ridgeai 0.1.0

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Ridge AI Python

Ridge AI Python client and interactive widgets for Jupyter Lab and Marimo.

Installation

pip install ridgeai

Widgets

Table

Display an interactive data table with profiling and row selection.

import ridgeai as ri

# From a Polars DataFrame
ri.Table(df)

# From a DuckDB connection
ri.Table(conn, table="my_table")

# With a custom query
ri.Table(conn, query="SELECT * FROM my_table WHERE value > 100")

Retrieve Selected Rows

table = ri.Table(df)
table

After having selected one or more rows, you can extract the row values as follows:

table.selected_rows

You can of course also observe changes:

table.observe(names=["selected_rows"])
def on_selection_change(change):
    selection = change["new"]
    print(selection)

Plot

Auto-generate a chart from your data.

import ridgeai as ri

# From a Polars DataFrame (specify columns to plot)
ri.Plot(df, columns=["date", "revenue"])

# From a DuckDB connection
ri.Plot(conn, table="my_table", columns=["x", "y"])

# With a custom query
ri.Plot(conn, query="SELECT date, sum(revenue) FROM sales GROUP BY date")

Dashboard

Render an interactive remote dashboard with AI-powered chat.

import ridgeai as ri

ri.Dashboard(id="your-dashboard-id")

For this to work, you need a Ridge AI API key from https://app.ridgedata.ai.

Set it as an environment variable or in a .env file in your working directory:

RIDGE_AI_API_KEY=rk_your_api_key_here

Alternatively, pass it directly:

ri.Dashboard(id="...", api_key="rk_...")

Local Dashboard

[!WARNING] You can also render a local dashboard if you have the spec handy. However, only do this if you know what you're up to.

import ridgeai as ri

ri.Dashboard(data=df, spec=my_spec)