# Python experiments installation - Docs

1.  1

    ## Install the package

    Required

    Install the PostHog Python library using pip:

    Terminal

    PostHog AI

    ```bash
    pip install posthog
    ```

2.  2

    ## Initialize PostHog

    Required

    Initialize the PostHog client with your project token and host from your project settings:

    Python

    PostHog AI

    ```python
    from posthog import Posthog
    posthog = Posthog(
        project_api_key='<ph_project_token>',
        host='https://us.i.posthog.com'
    )
    ```

    **Django integration**

    If you're using Django, check out our [Django integration](/docs/libraries/django.md) for automatic request tracking.

3.  3

    ## Implement your experiment

    Required

    Experiments run on top of our feature flags. You can define which version of your code runs based on the return value of the feature flag:

    **Note:** Server-side experiment metrics require you to manually send the feature flag information. See [this tutorial](/docs/experiments/adding-experiment-code.md) for more information.

    ```python
    experiment_flag_value = posthog.get_feature_flag("your-experiment-feature-flag", "user_distinct_id")
    if experiment_flag_value == 'test':
        # Do something differently for this user
    else:
        # It's a good idea to let control variant always be the default behaviour,
        # so if something goes wrong with flag evaluation, you don't break your app.
    ```

4.  4

    ## Run your experiment

    Required

    Once you've implemented the feature flag in your code, you'll enable it for a target audience by creating a new experiment in the PostHog dashboard.

5.  5

    ## Next steps

    Recommended

    Now that you're running experiments, continue with the resources below to learn what else Experiments enables within the PostHog platform.

    | Resource | Description |
    | --- | --- |
    | [Creating an experiment](/docs/experiments/creating-an-experiment.md) | How to create an experiment in PostHog |
    | [Adding experiment code](/docs/experiments/adding-experiment-code.md) | How to implement experiments for all platforms |
    | [Statistical significance](/docs/experiments/statistics-bayesian.md) | Understanding when results are meaningful |
    | [Experiment insights](/docs/experiments/analyzing-results.md) | How to analyze your experiment data |
    | [More tutorials](/docs/experiments/tutorials.md) | Other real-world examples and use cases |

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