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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).

The Semantic Layer

Map your data into a clear and consistent set of analytical assets 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).

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.