auto-group 1.2.0

Tools for creating tree structures from flat list of dicts

Auto Grouping Tools

This package comes with a set of helpful auto grouping tools.

These tools solve a problem where you have M:N relations between two entities and need to join them together. Their functionality is also helpful when working with SQL views.

SELECT name, surname, job.name as job___name
FROM person
JOIN
    works ON works.person_id = person.id
    job ON job.id = works.job_id
WHERE
    person.id = 2;

For a single person, who has two jobs, DB might output something like this:

name	surname	job___name
Jane	Doe	Accountant
Jane	Doe	Developer

Which might be OK when fetching only one person and his jobs. There are use cases, when you need to fetch more and more people. Output will be much larger. This is the place, where auto grouping tools come handy.

auto_group_dict

This function groups dict keys with same prefix under one dict key. Groups used as group keys are identified by group separator ___.

person = {
    "name": "Jane",
    "surname": "Doe",
    "job___name": "Accountant",
    "job___established": 2001
}

ret = auto_group_dict(person)

# Returns in
ret = {
    "name": "Jane",
    "surname": "Doe",
    "job": {
        "name": "Accountant",
        "established": 2001,
    }
}

auto_group_list

IMPORTANT: All items which are inside lists are sorted exactly the same as they came from the DB.

Let's say that we have want to retrieve a new person from our DB. Jane Doe now has two jobs: an accountant and a developer.

Database returns two rows as specified above. But in object oriented world, it would be better for us to have it in one dict. This is where auto_group_list comes handy.

SELECT person.name, person.surname, job.name as jobs__name
FROM person
JOIN
    works ON works.person_id = person.id
    job ON job.id = works.job_id
WHERE
    person.id = 2;

Assuming our SQL query returns 2 rows like this:

rows = [
    {
        "name": "Jane",
        "surname": "Doe",
        "jobs__name": "Accountant",
    },
    {
        "name": "Jane",
        "surname": "Doe",
        "jobs__name": "Developer",
    }
]

ret = auto_group_list(rows)

# Returns in
ret = {
    "name": "Jane",
    "surname": "Doe",
    "jobs": [
        {
            "name": "Accountant"
        },
        {
            "name": "Developer"
        }
    ]
}

This is kind of handy, isn't it? But what if we want to omit our WHERE statement? This is where auto_group_list_by_pkeys comes in place.

auto_group_list_by_pkeys

Next and the last useful is handy when you want to for example fetch multiple people from DB, keep m..n relations and have everything grouped nicely. Like so:

SELECT person.id as _id, person.name, person.surname, job.name as jobs__name
FROM person
JOIN
    works ON works.person_id = person.id
    job ON job.id = works.job_id
WHERE
    person.id IN (2, 3);

Our person no. 2 is Jane Doe, who works as an accountant and a developer. Person no. 3 is John Doe, works as an DevOps Engineer and a developer.

Let's say our grouping key is _id.

Our fetched data converted to python might look something like this:

rows = [
    {
        "_id": 2,
        "name": "Jane",
        "surname": "Doe",
        "jobs__name": "Accountant"
    },
    {
        "_id": 2,
        "name": "Jane",
        "surname": "Doe",
        "jobs__name": "Developer"
    },
    {
        "_id": 3,
        "name": "John",
        "surname": "Doe",
        "jobs__name": "DevOps Engineer"
    },
    {
        "_id": 3,
        "name": "John",
        "surname": "Doe",
        "jobs__name": "Developer"
    }
]

Let's make it prettier!

ret = auto_group_list_by_pkeys(("_id",), rows, use_auto_group_dict=True)

# Returns dict with 2 items, grouped by key "_id"
ret = {
    "2": {
        "_id": 2,
        "name": "Jane",
        "surname": "Doe",
        "jobs": [
            {
                "name": "Accountant"
            },
            {
                "name": "Developer"
            }
        ]
    },
    "3": {
        "_id": 3,
        "name": "John",
        "surname": "Doe",
        "jobs": [
            {
                "name": "DevOps Engineer"
            },
            {
                "name": "Developer"
            }
        ]
    }
}

Now we have all our cases covered, ready to go.