Anyone can use https://pypistats.org/ to see the downloads for a given PyPI package. However, the statistics and visualizations provided by the website are limited. The purpose of this project is to provide a better alternative with Ibis, ClickHouse, and ML-powered predictive analytics.

Then entire pypi dataset is approaching a trillion rows of data. Fortunately, with the aggregated data provided by ClickHouse on a public playground instance and Ibis for transformation, we can easily process this data in an embedded dashboard application.

WARNING: work in progress

import ibis

ibis.options.interactive = True

host = "clickpy-clickhouse.clickhouse.com"
port = 443
user = "play"
database = "pypi"

con = ibis.clickhouse.connect(
  host=host,
  port=port,
  user=user,
  database=database,
)

con.list_tables()

We need to finalize on a dashboarding framework (Quarto dashboard has some issues with interactive inputs and plotly), replicate the existing PyPI stats visualizations, add new visualizations, and more.

A user should be able to input their package name and see all relevant stats, then be able to drill into specific metrics and visualizations for a given package.

Using IbisML and time-series forecasting frameworks, we can predict the downloads over time and show visualizations for these predictions.