cube-alchemy 0.2.4

A lightweight, hypercube engine for multidimensional data analysis

A lightweight hypercube engine for multidimensional analytics on top of pandas.

Why It Matters

Reduce glue code and speed up your analysis so you can focus on insights.

• Speed: Automatic relationship discovery and traversal.

• Simplicity: Declarative queries achieve slicing and dicing in pure Python with less ad‑hoc joins.

• Consistency: Define your queries and use them everywhere with the same logic and filtering behavior.

• Maintainability: Centralized analytical logic in reusable components.

• Integration: Power fully interactive analytics apps using frameworks like Streamlit or Panel, or expose it to a web client.

Installation

Requires Python 3.8+.

cd 'your_new_project_path'
python -m venv venv
venv\Scripts\activate
pip install cube-alchemy

Basic usage

Transform your collection of pandas DataFrames into a cohesive analytical model in three simple steps:

• Connect your data - Add your pandas DataFrames to a Hypercube (relationships will be created automatically).

• Define your metrics, queries and plots.

• Query with ease - Extract insights.

Cube Alchemy connects your data by identifying common column names between DataFrames. These shared columns form the relationships; automatically building bridges between tables. The result is a unified schema you can slice and dice and query in a declarative, simple and intuetive way.

import pandas as pd
from cube_alchemy import Hypercube

# 1) Define DataFrames (nodes)
products = pd.DataFrame({
    'product_id': [1, 2, 3],
    'category': ['Electronics', 'Home', 'Other'],
    'cost': [300.0, 15.0, 500.0],
})

customers = pd.DataFrame({
    'customer_id': [100, 101, 102, 103],
    'customer_name': ['Acme Co', 'Globex', 'Initech', 'Umbrella'],  
    'segment': ['SMB', 'Enterprise', 'SMB', 'Consumer'],
    'region_id': [7, 8, 7, 9],  
})

regions = pd.DataFrame({
    'region_id': [7, 8, 9],
    'region': ['North', 'West', 'South'],
})

calendar = pd.DataFrame({
    'date': ['2024-01-01', '2024-01-02', '2024-01-03', '2024-01-04', '2024-01-05'],  
    'month': ['2024-01', '2024-01', '2024-01', '2024-01', '2024-01'],
})

sales = pd.DataFrame({
    'sale_id': [10, 11, 12, 13, 14, 15],
    'product_id': [1, 1, 2, 3, 2, 1],                        
    'customer_id': [100, 101, 102, 103, 100, 102],           
    'date': ['2024-01-01', '2024-01-02', '2024-01-03', '2024-01-04', '2024-01-05', '2024-01-03'],  
    'promo_code': ['NEW10', 'NONE', 'DISC5', 'NONE', 'DISC5', 'NEW10'],  
    'qty': [2, 1, 4, 3, 5, 2],
    'price': [500.0, 500.0, 25.0, 800.0, 25.0, 500.0],
})

promos = pd.DataFrame({
    'promo_code': ['NEW10', 'DISC5', 'NONE'],
    'promo_type': ['Launch', 'Discount', 'No Promo'],
})

# 2) Build the hypercube
cube = Hypercube({
    'Product': products,
    'Customer': customers,
    'Region': regions,
    'Calendar': calendar,
    'Sales': sales,
    'Promos': promos,
})

# Inspect your new hypercube model (shared columns will connect tables)

cube.visualize_graph(full_column_names=False)

# Define (can be done in YAML, here we expose the direct method)

# 3) Define metrics
cube.define_metric(
    name='Revenue',
    expression='[qty] * [price]',
    aggregation='sum'
)

cube.define_metric(
    name='Units',
    expression='[qty]',
    aggregation='sum'
)

cube.define_metric(
    name='Margin',
    expression='([price] - [cost]) * [qty]',
    aggregation='sum'
)

cube.define_metric(
    name='Number of Sales',
    expression='[sale_id]',
    aggregation='count'
)

# 4) Define query/ies
cube.define_query(
    name="sales analysis",
    dimensions={'region', 'category', 'promo_type'},
    metrics=['Revenue', 'Units', 'Margin', 'Number of Sales']
)

# 5) Execute the query (or queries)
cube.query("sales analysis")

Output:

   region     category promo_type  Revenue  Units  Margin  Number of Sales
0  North  Electronics     Launch   2000.0      4   800.0                2
1  North         Home   Discount    225.0      9    90.0                2
2  South        Other   No Promo   2400.0      3   900.0                1
3   West  Electronics   No Promo    500.0      1   200.0                1

# 6) Apply a filter and query again
cube.filter({'customer_name': ['Initech']})     
cube.query("sales analysis")

Output:

   region     category promo_type  Revenue  Units  Margin  Number of Sales
0  North  Electronics     Launch   1000.0      2   400.0                1
1  North         Home   Discount    100.0      4    40.0                1

Additional features such as filters, custom context states, nested metrics, and plotting integrations are available but omitted here for brevity. See the docs for details.

Full documentation

For concepts, API specs, advanced features, full examples and Streamlit integration see:

• Docs: https://cube-alchemy.info

Full documentation

Visit the github repository created for showing more examples and use cases:

• More examples: https://github.com/cube-alchemy/cube-alchemy-examples

Creator

Created with 🧠 and ☕ by Juan C. Del Monte