Metadata-Version: 2.1
Name: abtestools
Version: 1.1.0
Summary: AB Test Framework for Python
License: MIT
Author: Pablo Minué
Requires-Python: >=3.10,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: pandas (>=2.2.3,<3.0.0)
Requires-Dist: pydantic (>=2.9.2,<3.0.0)
Requires-Dist: rich (>=13.9.2,<14.0.0)
Requires-Dist: scipy (>=1.14.1,<2.0.0)
Requires-Dist: uuid (>=1.30,<2.0)
Description-Content-Type: text/markdown


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  <h3 align="center">pyabtesting</h3>

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      <a href="#about-the-project">About The Project</a>
    </li>
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      <a href="#getting-started">Getting Started</a>
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        <li><a href="#prerequisites">Prerequisites</a></li>
        <li><a href="#installation">Installation</a></li>
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    <li><a href="#usage">Usage</a></li>
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<!-- ABOUT THE PROJECT -->
## About The Project

This library provides tools for AB Testing, very useful when working with marketing data

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## Implementation

```python
import datetime

import pandas as pd

from abtestools.audiences import Audience
from abtestools.campaign import Campaign
from abtestools.test import Metric, Test

data = pd.read_csv("tests/cookie_cats.txt", delimiter=",")
data["version"] = data["version"].map({"gate_30": "control", "gate_40": "test"})
print(data.groupby("version")["retention_1"].sum())

audience = Audience(
    users=data["userid"], group_mapping=dict(zip(data["userid"], data["version"]))
)

campaign = Campaign(
    audience=audience,
    metrics=[
        Metric(name="retention_1", type="discrete"),
        Metric(name="retention_7", type="discrete"),
    ],
    date_range=[
        datetime.datetime.today() - datetime.timedelta(days=x) for x in range(10)
    ],
)


def extract_data(date, metric_column: str, convert_bool: bool = True) -> dict:
    if convert_bool:
        data[metric_column] = data[metric_column].astype(int)
    return dict(zip(data["userid"], data[metric_column]))


for res in campaign.backfill(
    metric=Metric(name="retention_1", type="discrete"),
    extract_data=extract_data,
    metric_column="retention_1",
):
    print(res)

```

<!-- GETTING STARTED -->
## Getting Started

### Prerequisites

To develop on this project, you will need to create a poetry environment with the needed dependencies
* install with Poetry
  ```sh
  poetry install
  ```

### Installation


1. 
   ```sh
   git clone https://github.com/pablominue/pyabtesting.git
   ```
   or
   ```sh
   pip install abtestools
   ```

3. Import main modules
   ```python
   from abtestools import audience, test
   ```


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_For more examples, please refer to the [Documentation](https://example.com)_

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See the [open issues](https://github.com/pablominue/pyabtesting/issues) for a full list of proposed features (and known issues).

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Distributed under the MIT License. See `LICENSE.txt` for more information.

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