pyanalytica 0.2.0

A Python analytics workbench for teaching data science

PyAnalytica

A Python analytics workbench for teaching data science. Built on Shiny for Python.

Features

• Interactive data exploration and visualization — profile, transform, pivot, crosstab
• Guided statistical analysis — group means, proportions, correlation, chi-squared
• Machine learning — regression, classification, model evaluation, prediction
• AI-powered insights — optional Anthropic integration for interpretation, suggestions, and natural-language queries
• Homework framework — YAML-based assignments with hash-checked grading for instructors
• Report generation — export analyses as HTML, Python scripts, or Jupyter notebooks
• Procedure builder — record, replay, and export multi-step analysis workflows
• Bundled datasets — ready-to-use data for classroom exercises

Installation

pip install pyanalytica

Optional extras:

pip install pyanalytica[ai]      # Anthropic AI integration
pip install pyanalytica[report]  # Jupyter notebook export
pip install pyanalytica[all]     # Everything
pip install pyanalytica[dev]     # Development and testing

Quick Start

Launch the interactive workbench

pyanalytica                # CLI entry point
python -m pyanalytica      # or as a module

Use as a Python library

Every analytics function returns a (result, CodeSnippet) tuple. The CodeSnippet contains the equivalent pandas/sklearn code so students can see what runs under the hood.

from pyanalytica.data.load import load_bundled
from pyanalytica.data.profile import profile_dataframe
from pyanalytica.visualize.distribute import histogram
from pyanalytica.visualize.relate import scatter
from pyanalytica.explore.summarize import group_summarize

# Load a bundled dataset
df, code = load_bundled("tips")

# Profile the dataframe
profile = profile_dataframe(df)

# Visualize
fig, code = histogram(df, "total_bill", bins=20)
fig, code = scatter(df, x="total_bill", y="tip", color_by="smoker")

# Summarize
result, code = group_summarize(df, group_cols=["day"], agg_col="tip", agg_func="mean")

Bundled Datasets

Name	Rows	Description
tips	244	Restaurant tipping data
diamonds	~54,000	Prices and attributes of diamonds
candidates	5,000	JobMatch recruiting simulation — job candidates
jobs	500	JobMatch recruiting simulation — job postings
companies	200	JobMatch recruiting simulation — companies
events	—	JobMatch recruiting simulation — recruiting events

from pyanalytica.datasets import list_datasets, load_dataset

list_datasets()          # ['candidates', 'companies', 'diamonds', 'events', 'jobs', 'tips']
df = load_dataset("diamonds")

Development

git clone https://github.com/social-engineer-ai/PyAnalytica.git
cd PyAnalytica
pip install -e ".[dev,all]"
python -m pytest tests/ -v

License

MIT License. See LICENSE for details.