https://github.com/lesiaukr/goit-pythonweb-hw-06
Master's degree | Advanced Python Programming | HW | SQLAlchemy | Database Schema Migrations | Alembic
https://github.com/lesiaukr/goit-pythonweb-hw-06
alembic faker goit-pythonweb-hw-06 migrations-generator python sqlalchemy
Last synced: 3 months ago
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Master's degree | Advanced Python Programming | HW | SQLAlchemy | Database Schema Migrations | Alembic
- Host: GitHub
- URL: https://github.com/lesiaukr/goit-pythonweb-hw-06
- Owner: LesiaUKR
- Created: 2025-01-06T11:44:33.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-01-08T10:37:38.000Z (9 months ago)
- Last Synced: 2025-03-02T18:16:38.510Z (8 months ago)
- Topics: alembic, faker, goit-pythonweb-hw-06, migrations-generator, python, sqlalchemy
- Language: Python
- Homepage:
- Size: 1.25 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Topic 6. Homework | SQLAlchemy | Database Schema Migrations | Alembic
## Task: Implement a database with the following schema:
- A table for students;
- A table for groups;
- A table for teachers;
- A table for subjects with a reference to the teacher responsible for the subject;
- A table for student grades, specifying the date the grade was received.
### Instructions
#### Database Setup
For this homework, we will use a PostgreSQL database. Run the following command
to start a Docker container:
```bash
docker run --name some-postgres -p 5432:5432 -e POSTGRES_PASSWORD=mysecretpassword -d postgres
```
Replace `some-postgres` with your container name and `mysecretpassword` with your
custom password for connecting to the database.
---
## Technical Task Description
### Step 1
#### Implement SQLAlchemy models for the following tables:
- Students table;
- Groups table;
- Teachers table;
- Subjects table (with a reference to the teacher);
- Grades table (where each student has grades for subjects, with a date for each grade).
---
### Step 2
#### Use Alembic to create migrations for the database schema.
---
### Step 3
#### Create a script seed.py to populate the database with random data:
- ~30–50 students;
- 3 groups;
- 5–8 subjects;
- 3–5 teachers;
- Up to 20 grades for each student across all subjects.
Use the `Faker` package for generating random data.
Populate the database using SQLAlchemy sessions.
---
### Step 4
#### Perform the following queries on the populated database:
1. Find the top 5 students with the highest average grade across all subjects.
2. Find the student with the highest average grade in a specific subject.
3. Find the average grade for groups in a specific subject.
4. Find the overall average grade across all grades.
5. Find the courses taught by a specific teacher.
6. Get the list of students in a specific group.**
7. Get the grades of students in a specific group for a specific subject.**
8. Find the average grade given by a specific teacher across their subjects.**
9. Get the list of courses attended by a specific student.**
10. List the courses taught to a specific student by a specific teacher.**
Для запитів оформити окремий файл `my_select.py`, де будуть 10 функцій від
`select_1` до `select_10`. При запитах використовуємо механізм сесій `SQLAlchemy`.
------
# TASK RESULTS
#### 1. Start `docker postgres container`
#### 2. Initial migration with `alembic`
#### 2a. Add new column - student's email - migration with `alembic`
#### 3. Database populated with the random data using `Faker`
#### 4. Results of the requested queries
