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
import retentioneering as rete

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

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

import retentioneering as rete

stream = rete.datasets.load_ecom()

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 = (
    rete.datasets.load_ecom()
    .filter_events(drop={"event": ["checkout_bug"]})
    .rename_events({"wishlist_add": "add_to_wishlist"})
)

stream.step_sankey()

Every processor returns a new eventstream, so they chain into a pipeline and never modify what they were called on. Note that they also validate event names against your data: dropping or renaming an event that isn't there raises an error instead of silently doing nothing.

Next steps

• Path Analysis — the one page that explains what all of these widgets actually compute. Read this before the rest.
• Eventstream — schema configuration and data format
• Widgets — all available visualizations and how they work
• Data Processors — full list of transformations