select csr.school_year 学年,csr.name 学期,cs.name 姓名,cs.code 学号,cs.grade 年级,cd.name 学院,cm.name 专业,ca.name 行政班,cj.lesson_no 课程号,tc.code 课程代码,tc.name 课程名称, listagg(ct.name,',') within group(order by ct.name) 授课教师, tc.credits 学分,cj.score_text 分数,cj.gp 绩点,xc.name 课程类别,xc.code 课程类别代码,hc.name 修读类别,xs.name 学生类别,pscj.score_text 平时成绩,qzcj.score_text 期中成绩, sycj.score_text 实验成绩,qmcj.score_text 期末成绩,zpcj.score_text 总评成绩,bkcj.score_text 补考成绩 from t_course_grades cj left join c_students cs on cs.id = cj.std_id left join c_semesters csr on csr.id = cj.semester_id left join t_courses tc on tc.id = cj.course_id left join c_departments cd on cd.id = cs.department_id left join c_majors cm on cm.id = cs.major_id left join c_adminclasses ca on ca.id = cs.adminclass_id left join xb_course_types xc on xc.id = cj.course_type_id left join hb_course_take_types hc on hc.id = cj.course_take_type_id left join xb_std_types xs on xs.id = cs.type_id left join t_lessons_teachers tlt on tlt.lesson_id=cj.lesson_id left join c_teachers ct on ct.id = tlt.teacher_id left join t_exam_grades bkcj on cj.id = bkcj.course_grade_id and bkcj.grade_type_id=4 left join t_exam_grades pscj on cj.id = pscj.course_grade_id and pscj.grade_type_id=3 left join t_exam_grades qzcj on cj.id = qzcj.course_grade_id and qzcj.grade_type_id=1 left join t_exam_grades zpcj on cj.id = zpcj.course_grade_id and zpcj.grade_type_id=7 left join t_exam_grades qmcj on cj.id = qmcj.course_grade_id and qmcj.grade_type_id=2 left join t_exam_grades sycj on cj.id = sycj.course_grade_id and sycj.grade_type_id=8 where cj.semester_id='243' and cs.code='21403186' order by cs.code

这是一条SQL语句，用于查询学生某学期的课程成绩及相关信息，其中包括学年、学期、姓名、学号、年级、学院、专业、行政班、课程号、课程代码、课程名称、授课教师、学分、分数、绩点、课程类别、课程类别代码、修读类别、学生类别、平时成绩、期中成绩、实验成绩、期末成绩、总评成绩、补考成绩等。其中的参数'243'和'21403186'需要根据具体情况进行调整。

相关问题

scipy.sparse.csr.csr_matrix

scipy.sparse.csr.csr_matrix是scipy库中的一种稀疏矩阵格式，它采用压缩稀疏行（CSR）存储方式，可以有效地存储大规模的稀疏矩阵，并且支持快速的矩阵运算。在机器学习、自然语言处理等领域中，经常需要处理大规模的稀疏矩阵，因此scipy.sparse.csr.csr_matrix是一种非常常用的数据结构。

torch.sparse_csr

torch.sparse_csr is a module in PyTorch that provides support for compressed sparse row (CSR) matrices. CSR matrices are a popular format for storing sparse matrices in a compressed form, where only the non-zero elements are stored.

The torch.sparse_csr module provides functions to create, manipulate, and perform operations on CSR matrices. Some of the key functions in this module include:

- torch.sparse_csr_matrix: Creates a new CSR matrix from a dense or sparse input tensor.
- torch.sparse_csr_tensor: Creates a new CSR tensor from a COO (coordinate) tensor.
- torch.sparse_csr_matmul: Performs a matrix multiplication between two CSR matrices.
- torch.sparse_csr_add: Adds two CSR matrices.
- torch.sparse_csr_sub: Subtracts two CSR matrices.
- torch.sparse_csr_transpose: Transposes a CSR matrix.

Overall, the torch.sparse_csr module provides a powerful and efficient way to work with sparse matrices in PyTorch.