在 pandas 中分组和聚合

Question

    code_presentation   code_module score   id_student  id_assessment   date_submitted
0   2013J               AAA         78.0    11391        1752           18
1   2013J               AAA         70.0    11391        1800           22
2   2013J               AAA         72.0    31604        1752           17
3   2013J               AAA         69.0    31604        1800           26
.....

我需要计算提交的天数以及如何正确分组以获得以下结果：

id_student  id_assessment date_submitted
11391       1752          1
            1800          1
31604       1752          1
            1800          1

... ETC

我尝试：

analasys_grouped = analasys.groupby ( 'id_student', as_index = False)\
.agg({'id_assessment':'count', 'date_submitted': 'count'})
analasys_grouped 

但它工作不正常

Answer 1

如果我对您的理解正确，您想对按id_assessment分组的id_student应用value_counts() 。 尝试：

assessment_count_per_student = df.groupby('id_student')['id_assessment'].value_counts()

print(assessment_count_per_student)

id_student  id_assessment
11391       1752             1
            1800             1
31604       1752             1
            1800             1
Name: id_assessment, dtype: int64

Answer 2

您需要将id_assessment传递到groupby语句中。

df.groupby(['id_student', 'id_assessment'])['date_submitted'].count()


id_student  id_assessment
11391       1752             1
            1800             1
31604       1752             1
            1800             1

---

在您的尝试中，您仅按id_student分组，然后计算提交的评估和日期。