### Install Project Dependencies with make

Source: https://github.com/astronomer/dag-factory/blob/main/docs/contributing/howto.md

An alternative method to install all project dependencies, including test dependencies, using a traditional make setup.

```bash
make setup
```

--------------------------------

### Set up Development Environment

Source: https://github.com/astronomer/dag-factory/blob/main/AGENTS.md

Installs dependencies using uv and activates the virtual environment. Ensure uv and hatch are installed first.

```bash
uv sync --dev
source .venv/bin/activate
pre-commit install
```

--------------------------------

### Install Airflow Dependencies

Source: https://github.com/astronomer/dag-factory/blob/main/scripts/airflow3/README.md

Installs necessary dependencies for Airflow 3 using a setup script.

```bash
sh scripts/airflow3/setup.sh
```

--------------------------------

### Install DAG Factory

Source: https://github.com/astronomer/dag-factory/blob/main/docs/getting-started/quick-start-airflow-standalone.md

Install the DAG Factory library into your activated virtual environment.

```command-line
pip install dag-factory
```

--------------------------------

### Install Apache Airflow

Source: https://github.com/astronomer/dag-factory/blob/main/docs/getting-started/quick-start-airflow-standalone.md

Install the Apache Airflow package using pip.

```command-line
pip install apache-airflow
```

--------------------------------

### Start Airflow Standalone

Source: https://github.com/astronomer/dag-factory/blob/main/docs/getting-started/quick-start-airflow-standalone.md

Command to start a standalone Airflow environment with DAG Factory setup.

```command-line
airflow standalone
```

--------------------------------

### Install Project Dependencies with uv

Source: https://github.com/astronomer/dag-factory/blob/main/docs/contributing/howto.md

Use uv to install all project dependencies, including those for testing. This method ensures fast and reproducible builds using the lockfile.

```bash
uv sync --dev
```

--------------------------------

### Run Integration Tests Setup

Source: https://github.com/astronomer/dag-factory/blob/main/AGENTS.md

Sets up the environment for integration tests for a specific Python and Airflow version matrix cell.

```bash
hatch run tests.py3.11-2.9:test-integration-setup
```

--------------------------------

### Install Pre-Commit Hooks

Source: https://github.com/astronomer/dag-factory/blob/main/docs/contributing/howto.md

Install pre-commit hooks to ensure code quality checks are performed before committing changes.

```bash
pre-commit install
```

--------------------------------

### Example .airflowignore Content

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/load_yaml_dags.md

This example shows the syntax and behavior of an `.airflowignore` file used with `load_yaml_dags` to exclude specific YAML files from DAG discovery.

```text
*.yml
!keep.yml
ignored/
backup/**/*.yaml
```

--------------------------------

### List-Based DAG Tasks and Task Groups Example

Source: https://github.com/astronomer/dag-factory/blob/main/docs/migration_guide.md

Example demonstrating list-based configuration for tasks and task groups, promoting better readability and maintenance. Note the use of `dependencies` and `task_group_name`.

```yaml
basic_example_dag:
  schedule: "0 3 * * *"
  start_date:
    __type__: datetime.datetime
    year: 2025
    month: 1
    day: 1
  catchup: false
  task_groups:
    - group_name: "example_task_group"
      tooltip: "this is an example task group"
      dependencies: [task_1]
  tasks:
    - task_id: "task_1"
      operator: airflow.operators.bash.BashOperator
      bash_command: "echo 1"
    - task_id: "task_2"
      operator: airflow.operators.bash.BashOperator
      bash_command: "echo 2"
      dependencies: [task_1]
    - task_id: "task_3"
      operator: airflow.operators.bash.BashOperator
      bash_command: "echo 3"
      dependencies: [task_1]
      task_group_name: "example_task_group"
```

--------------------------------

### Install Airflow from Main Branch

Source: https://github.com/astronomer/dag-factory/blob/main/scripts/airflow3/README.md

Installs Airflow directly from the main branch using a dedicated installation script.

```bash
sh scripts/airflow3/install_from_main.sh
```

--------------------------------

### Example DAG Configuration (List Format)

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/configuring_workflows.md

Demonstrates the recommended list format for defining tasks and task groups in a DAG Factory YAML configuration. This format is more readable and maintainable.

```yaml
--8<-- "dev/dags/example_dag_factory.yml:example_dag_yaml_configuration"
```

--------------------------------

### Example Object Storage Configuration

Source: https://github.com/astronomer/dag-factory/blob/main/docs/features/object_storage.md

This YAML configuration demonstrates how to set up object storage within DAG-Factory. It specifies the type of storage and its associated parameters.

```yaml
--8<-- "dev/dags/example_object_storage.yml"
```

--------------------------------

### KubernetesPodOperator DAG Example

Source: https://github.com/astronomer/dag-factory/blob/main/docs/migration_guide.md

Example of a DAG definition using KubernetesPodOperator. Ensure correct configuration for image, commands, and resource requirements.

```yaml
kubernetes_pod_dag:
  start_date: 2025-01-01
  schedule: "@daily"
  description: "A DAG that runs a simple KubernetesPodOperator task"
  catchup: false
  tasks:
    - task_id: hello-world-pod
      operator: airflow.providers.cncf.kubernetes.operators.pod.KubernetesPodOperator
      config_file: "path/to/kube/config"
      image: "python:3.12-slim"
      cmds: ["python", "-c"]
      arguments: ["print('Hello from KubernetesPodOperator!')"]
      name: "example-pod-task"
      namespace: "default"
      get_logs: true
      container_resources:
        __type__: kubernetes.client.models.V1ResourceRequirements
        limits:
          cpu: "1"
          memory: "1024Mi"
        requests:
          cpu: "0.5"
          memory: "512Mi"
```

--------------------------------

### Timedelta Schedule Example

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/schedule.md

Configure a fixed interval between DAG runs using timedelta. This example schedules the DAG to run every 6 hours.

```yaml
schedule:
  type: timedelta
  value: "6h"

```

--------------------------------

### Start Airflow Project with Astro CLI

Source: https://github.com/astronomer/dag-factory/blob/main/docs/getting-started/quick-start-astro-cli.md

Start your Airflow environment using the Astro CLI. This command sets up the Airflow services and makes them accessible.

```commandline
astro dev start
```

--------------------------------

### RelativeDelta Schedule Example

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/schedule.md

Utilize relativedelta for calendar-aware scheduling. This example triggers the DAG on the first day of every month.

```yaml
schedule:
  type: relativedelta
  value: "1 day"

```

--------------------------------

### Activate Virtual Environment for Main Branch Installation

Source: https://github.com/astronomer/dag-factory/blob/main/scripts/airflow3/README.md

Activates the Airflow virtual environment before installing from the main branch.

```bash
source scripts/airflow3/env.sh

source "$(pwd)/scripts/airflow3/venv-af3/bin/activate"
```

--------------------------------

### Activate Virtual Environment and Run Airflow Standalone

Source: https://github.com/astronomer/dag-factory/blob/main/scripts/airflow3/README.md

Activates the Airflow 3 virtual environment and starts Airflow in standalone mode. This initializes the database and starts the webserver, scheduler, and trigger.

```bash
source "$(pwd)/scripts/airflow3/venv-af3/bin/activate"

airflow standalone
```

--------------------------------

### Create Project Directory and Set Airflow Home

Source: https://github.com/astronomer/dag-factory/blob/main/docs/getting-started/quick-start-airflow-standalone.md

Create a directory for your project and set the AIRFLOW_HOME environment variable. Also, disable loading example DAGs.

```command-line
mkdir dag-factory-quick-start && cd dag-factory-quick-start
export AIRFLOW_HOME=$(pwd)
export AIRFLOW__CORE__LOAD_EXAMPLES=False
```

--------------------------------

### Activate Local Python Environment (Traditional)

Source: https://github.com/astronomer/dag-factory/blob/main/docs/contributing/howto.md

Activate the Python virtual environment created by a traditional setup to make project dependencies accessible.

```bash
source venv/bin/activate
```

--------------------------------

### Example DAG Configuration (Dictionary Format)

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/configuring_workflows.md

Illustrates the legacy dictionary format for defining tasks and task groups in a DAG Factory YAML configuration. This format is supported for backward compatibility.

```yaml
--8<-- "dev/dags/example_dag_factory_tasks_taskgroups_as_dict_format.yml:example_dag_dict_configuration"
```

--------------------------------

### Example DAG with Dataset Outlets and Inlets

Source: https://github.com/astronomer/dag-factory/blob/main/docs/features/datasets.md

Configure a DAG to produce and consume datasets by specifying their locations in the 'outlets' and 'inlets' keys. The consumer DAG will run when all specified datasets are updated.

```yaml
--8<-- "dev/dags/datasets/example_dag_datasets_outlet_inlet.yml"
```

--------------------------------

### Run Local Airflow via Docker

Source: https://github.com/astronomer/dag-factory/blob/main/AGENTS.md

Starts and stops a local Airflow environment using Docker Compose.

```bash
make docker-run
```

```bash
make docker-stop
```

--------------------------------

### DAG with Kubernetes Executor Configuration

Source: https://github.com/astronomer/dag-factory/blob/main/docs/features/custom_python_object.md

Example demonstrating how to define a DAG with Kubernetes-specific executor configurations using the `__type__` feature for custom Python objects.

```yaml
--8<-- "dev/dags/airflow2/example_custom_py_object_dag.yml"
```

--------------------------------

### Cron Schedule Example

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/schedule.md

Use a cron string to define a recurring schedule for your DAG. This example sets the DAG to run daily at midnight.

```yaml
schedule:
  type: cron
  value: "0 0 * * *"

```

--------------------------------

### Configure Provider Callback

Source: https://github.com/astronomer/dag-factory/blob/main/docs/features/callbacks.md

Utilize provider-specific callbacks by specifying the callback function and its arguments. Ensure the provider package is installed and accessible.

```yaml
...
  - task_id: task_4
    operator: airflow.operators.bash_operator.BashOperator
    bash_command: "echo task_4"
    on_failure_callback:
      callback: airflow.providers.slack.notifications.slack.send_slack_notification
      slack_conn_id: slack_conn_id
      text: |
        :red_circle: Task Failed.
        This task has failed and needs to be addressed.
        Please remediate this issue ASAP.
      channel: "#channel"
...
```

--------------------------------

### Asset-Based Triggering (Nested AND/OR)

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/schedule.md

Combine AND and OR conditions for complex asset-based triggering logic. This example triggers if file1 is ready AND (file2 OR file3) are ready.

```yaml
schedule:
  type: assets
  value:
    and:
      - "s3://my-bucket/data/file1.csv"
      - or:
        - "s3://my-bucket/data/file2.csv"
        - "s3://my-bucket/data/file3.csv"

```

--------------------------------

### Clone DAG Factory Repository

Source: https://github.com/astronomer/dag-factory/blob/main/docs/contributing/howto.md

Clone the DAG Factory repository and navigate into its root directory to begin local development setup.

```bash
git clone https://github.com/astronomer/dag-factory.git
cd dag-factory/
```

--------------------------------

### Example Airflow DAG with Params

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/params.md

This YAML snippet demonstrates how to define and use parameters within an Airflow DAG. Use this to configure tasks with dynamic values at runtime.

```yaml
--8<-- "dev/dags/airflow2/example_params.yml"
```

--------------------------------

### Show DAG Factory CLI Version

Source: https://github.com/astronomer/dag-factory/blob/main/docs/features/cli.md

Display the installed version of DAG Factory. Both the CLI and the library share the same version number.

```bash
dagfactory --version
```

--------------------------------

### Conditional Dataset Scheduling with YAML Syntax

Source: https://github.com/astronomer/dag-factory/blob/main/docs/features/datasets.md

Configure conditional dataset scheduling using YAML syntax for clarity. This example demonstrates combining dataset conditions with logical operators.

```yaml
--8<-- "dev/dags/datasets/example_dataset_yaml_syntax.yml"
```

--------------------------------

### Timetable Schedule Example

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/schedule.md

Implement advanced custom trigger logic using Airflow's timetable feature. This requires a custom timetable class.

```yaml
schedule:
  type: timetable
  value: "com.example.MyCustomTimetable"

```

--------------------------------

### Run Integration Tests with Specific Python and Airflow Versions

Source: https://github.com/astronomer/dag-factory/blob/main/docs/contributing/howto.md

Execute integration tests for a specific Python and Airflow version combination. This involves running setup and the main integration test commands.

```bash
hatch run tests.py3.11-2.9:test-integration-setup
hatch run tests.py3.11-2.9:test-integration
```

--------------------------------

### Datasets-Based Triggering

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/schedule.md

Trigger a DAG based on the readiness of specified datasets. This example uses a nested structure for dataset dependencies.

```yaml
schedule:
  type: datasets
  value:
    and:
      - "my_dataset.v1"
      - or:
        - "my_dataset.v2"
        - "my_dataset.v3"

```

--------------------------------

### Example DAG with User-Defined Macros

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/jinja2_template.md

Shows how to define and use custom Jinja2 macros in a YAML-based DAG definition using DAG-Factory's 'user_defined_macros' parameter.

```yaml
--8<-- "dev/dags/airflow3/example_user_defined_macros.yml"
```

--------------------------------

### Example DAG with Built-in Jinja2 Variables

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/jinja2_template.md

Demonstrates the use of Airflow's built-in Jinja2 variables like 'ds', 'run_id', and 'task' within a YAML-based DAG definition.

```yaml
--8<-- "dev/dags/airflow2/example_jinja2_template_dag.yml"
```

--------------------------------

### Asset-Based Triggering with Watchers

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/schedule.md

Trigger a DAG based on asset readiness and include watchers for additional checks. This example triggers when file1 is ready and checks for file2.

```yaml
schedule:
  type: assets
  value:
    and:
      - "s3://my-bucket/data/file1.csv"
    watch:
      - "s3://my-bucket/data/file2.csv"

```

--------------------------------

### Usage of default block in YAML for Dynamic DAG Generation

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/defaults.md

This example shows how the `default` block can be used for dynamic DAG generation. It defines both `default_args` and default tasks, creating a template that can be used to generate multiple DAGs with consistent structures and arguments, while allowing for individual overrides.

```yaml
--8<-- "dev/dags/airflow2/example_dag_factory_default_config.yml"
```

--------------------------------

### Configure Airflow for Local Development

Source: https://github.com/astronomer/dag-factory/blob/main/docs/contributing/howto.md

Set environment variables to configure Apache Airflow's home directory and disable loading example DAGs. This also sets the directory for locating DAG config files.

```bash
export AIRFLOW_HOME=$(pwd)/dev
export AIRFLOW__CORE__LOAD_EXAMPLES=false
export CONFIG_ROOT_DIR=$AIRFLOW_HOME/dags
```

--------------------------------

### Conditional Dataset Scheduling with String Condition

Source: https://github.com/astronomer/dag-factory/blob/main/docs/features/datasets.md

Use a string condition to define complex dataset update requirements for a consumer DAG. This example shows how to specify multiple datasets with logical AND and OR operators.

```yaml
--8<-- "dev/dags/datasets/example_dataset_condition_string.yml"
```

--------------------------------

### Build and Serve Documentation Locally

Source: https://github.com/astronomer/dag-factory/blob/main/docs/contributing/howto.md

Use this command to build and serve the documentation locally for development and review.

```bash
hatch run docs:dev
```

--------------------------------

### Release Documentation to GitHub Pages

Source: https://github.com/astronomer/dag-factory/blob/main/docs/contributing/howto.md

Execute this command to build and deploy the documentation, updating it with the current project version.

```bash
hatch run docs:gh-release
```

--------------------------------

### Build Wheel and Source Distribution

Source: https://github.com/astronomer/dag-factory/blob/main/AGENTS.md

Builds the project's wheel and sdist packages using uv.

```bash
uv build --wheel --sdist
```

--------------------------------

### Create and Activate Virtual Environment

Source: https://github.com/astronomer/dag-factory/blob/main/docs/getting-started/quick-start-airflow-standalone.md

Create and activate a Python virtual environment for your project.

```command-line
python3 -m venv dagfactory_env
source dagfactory_env/bin/activate
```

--------------------------------

### Build and Publish Package to PyPI

Source: https://github.com/astronomer/dag-factory/blob/main/docs/contributing/howto.md

Commands to build the package wheels and source distribution, and then publish it to PyPI. Requires a PyPI token.

```bash
uv build --wheel --sdist
```

```bash
uv publish --token <your-pypi-token>
```

--------------------------------

### Load DAGs with .airflowignore Support

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/load_yaml_dags.md

Demonstrates loading DAGs from a folder while respecting `.airflowignore` rules. Matching YAML files will be skipped during discovery.

```python
from dagfactory import load_yaml_dags

load_yaml_dags(
    globals_dict=globals(),
    dags_folder="/path/to/your/dags",
)

# If /path/to/your/dags/.airflowignore exists, matching YAML files are skipped during discovery.
```

--------------------------------

### Show DAG Factory CLI Help

Source: https://github.com/astronomer/dag-factory/blob/main/docs/features/cli.md

Display help information for the DAG Factory command line, listing available options and commands.

```bash
dagfactory --help
```

--------------------------------

### Instantiate DAGs with default configuration path

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/defaults.md

Demonstrates how to load DAGs from a specific YAML file while specifying a parent directory for merging default configurations. Precedence is given to `defaults.yml` files closer to the DAG YAML.

```python
load_yaml_dags(
   "a/b/c/some_dags.yml",
   defaults_config_path="a"
)
```

--------------------------------

### Build Documentation Strictly

Source: https://github.com/astronomer/dag-factory/blob/main/AGENTS.md

Performs a strict build of the project's documentation.

```bash
hatch run docs:build
```

--------------------------------

### Initialize Airflow Project with Astro CLI

Source: https://github.com/astronomer/dag-factory/blob/main/docs/getting-started/quick-start-astro-cli.md

Create a new directory and initialize your Astro CLI project. This sets up the necessary Airflow files and directories.

```commandline
mkdir dag-factory-quick-start && cd dag-factory-quick-start

astro dev init
```

--------------------------------

### Run Airflow Tests

Source: https://github.com/astronomer/dag-factory/blob/main/scripts/airflow3/README.md

Activates the Airflow environment and virtual environment, then executes the Airflow tests.

```bash
source scripts/airflow3/env.sh

source "$(pwd)/scripts/airflow3/venv-af3/bin/activate"

sh scripts/airflow3/tests.sh
```

--------------------------------

### Run Development Environment with Docker

Source: https://github.com/astronomer/dag-factory/blob/main/docs/contributing/howto.md

Build and run the development environment using Docker. This approach builds a DAG Factory wheel, and code changes require restarting the containers.

```bash
make docker-run
```

--------------------------------

### Run All Unit Tests with All Matrix Combinations

Source: https://github.com/astronomer/dag-factory/blob/main/docs/contributing/howto.md

Execute all defined unit tests across all supported Python and Airflow version matrices using hatch.

```bash
hatch run tests:test-cov
```

--------------------------------

### Named Mapping for Dynamic Tasks

Source: https://github.com/astronomer/dag-factory/blob/main/docs/features/dynamic_tasks.md

Configures custom naming for dynamically mapped tasks using Airflow's 'map_index_template' feature. This example uses a Jinja2 expression with a callable function to set meaningful task names based on extracted data.

```yaml
--8<-- "dev/dags/example_map_index_template.yml"
```

--------------------------------

### Activate Local Python Environment (uv)

Source: https://github.com/astronomer/dag-factory/blob/main/docs/contributing/howto.md

Activate the Python virtual environment created by uv to ensure all project dependencies are available in your current session.

```bash
source .venv/bin/activate
```

--------------------------------

### Define Callback with Parameters

Source: https://github.com/astronomer/dag-factory/blob/main/docs/features/callbacks.md

Configure a callback with keyword arguments directly in the YAML. This allows for dynamic parameter passing to user-defined functions.

```yaml
...

  - task_id: task_2
    operator: airflow.operators.bash_operator.BashOperator
    bash_command: "echo task_2"
    on_success_callback:
      callback: include.custom_callbacks.output_custom_message
      param1: "Task status"
      param2: "Successful!"
...
```

--------------------------------

### DAG Factory YAML Implementation

Source: https://github.com/astronomer/dag-factory/blob/main/docs/comparison/taskflow_api.md

Shows how to implement the PyPI stats workflow using DAG Factory and YAML, referencing the same core Python logic.

```yaml
from __future__ import annotations

from airflow.decorators import dag


@dag(
    schedule=None,
    start_date=pendulum.datetime(2023, 10, 26, tz="UTC"),
    catchup=False,
    tags=["example"],
)
def example_pypi_stats_dagfactory():
    return DAGFactory("example_pypi_stats_dagfactory.yml", globals())

example_pypi_stats_dagfactory()

```

--------------------------------

### Run Static Checks with Hatch

Source: https://github.com/astronomer/dag-factory/blob/main/docs/contributing/howto.md

Execute static code analysis and checks using hatch for a specific Python and Airflow version matrix.

```bash
hatch run tests.py3.11-2.9:static-check
```

--------------------------------

### Advanced Dynamic Task Mapping Usage

Source: https://github.com/astronomer/dag-factory/blob/main/docs/features/dynamic_tasks.md

Illustrates advanced dynamic task mapping techniques including static input mapping, task-generated input mapping using taskflow outputs, and mapping with multiple inputs from different tasks.

```yaml
--8<-- "dev/dags/airflow2/example_taskflow.yml"
```

--------------------------------

### Load DAGs from a Folder

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/load_yaml_dags.md

Use this snippet to load all DAG configurations from YAML files within a specified folder. Ensure the folder path is correctly set.

```python
from dagfactory import load_yaml_dags

# Load DAGs from a folder, e.g. /path/to/dags
load_yaml_dags(globals_dict=globals(), dags_folder="/path/to/your/dags")
```

--------------------------------

### Access PostgreSQL Console

Source: https://github.com/astronomer/dag-factory/blob/main/scripts/airflow3/README.md

Connects to the running PostgreSQL container via the command line using psql.

```commandline
psql --u postgres
```

--------------------------------

### Setting DAG-specific arguments in defaults.yml

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/defaults.md

Explains how to set DAG-specific arguments like `schedule` and `catchup` at the root level of `defaults.yml`, as they are not supported under `default_args`.

```yaml
schedule: 0 1 * * *   # set DAG-specific arguments at the root level
catchup: False

default_args:
   start_date: '2024-12-31'
...
```

--------------------------------

### Load DAGs from a Specific YAML File

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/load_yaml_dags.md

Use this snippet to load DAG configurations from a single, specific YAML file. Provide the exact file path.

```python
from dagfactory import load_yaml_dags

# Load DAGs from a specific YAML file
load_yaml_dags(globals_dict=globals(), config_filepath="/path/to/your/dag_config.yaml")
```

--------------------------------

### Plain Airflow Python DAG Implementation

Source: https://github.com/astronomer/dag-factory/blob/main/docs/comparison/taskflow_api.md

Shows how to implement the PyPI stats workflow using Airflow's TaskFlow API decorators directly in Python.

```python
from __future__ import annotations

import pendulum

from airflow.decorators import dag, task


@dag(
    schedule=None,
    start_date=pendulum.datetime(2023, 10, 26, tz="UTC"),
    catchup=False,
    tags=["example"],
)
def example_pypi_stats_plain_airflow():
    @task
    def get_pypi_stats(package_name: str) -> dict:
        """Fetches PyPI stats for a given package."""
        import requests

        response = requests.get(f"https://api.pypistats.org/v1/quick/{package_name}")
        response.raise_for_status()
        return response.json()

    @task
    def summarize_stats(stats: dict) -> str:
        """Summarizes the fetched PyPI stats into a Markdown table."""
        from io import StringIO

        output = StringIO()
        output.write("| package_name | last_day | last_month | last_week |
")
        output.write("|:-----------------|-----------:|-------------:|------------:|
")

        for package_data in stats["data"]["rows"]:
            output.write(
                f"| {package_data['package_name']} | {package_data['last_day']} | {package_data['last_month']} | {package_data['last_week']} |\n"
            )
        return output.getvalue()

    package_names = ["apache-airflow", "astronomer-cosmos", "dag-factory"]
    stats = get_pypi_stats.partial(package_names=package_names).expand()
    markdown_table = summarize_stats(stats)

example_pypi_stats_plain_airflow()

```

--------------------------------

### Define Dynamic Tasks with YAML

Source: https://github.com/astronomer/dag-factory/blob/main/docs/features/dynamic_tasks.md

This configuration defines a 'request' task to generate a list of items and a 'process' task that dynamically creates a task for each item in the list. Use 'expand' for runtime task generation and 'partial' for fixed parameters.

```yaml
--8<-- "dev/dags/airflow2/example_dynamic_task_mapping.yml"
```

--------------------------------

### Load DAGs from YAML with Custom Defaults Path

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/load_yaml_dags.md

Load DAGs from a specific YAML file while specifying a custom root directory for default configurations. This is useful when default configurations are not in the same directory as the DAG config file.

```python
from dagfactory import load_yaml_dags

# Load a single DAG from a YAML file with a custom defaults root directory
load_yaml_dags(
    globals_dict=globals(),
    config_filepath="/path/to/your/dag_config.yaml",
    defaults_config_path="/path/to/your/config-root",
)
```

--------------------------------

### Run Unit Tests with Specific Python and Airflow Versions

Source: https://github.com/astronomer/dag-factory/blob/main/docs/contributing/howto.md

Execute unit tests for a specific Python and Airflow version combination. This command uses hatch to run tests with coverage.

```bash
hatch run tests.py3.10-2.9:test-cov
```

--------------------------------

### Load DAGs from a Dictionary

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/load_yaml_dags.md

Use this snippet to load DAG configurations directly from a Python dictionary. This is useful for dynamically generating DAGs or when configurations are managed in memory.

```python
from dagfactory import load_yaml_dags

# Load DAGs from a dictionary configuration
dag_config_dict = {
    # Your DAG configuration here
}
load_yaml_dags(globals_dict=globals(), config_dict=dag_config_dict)
```

--------------------------------

### Define Callback by String Reference

Source: https://github.com/astronomer/dag-factory/blob/main/docs/features/callbacks.md

Use this method to reference a pre-defined Python function by its import path. Ensure the file containing the function is on the Python sys.path.

```yaml
...

  - task_id: task_1
    operator: airflow.operators.bash_operator.BashOperator
    bash_command: "echo task_1"
    on_failure_callback: include.custom_callbacks.output_standard_message
...
```

--------------------------------

### Use Airflow's direct equivalents for timeout and delay parameters

Source: https://github.com/astronomer/dag-factory/blob/main/docs/migration_guide.md

Replace deprecated YAML parameters like dagrun_timeout_sec, retry_delay_sec, and sla_secs with Airflow's direct equivalents: dagrun_timeout, retry_delay, and sla.

```yaml
dagrun_timeout:
  __type__: datetime.timedelta
  seconds: 300
```

```yaml
retry_delay:
  __type__: datetime.timedelta
  seconds: 10
```

--------------------------------

### Define Callback by File and Function Name

Source: https://github.com/astronomer/dag-factory/blob/main/docs/features/callbacks.md

Specify a callback by providing the full path to the Python file and the function name. This is useful when the callback's directory is not in sys.path.

```yaml
...

  - task_id: task_3
    operator: airflow.operators.bash_operator.BashOperator
    bash_command: "echo task_3"
    on_retry_callback_name: output_standard_message
    on_retry_callback_file: /usr/local/airflow/include/custom_callbacks.py
...
```

--------------------------------

### Run Integration Tests

Source: https://github.com/astronomer/dag-factory/blob/main/AGENTS.md

Executes integration tests for a specific Python and Airflow version matrix cell.

```bash
hatch run tests.py3.11-2.9:test-integration
```

--------------------------------

### HttpSensor with External Response Check File

Source: https://github.com/astronomer/dag-factory/blob/main/docs/features/http_task.md

Configure HttpSensor to use a Python file for response checking. Ensure the specified file exists and contains the `check_sensor` function.

```yaml
- task_id: task_2
  operator: airflow.providers.http.sensors.http.HttpSensor
  http_conn_id: 'test-http'
  method: 'GET'
  response_check_name: check_sensor
  response_check_file: /path/to/example1/http_conn.py
  dependencies: [task_1]
```

--------------------------------

### Create DAGs Folder

Source: https://github.com/astronomer/dag-factory/blob/main/docs/getting-started/quick-start-airflow-standalone.md

Create a 'dags' folder within your AIRFLOW_HOME directory to store DAG files.

```command-line
mkdir dags
```

--------------------------------

### Update Project Version (Minor)

Source: https://github.com/astronomer/dag-factory/blob/main/docs/contributing/howto.md

Use this hatch command to increment the project's version by a minor release.

```bash
hatch version minor
```

--------------------------------

### Load KubernetesPodOperator DAG Configuration Dynamically

Source: https://github.com/astronomer/dag-factory/blob/main/docs/features/kpo.md

Load the YAML configuration dynamically in Python to create and run Pods on a Kubernetes cluster using the Airflow Kubernetes Provider. Ensure the YAML file is correctly referenced.

```python
--8<-- "dev/dags/kpo.py"
```

--------------------------------

### Python File Usage of default_args_config_dict

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/defaults.md

Shows how to use the `default_args_config_dict` parameter within a Python file to configure default arguments for DAGs generated by DagFactory.

```python
--8<-- "dev/dags/example_dag_factory_default_config_dict.py:13:19"
```

--------------------------------

### Run Pre-Commit Checks Manually

Source: https://github.com/astronomer/dag-factory/blob/main/docs/contributing/howto.md

Manually run all pre-commit checks across all files in the repository to verify code quality and style.

```bash
pre-commit run --all-files
```

--------------------------------

### Defining default_args in defaults.yml

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/defaults.md

Illustrates how to keep `default_args` within the `defaults.yml` file, which will be applied to all DAG Factory-generated DAGs. Use with caution as these settings affect all DAGs.

```yaml
--8<-- "dev/dags/defaults.yml"
```

--------------------------------

### Pull Postgres Image

Source: https://github.com/astronomer/dag-factory/blob/main/scripts/airflow3/README.md

Pulls the official PostgreSQL Docker image and creates a container named 'postgres'.

```commandline
docker run --name postgres -p 5432:5432 -p 5433:5433 -e POSTGRES_PASSWORD=postgres postgres
```

--------------------------------

### Define timetable using '__type__' annotation

Source: https://github.com/astronomer/dag-factory/blob/main/docs/migration_guide.md

Custom parsing for 'timetable' is removed. Use the '__type__' annotation to define timetables in YAML, specifying the full Python path and arguments.

```yaml
timetable:
  __type__: airflow.timetables.trigger.CronTriggerTimetable
  __args__:
    - 0 1 * * 3
```

--------------------------------

### Consolidate logical keys in YAML configuration

Source: https://github.com/astronomer/dag-factory/blob/main/docs/migration_guide.md

Replace logical and join keys like '!and', '!or', '!join', 'and', 'or' with their consolidated equivalents: '__and__', '__or__', '__join__'.

--------------------------------

### Load Asset Triggered DAGs Dynamically

Source: https://github.com/astronomer/dag-factory/blob/main/docs/features/asset.md

This Python script dynamically loads DAG configurations defined in YAML, enabling the use of asset-triggered DAGs. Ensure the YAML file is correctly referenced.

```python
--8<-- "dev/dags/asset_triggered_dags.py"
```

--------------------------------

### Opt-out of Telemetry via Environment Variables

Source: https://github.com/astronomer/dag-factory/blob/main/PRIVACY_NOTICE.md

Alternatively, opt out of analytics by setting either DO_NOT_TRACK to True or SCARF_NO_ANALYTICS to True.

```shell
DO_NOT_TRACK=True
```

```shell
SCARF_NO_ANALYTICS=True
```

--------------------------------

### Define KubernetesPodOperator DAG Configuration

Source: https://github.com/astronomer/dag-factory/blob/main/docs/features/kpo.md

Define your DAG configuration in a YAML file to specify Kubernetes Pod settings. This is the first step in setting up a KPO task.

```yaml
--8<-- "dev/dags/kpo.yml"
```

--------------------------------

### Load DAGs from Dictionary with Custom Defaults Dictionary

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/load_yaml_dags.md

Load DAGs from a Python dictionary, providing custom default arguments also defined in a dictionary. This allows for fine-grained control over default DAG parameters.

```python
from dagfactory import load_yaml_dags

# Load DAGs with custom default arguments from a dictionary
default_args_dict = {
    # Your default arguments
}

dag_config_dict = {
    # Your DAG configuration here
}
load_yaml_dags(
    globals_dict=globals(),
    config_dict=dag_config_dict,
    defaults_config_dict=default_args_dict,
)
```

--------------------------------

### Usage of default block for default_args in YAML

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/defaults.md

This snippet demonstrates how to use the `default` block to specify `default_args` that will be inherited by all DAGs defined in the same YAML file. This ensures consistent arguments across multiple DAGs.

```yaml
--8<-- "dev/dags/airflow2/example_dag_factory_default_args.yml"
```

--------------------------------

### Set Environment Variables for Integration Tests

Source: https://github.com/astronomer/dag-factory/blob/main/docs/contributing/howto.md

Configure necessary environment variables before running integration tests. This includes setting Airflow timeouts, home directory, and Python path.

```bash
export AIRFLOW__CORE__DAGBAG_IMPORT_TIMEOUT=90
export AIRFLOW_HOME=$(pwd)/dev/
export CONFIG_ROOT_DIR=$(pwd)/dev/dags
export PYTHONPATH=$(pwd)/dev/dags:$PYTHONPATH
```

--------------------------------

### General YAML Format for Python Objects

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/custom_py_object.md

Defines a generalized object in YAML. Use '__type__' for the Python import path and other keys for arguments or attributes. '__args__' is used for positional arguments.

```yaml
object_name:
  __type__: <python.import.path.ClassName>
  <attribute1>: <value1>
  <attribute2>: <value2>
  ...
```

--------------------------------

### Convert DAGs with CLI

Source: https://github.com/astronomer/dag-factory/blob/main/docs/features/cli.md

Use the convert command to update DAG files to the latest Airflow 3 syntax. This command processes specified directories and reports on changes made.

```bash
dagfactory convert dev/dags/airflow3
```

--------------------------------

### DAG Factory YAML Loading Script

Source: https://github.com/astronomer/dag-factory/blob/main/docs/comparison/taskflow_api.md

A Python script that loads and registers DAGs defined in YAML files using DAG Factory.

```python
from __future__ import annotations

import pendulum

from airflow.decorators import dag
from dag_factory import DAGFactory


@dag(
    schedule=None,
    start_date=pendulum.datetime(2023, 10, 26, tz="UTC"),
    catchup=False,
    tags=["example"],
)
def example_load_yaml_dags():
    return DAGFactory("example_pypi_stats_dagfactory.yml", globals())


example_load_yaml_dags()

```

--------------------------------

### Define Datetime Object in Python

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/custom_py_object.md

Instantiate a Python datetime object. This is useful for setting specific dates in configurations.

```python
from datetime import datetime
start_date = datetime(2025, 1, 1)
```

--------------------------------

### Define Airflow Asset Object in Python

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/custom_py_object.md

Instantiate an Airflow Asset object. Use this to define data assets with names and URIs.

```python
from airflow.sdk import Asset
my_asset = Asset(name="sales_report", uri="s3://data/object_storage_ops.csv")
```

--------------------------------

### Define ObjectStoragePath in YAML

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/custom_py_object.md

Represent an ObjectStoragePath object in YAML format using positional arguments. This allows configuration of object storage paths.

```yaml
my_obj_storage:
  __type__: airflow.io.path.ObjectStoragePath
  __args__:
    - file:///data/object_storage_ops.csv
```

--------------------------------

### Asset-Based Triggering (AND Condition)

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/schedule.md

Explicitly define an AND condition to trigger a DAG only when all specified assets are ready.

```yaml
schedule:
  type: assets
  value:
    and:
      - "s3://my-bucket/data/file1.csv"
      - "s3://my-bucket/data/file2.csv"

```

--------------------------------

### Define Timedelta Object in Python

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/custom_py_object.md

Instantiate a Python timedelta object. Use this to specify durations or time differences in configurations.

```python
from datetime import timedelta
execution_timeout = timedelta(hours=1)
```

--------------------------------

### Load DAGs with Suffix Configuration

Source: https://github.com/astronomer/dag-factory/blob/main/docs/features/multiple_configuration_files.md

Use `load_yaml_dags` with a suffix to load relevant YAML files as Airflow DAGs. This is useful for organizing complex DAG structures into multiple configuration files.

```python
from dagfactory import load_yaml_dags  # load relevant YAML files as airflow DAGs

load_yaml_dags(globals_dict=globals(), suffix=['dag.yaml'])
```

--------------------------------

### Define ObjectStoragePath in Python

Source: https://github.com/astronomer/dag-factory/blob/main/docs/configuration/custom_py_object.md

Instantiate an ObjectStoragePath object using positional arguments. This is for specifying object storage locations.

```python
from airflow.io.path import ObjectStoragePath
my_obj_storage = ObjectStoragePath("file:///data/object_storage_ops.csv")
```

--------------------------------

### DAG Factory YAML for Hacker News

Source: https://github.com/astronomer/dag-factory/blob/main/docs/comparison/traditional_operators.md

Defines the Hacker News workflow using DAG Factory's YAML syntax. This demonstrates an alternative to traditional Python DAGs.

```yaml
from __future__ import annotations

import pendulum

from airflow.decorators import dag


@dag(
    dag_id="example_hackernews_dagfactory",
    schedule=None,
    start_date=pendulum.datetime(2023, 10, 24, tz="UTC"),
    catchup=False,
    tags=["example"],
)
def example_hackernews_dagfactory():
    pass


example_hackernews_dagfactory_dag = example_hackernews_dagfactory()

```

--------------------------------

### Lint YAML Files with DAG Factory

Source: https://github.com/astronomer/dag-factory/blob/main/docs/features/cli.md

Check if the given directory or file contains valid YAML. Use the --verbose flag for detailed results, including syntax errors.

```bash
dagfactory lint some/dir --verbose
```

--------------------------------

### Create Airflow Database

Source: https://github.com/astronomer/dag-factory/blob/main/scripts/airflow3/README.md

Creates a new database named 'airflow_db' within the psql interactive terminal.

```sql
CREATE DATABASE airflow_db;
```

--------------------------------

### Define Asset Triggered DAGs Configuration

Source: https://github.com/astronomer/dag-factory/blob/main/docs/features/asset.md

This YAML file defines the configuration for DAGs that are triggered by asset events. It specifies the DAGs to load and their associated configurations.

```yaml
--8<-- "dev/dags/asset_triggered_dags.yml"
```