删除重复数据帧

Question

我有一个数据框，我想把所有的“小时”（列标题）加起来，在1“管理器”（列标题）下的每个“名称”（列标题）加起来。然后，我想在根据总小时数和对数据帧排序之前删除所有重复项，并逐行打印出来。然而，我不断得到重复的经理逐行打印出来？你知道吗

|---------------------|------------------|---------------------|------------------|
|      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))

数据帧打印

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

新数据帧打印：

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

我想要的是这样的数据帧：

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

Answer 1

你想试试这个吗

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