自加入 Pandas：在同一 table 中合并/加入

Question

我把数据附在这里了。

Excel Data

我需要 return 一个包含所有员工列表（EmployeeID、名字、中间名、姓氏）及其经理的名字和姓氏的 DataFrame。输出 DataFrame 中的列应为：EmployeeID、FirstName、MiddleName、LastName、ManagerFirstName、ManagerLastName。

提示：考虑单独加入 table，因为管理者本身就是雇员。

这是我目前的代码，它给我重复的记录：

# Creating data frame from Excel File. Enter the appropriate file path
df = pd.read_excel(Employees)

df_new = df[['EmployeeID', 'ManagerID', 'FirstName', 'MiddleName', 'LastName']].copy()
df_new['ManagerID'] = pd.to_numeric(df_new['ManagerID'], errors='coerce').fillna(0)
# convert object to int64
df_new['ManagerID'] = df_new['ManagerID'].astype(np.int64)

result = df_new.merge(df_new, left_on='EmployeeID', right_on='ManagerID')

print(result.head())

如有任何帮助，我们将不胜感激。

Answer 1

我认为这行得通

df = pd.DataFrame({"EmployeeID":[259,278,204,78,255],
                  "ManagerID":[278,204,78,255,259],
                  "FirstName":["ben","garret","gabe","reuben","gordon"],
                  "MiddleName":["T","R","B","H","L"],
                  "LastName":["miller","vargas","mares","dsa","hee"]})

df['ManagerID'] = pd.to_numeric(df['ManagerID'], errors='coerce').fillna(0)
df_ = df[["EmployeeID","FirstName","LastName"]]
df_ = df_.rename(columns={"EmployeeID":"ManagerID","FirstName":"ManagerFirstName","LastName":"ManagerLastName"})
out = pd.merge(df,df_,on=["ManagerID"],how="left")
out = out.drop(["ManagerID"],axis=1)

自加入 Pandas：在同一 table 中合并/加入

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