删除重复 dataframe pandas

Question

我有一个 dataframe ，我想将所有“小时”（列标题）总结为 1 个“经理”（列标题）下每个“名称”（列标题）的“总和”。 然后我想删除所有重复项，然后根据总小时数对 dataframe 进行排序并逐行打印。 但是，我一直在逐行打印出 Manager 的副本？

|---------------------|------------------|---------------------|------------------|
|      Department     |     Name         | Manager             | Hours            | 
|---------------------|------------------|---------------------|------------------|
|   Department name   |     person Name  | Manager Name        |no of hours       |
|---------------------|------------------|---------------------|------------------|

def total_group(csv_file):
    df = pd.read_csv(csv_file)
    df['Total Hours'] = df.groupby(['Manager'])['Hours'].transform('sum')
    new_df = df.drop_duplicates(subset=['Department', 'name', 'Manager']).sort_values('Total Hours')
    for index, row in new_df.iterrows():
        manager_value = row['Manager']
        total_hours = row['Total Hours']
        print("manager: {}, has: {} Total hours".format(manager_value, total_hours))


print(total_group(csv_file))

Dataframe 打印

df1 = df['Total Hours'] = df.groupby(['Direct Manager'])['Labor Hours'].transform('sum')
    print(df1)

结果

0        450.0
1        450.0
2        450.0
3        450.0
4        450.0
         ...  
43929    320.5
43930    320.5
43931    320.5
43932    320.5
43933    320.5
Name: Hours, Length: 43934, dtype: float64

新 dataframe 打印：

new_df = df.drop_duplicates(subset=['Department', 'Direct Manager']).sort_values('Total Hours')
    print(new_df)

结果：

                     Department              Name                Hours                   Total Hours
9554             Europe                     Dri, Bas ...         8.0                        72.000000
34498           Product & Design    Sun, Sunn  ...     5.0                        81.000000
19140           Product & Design    Oers, Len  ...      8.0                        122.000000

我想要的是这样的 dataframe：

                     Department              Manager                                Total Hours
9554             Europe                     Last, First ...                             72.000000
34498           Product                    Last, first  ...                         81.000000
19140           Design                     Last, First  ...                          122.000000

Answer 1

你想试试这个

df.groupby('Manager').agg({'Hours':['sum','count']}).sort_values(('Hours','sum'), ascending=False)