py-directus 0.0.30

Python wrapper for asynchronous interaction with Directus

py-directus

Disclaimer: Under development

This is very early in development and the API is subject to change.

If you intend to use this library in a production environment, expect undocumented changes and bugs until the first stable release.

If you find any issues or have any suggestions, please open an issue or a pull request.

Documentation here

py-directus is a Python wrapper for asynchronous interaction with the Directus headless CMS API. It provides a convenient and easy-to-use interface for performing CRUD operations, querying data, and managing resources in Directus.

Features

• Asynchronous
• Login and authentication handling
• Reading and writing data from Directus collections
• Filtering, sorting, and searching data
• Aggregating data using aggregation operators
• Creating, updating, and deleting items in Directus collections
• Handling multiple users in the same session

Dependencies:

• Pydantic: This library leverages Pydantic for data validation and parsing. Pydantic is a powerful tool in Python for ensuring data integrity and handling data validation with ease.

• HTTPX: The library utilizes HTTPX, a fully featured HTTP client for Python 3, which provides sync and async APIs, and support for both HTTP/1.1 and HTTP/2.

Directus API: This library interacts with the Directus API.

To make the most of this library, it is highly recommended to familiarize yourself with the Directus API documentation. Understanding the API's capabilities and endpoints will help you effectively utilize this library for seamless integration with Directus.

Installation

You can install the library directly from pypi using pip:

$ pip install py-directus

FastAPI support requires additional dependencies installation. You can install them along others like this:

$ pip install py-directus[FastAPI]

Authentication and Session Handling

Login

Create a Directus instance using email and password

from py_directus import Directus

directus = await Directus("https://example.com", email="[e-mail]", password="secret")

Alternatively create a Directus instance using the static token

from py_directus import Directus

directus = await Directus("https://example.com", token="static_token")

Another way is to use the with statement to automatically logout when the session ends

async with Directus(url, email, password) as directus:
    # Manually login
    await directus.login()
    # Manually start cache
    await directus.start_cache()
    # do stuff

# OR

async with await Directus(url, email, password) as directus:
    # do stuff

Refresh Token

If you want to refresh the token you can use the refresh method

await directus.refresh()

Logout

Logout from Directus

await directus.logout()

Multiple Users in the Same Session

You can use multiple users in the same session by creating a new Directus instance by passing the client object

connection = httpx.AsyncClient()
directus1 = await Directus(url, token=token, connection=connection)
directus2 = await Directus(url, email=email, password=password, connection=connection)

Collections

There are two ways to set a collection, either by passing the collection name as a string or by passing the collection as a Pydantic model.

Using the collection method you can pass the collection name as a string

directus.collection("directus_users")

Or you can pass the collection as a Pydantic model

from typing import Optional

from pydantic import ConfigDict

from py_directus.models import DirectusModel


class User(DirectusModel):
    id: Optional[str]
    first_name: Optional[str]
    last_name: Optional[str]
    avatar: Optional[str]
    description: Optional[str]
    email: Optional[str]
    role: Optional[str] | Optional[Role]
    status: Optional[str]
    title: Optional[str]
    token: Optional[str]

    model_config = ConfigDict(collection="directus_users")


directus.collection(User)

Don't forget to set the collection attribute in the model_config attribute

If you go with the second option, you will get the responses as Pydantic models (auto parsing)

The collection method returns a DirectusRequest object which is used to perform READ, CREATE, UPDATE and DELETE operations

Reading Data

When you have the DirectusRequest object you can use the read method to get the data. This will return a DirectusResponse object which contains the data.

Imporatnt note: The read method must be awaited

await directus.collection("directus_users").read()

Filtering

For an easy equality filter you can pass the field name and the value

await directus.collection("directus_users").filter(first_name="John").read()

To add multiple equality filters you can chain the filter method

await directus.collection("directus_users")
.filter(first_name="John")
.filter(last_name="Doe").read()

Using it like this you chain the filters with AND operator

F objects

To define complex logic in filters, use the F object

from py_directus import F

await directus.collection("directus_users")
.filter(
    (F(first_name="John") | F(first_name="Jane")) 
    & F(last_name="Doe")
).read()

Important note: The F object does not support negation

Sorting

You can sort the data by passing the field name to the sort method

await directus.collection("directus_users").sort("first_name", asc=True).read()

To add multiple sorting fields you can chain the sort method

await directus.collection("directus_users")
.sort("first_name", asc=True)
.sort("last_name", asc=False).read()

Limiting

You can limit the data by passing the limit to the limit method

await directus.collection("directus_users").limit(10).read()

Aggregation

Aggregate the number of records in the query

await directus.collection("directus_users").aggregate().read()

# OR

await directus.collection("directus_users").aggregate(count="*").read()

To add multiple aggregates you can chain the aggregate method

await directus.collection("products")
.aggregate(countDistinct="id")
.aggregate(sum="price").read()

Agg objects

You can aggregate the data by defining the needed aggregation with the Agg class and passing it to the aggregate method

from py_directus.aggregator import Agg

agg_obj = Agg(operator=AggregationOperators.Count)

await directus.collection("directus_users").aggregate(agg_obj).read()

In case you need only certain fields

from py_directus.aggregator import Agg

amount_agg = Agg(operator=AggregationOperators.Sum, fields="amount")

await directus.collection("transactions").aggregate(amount_agg).read()

The available aggregation operators are:

• Count
• CountDistinct
• CountAll (Only in GraphQL)
• Sum
• SumDistinct
• Average
• AverageDistinct
• Minimum
• Maximum

Grouping

You can group the data by passing the field names to the group_by method

await directus.collection("directus_users").group_by("first_name", "last_name").read()

Searching

You can search the data by passing the search term to the search method

await directus.collection("directus_users").search("John").read()

Selecting Fields

You can select the fields you want to get by passing the field names to the fields method

await directus.collection("directus_users").fields("first_name", "last_name").read()

Getting the Count Metadata

You can get the count of the data (total count and filtered count) calling include_count

await directus.collection("directus_users").include_count().read()

CRUD

Retrieving items

After you call read() you get a DirectusResponse object which contains the data.

• item for single item
• items for multiple items

Getting the data as a dictionary or a list of dictionaries

response = await directus.collection("directus_users").read()
print(response.item["first_name"])
print(response.items)

If you provide the collection method a Pydantic model you will get the data as a Pydantic object or a list of Pydantic objects

response = await directus.collection(User).read()
print(response.item.first_name)
print(response.items)

Converting to Models (pydantic) or to Dictionary

Apart from the auto parsing, you can manually convert the data to a Pydantic model instance or to a dictionary using:

• item_as(User) or items_as(User)
• item_as_dict() or items_as_dict()

response = await directus.collection("directus_users").read()
print(response.item_as(User))

response = await directus.collection(User).read()
print(response.item_as_dict())

Creating Items

The library does not support Pydantic models for creation, you have to pass a dictionary

• create(items: dict|List[dict])

await directus.collection("directus_users").create({
    "first_name": "John", "last_name": "Doe"
})

# OR

await directus.collection("directus_users").create(
    [
        {"first_name": "John", "last_name": "Doe"},
        {"first_name": "Jane", "last_name": "Doe"}
    ]
)

Updating Items

The library do not support Pydantic models for updating, you have to pass a dictionary

• update(ids: str|int, items: dict)
• update(ids: List[str|int], items: List[dict])

await directus.collection("directus_users").update(1, {
    "first_name": "Red",
    "last_name": "John"
})

# OR

await directus.collection("directus_users").update(
    [1, 2],
    [
        {"first_name": "Jean-Luc"},
        {"first_name": "Jane", "last_name": "Doe"}
    ]
)

Deleting Items

• delete(ids: str|int|List[str|int])

await directus.collection("directus_users").delete(1)

# OR

await directus.collection("directus_users").delete([1, 2])

Supporting Pydantic models for create/update/delete item operations is shortly coming.

Examples

Examples are not included with the pypi package, so you will have to download them separately and execute in a virtual environment.

Run individual examples as such:

python -m examples.<example_file_name>

Tests

Run tests as such:

# All unit tests

python -m unittest discover -s tests/unit

# All integration tests

python -m unittest discover -s tests/integration