bizlens 2.2.13

Educational descriptive analytics + statistical inference + process mining. Rich tables, diagnostic checks, hypothesis testing, and business process analysis for teaching and analysis.

BizLens 📊

Fast descriptive analytics for business + real process mining event logs

BizLens is a Python library for business analysts, data scientists, educators, and students. It provides professional statistical analysis, beautiful visualizations, and special support for business process mining.

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🚀 Quick Start - Try in Google Colab Now!

No installation needed. Click any link below to start learning immediately:

📚 Interactive Tutorials (5-20 minutes each)

Tutorial	Duration	What You'll Learn
Quick Start	5 min	Overview, data quality, diagnostics
Descriptive Analytics	15 min	Tables, distributions, quality checks
Process Mining	15 min	Event logs, workflows, bottlenecks
Statistical Inference	20 min	Hypothesis testing, ANOVA, correlation

All notebooks auto-install BizLens - just run the first cell!

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💾 Installation

Standard Installation

pip install bizlens

With Specific Version

pip install bizlens==2.2.12

For Development

git clone https://github.com/solutiongate-learn/bizlens.git
cd bizlens
pip install -e .

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📦 What's Included

6 Core Modules

Module	Purpose	Key Functions
tables	Statistical tables & distributions	frequency_table, percentile_table, contingency_table, summary_statistics
diagnostic	Data quality & outlier detection	detect_outliers, normality_test, correlation_analysis, missing_value_analysis
inference	Hypothesis testing & confidence intervals	confidence_interval, two_sample_ttest, anova_test, correlation_test
process_mining	Business process analysis	case_metrics, variant_discovery, bottleneck_analysis, rework_detection
quality	Data quality scoring	data_profile, completeness_report, consistency_check
core	Main describe() function	Smart data exploration with Pandas/Polars

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🎯 Use Cases

• Business Analytics: Analyze customer data, sales metrics, process efficiency
• Education: Teach descriptive statistics, hypothesis testing, process analysis
• Data Science: Quick exploratory analysis with publication-ready tables
• Quality Assurance: Detect outliers, assess data completeness, find anomalies
• Process Improvement: Identify bottlenecks, discover process variants, measure efficiency

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💡 Example Usage

Quick Data Exploration

import bizlens as bz
import pandas as pd

# Load your data
df = pd.read_csv('data.csv')

# Smart analysis with one function
bz.describe(df)

Create Statistical Tables

# Frequency distribution
freq = bz.tables.frequency_table(df, 'category')

# Summary statistics
stats = bz.tables.summary_statistics(df[['sales', 'profit']])

# Percentile analysis
percentiles = bz.tables.percentile_table(df[['age']])

Analyze Business Processes

# Detect event logs automatically
metrics = bz.process_mining.case_metrics(event_log)
bottlenecks = bz.process_mining.bottleneck_analysis(event_log)
variants = bz.process_mining.variant_discovery(event_log)

Run Statistical Tests

# Confidence intervals
ci = bz.inference.confidence_interval(data, confidence=0.95)

# Hypothesis testing
result = bz.inference.two_sample_ttest(group1, group2)

# ANOVA for multiple groups
anova = bz.inference.anova_test(df, group_col='category', value_col='metric')

Assess Data Quality

# Overall quality score (0-100)
quality = bz.quality.data_profile(df)

# Detailed completeness report
completeness = bz.quality.completeness_report(df)

# Outlier detection
outliers = bz.diagnostic.detect_outliers(df[['column']], method='iqr')

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📖 Documentation

• API Reference: Each function has detailed docstrings
• Examples: See /examples/ directory for Python scripts
• Notebooks: Run interactive Colab tutorials (links above)
• Source: Full source code in /src/bizlens/

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✨ Key Features

✅ Pandas & Polars compatible - Works with both DataFrames ✅ Auto-install dependencies - All notebooks handle setup automatically ✅ Publication-ready output - Professional tables and visualizations ✅ Educational focus - Clear explanations in every function ✅ Sample datasets included - Learn without external data ✅ Process mining support - Analyze event logs automatically ✅ Statistical rigor - Proper hypothesis testing with effect sizes ✅ Data quality tools - Comprehensive profiling and diagnostics

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🔄 Supported Environments

Environment	Status	Notes
Google Colab	✅ Full	Recommended for quick learning
Jupyter Notebook	✅ Full	Local installation required
JupyterLab	✅ Full	Modern notebook interface
VS Code	✅ Full	With Jupyter extension
Terminal/CLI	✅ Full	Standard Python environments

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📊 Version Info

• Current Version: 2.2.12
• Python: 3.8+
• Status: Production ready
• License: MIT

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🤝 Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Submit a pull request

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📞 Support

• Issues: Report bugs on GitHub Issues
• Questions: Ask in GitHub Discussions
• Feedback: We'd love to hear how you're using BizLens!

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📋 Getting Started Checklist

• Install: pip install bizlens
• Try Colab: Click any tutorial link above
• Explore examples: Check /examples/ directory
• Read docstrings: help(bz.tables.frequency_table)
• Build something: Apply to your own data!

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Made with ❤️ for business analysts, data scientists, and students

GitHub • PyPI • MIT License