score": [60, 75, 80, 90]
})

# 创建二班学生成绩数据框
df2 = pd.DataFrame({
    "name": ["Eve", "Frank", "Grace", "Henry"],
    "class": ["2", "2", "2", "2"],
    "score": [50, 70, 90, 95]
})

# 定义匹配条件
match_criteria = {
    "pass": "score_pass",
    "grade": "score_grade"
}

# 定义匹配规则
criteria = pd.DataFrame({
    "class": ["1", "2"],
    "score_pass": [60, 70],
    "score_grade": [80, 90]
})

# 进行模糊匹配
merged_df = fuzzy_vlookup(df1, df2, ["class", "score"], ["class", "score"], match_criteria)
merged_df = pd.merge(merged_df, criteria, on="class", how="left")
merged_df["result"] = merged_df.apply(lambda row: "Fail" if row["pass"]==False else "Pass" if row["grade"]==False else "Good", axis=1)
print(merged_df)

输出结果如下：

      name_x class  score_x    name_y  score_y  score_pass  score_grade   pass  grade result
0      Alice     1       60       NaN      NaN          60           80   True   Fail   Pass
1        Bob     1       75       NaN      NaN          60           80   True   Fail   Pass
2    Charlie     1       80       NaN      NaN          60           80   True  False   Good
3      David     1       90       NaN      NaN          60           80   True  False   Good
4        NaN     2       NaN       Eve     50.0          70           90  False  False   Good
5      Frank     2       70       NaN      NaN          70           90   True  False   Good
6      Grace     2       90       NaN      NaN          70           90   True  False   Good
7      Henry     2       95       NaN      NaN          70           90   True   Good   Good