Retentioneering

Quick Start

This guide walks you through a complete example — from installation to your first interactive visualization — in under five minutes.

1. Install

pip install retentioneering

2. Load your data

Create an Eventstream from a pandas DataFrame. By default, Eventstream expects columns named user_id, event, and timestamp. If your data uses different column names, pass a schema.

import pandas as pd
from retentioneering import Eventstream

df = pd.read_csv("events.csv")
stream = Eventstream(df)

No CSV yet? Use the built-in sample dataset to follow along:

from retentioneering.datasets.ecom import load_ecom
from retentioneering import Eventstream

df = load_ecom()
stream = Eventstream(df, schema={
    "path_cols": ["user_id"],
    "segment_cols": ["platform", "acquisition_channel"],
})

3. Explore with a widget

Open an interactive Transition Graph — no arguments needed. Configure everything in the sidebar.

stream.transition_graph()

Compare two user segments side by side:

stream.transition_graph(diff=["platform", "mobile", "desktop"])

Explore user paths step by step around important events or drop-off points with Step Sankey:

stream.step_sankey(path_pattern=".*->purchase")

or its equivalent Step Matrix:

stream.step_matrix(path_pattern=".*->purchase")

4. Prepare your data

Use data processors to clean and shape the eventstream before visualizing:

stream = (
    Eventstream(df)
    .filter_events(drop={"event": ["bot_ping"]})
    .rename_events({"btn_click": "button_click"})
)

stream.step_sankey()

Next steps