英文:
How to Get Proper Count of Values in Python
问题
我之前尝试提出这个问题，但传达了我遇到问题的地方。我有一个在Python中使用NumPy和Pandas的数据集，我正在尝试按职位类型获取报告的数量。共有100多个职位标题，所以我将缩短示例：
ID Job_Title 报告数1 销售经理 21 销售经理 22 技术支持 03 技术支持 13 技术支持 14 销售主管 44 销售主管 45 销售经理 56 技术支持 2
我想要准确计算每个职位的报告数。类似这样：
职位标题 报告数销售经理 7销售主管 4技术支持 3
到目前为止，我有这个：
df.groupby('Job_Title')['报告数'].count().sort_values(ascending=False)
这是我得到的结果：
职位标题 报告数技术支持 4销售经理 3销售主管 2
英文:
I tried asking this question earlier and miscommunicated what I am having trouble with. I have a dataset in python that I am using numpy and pandas in and I am trying to get a count of reports by job type. There are are 100+ titles so I will shorten it for an example:
ID Job_Title reports1 Sales Manager 21 Sales Manager 22 Tech Support 03 Tech Support 13 Tech Support 14 Sales Lead 44 Sales Lead 45 Sales Manager 56 Tech Support 2
I would like to get a accurate count of the reports by position. Something Like this:
Job_Title reportsSales Manager 7Sales Lead 4Tech Support 3
So far what I have is this:
df.groupby('Job_Title')['reports'].count().sort_values(ascending = False)
And this is what I am getting:
Job_Title reportsTech Support 4Sales Manager 3Sales Lead 2
答案1
得分: 1
为了获得期望的结果，在按Job_Title分组并求和reports值之前，您需要通过ID和Job_Title删除重复项：
(df.drop_duplicates(['ID', 'Job_Title']).groupby('Job_Title', as_index=False)['reports'].sum().sort_values(by='reports', ascending=False, ignore_index=True))
英文:
To get the expected result, you have to drop duplicates by ID and Job_Title before grouping by Job_Title and sum reports values:
>>> (df.drop_duplicates(['ID', 'Job_Title']).groupby('Job_Title', as_index=False)['reports'].sum().sort_values(by='reports', ascending=False, ignore_index=True))Job_Title reports0 Sales Manager 71 Sales Lead 42 Tech Support 3
答案2
得分: 1
试试这个：
df_new = (df.groupby('Job_Title', as_index=False)['reports'].apply(lambda g: sum(set(g))).sort_values('reports', ascending=False, ignore_index=True))print(df_new)
输出：
Job_Title reports0 Sales Manager 71 Sales Lead 42 Tech Support 3
英文:
Try this:
df_new = (df.groupby('Job_Title', as_index=False)['reports'].apply(lambda g: sum(set(g))).sort_values('reports', ascending = False, ignore_index=True))print(df_new)
Output:
Job_Title reports0 Sales Manager 71 Sales Lead 42 Tech Support 3
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