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Overview

Cube Alchemy transforms your pandas DataFrames into a powerful hypercube, creating a unified semantic layer for multidimensional analysis. This allows you to move from disconnected tables to a coherent analytical model where you can explore data simply and declaratively.

Core Capabilities

  • Automatic Relationships: Discovers relationships between your DataFrames by matching shared column names.

    • Complex Relationship Handling: Handles composite keys and complex relationships transparently.

  • Multidimensional Analytics: Slice, dice, and aggregate your data across any dimension with consistent, reusable metrics and queries that reduce boilerplate code.

  • Stateful Analysis: Maintain a filtering context across queries to easily compare different scenarios.

  • Interactive & Scalable: Works seamlessly in notebook and data apps (Streamlit/Panel).

  • Framework-Agnostic Visualization: Flexible plotting system with a clean renderer interface that works with any visualization framework.

The Semantic Layer

Map your data into a clear and consistent analytical model to work with your hypercube:

  • Dimensions: The "by" of your analysis—the entities you use to slice and dice data (e.g., Customer, Region, Product).

  • Metrics: The key performance indicators (KPIs) you measure (e.g., Total Revenue, Conversion Rate, Average Order Value).

  • Queries: The questions you ask of your data, combining metrics and dimensions to produce insights (e.g., Revenue by Region over Time).

  • Plot Configurations: Visualization definitions that specify how query results should be displayed, with support for multiple views of the same data.

Why It Matters

Build faster, reliable analytics with a fraction of the effort.

  • Accelerate Insights: Get into deep analysis in minutes. Relationships are discovered automatically, not manually coded.

  • Simplify Complexity: Replace ad-hoc joins and messy code with clean, declarative queries that are easy to read and maintain.

  • Ensure Consistency: Standardized metrics and a central data model guarantee that everyone gets reliable, consistent results.

  • Integrate Seamlessly: Designed to work with Streamlit and other Python-based frameworks for building interactive data applications.