bizlens 2.2.16

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

BizLens 📊

Business analytics, statistical inference, and process mining — all in one package

BizLens is a Python library for business analysts, data scientists, educators, and students. It delivers professional statistical analysis, beautiful Rich tables, and built-in support for business process mining — all with a single pip install.

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🚀 Open in Google Colab — No Installation Needed

Click any link to launch a notebook instantly in your browser:

Core Analytics

Notebook	Colab Link	What You'll Learn
Quick Start		Overview, frequency tables, outlier detection
Descriptive Analytics		Frequency, percentile, contingency, data profile
Statistical Inference		Confidence intervals, t-tests, ANOVA, correlation
Chi-Square & Association		Chi-square, contingency tables, Cramér's V
Probability & Distributions		Distribution fitting, simulation, sampling

Machine Learning

Notebook	Colab Link	What You'll Learn
Linear & Multiple Regression		OLS regression, diagnostics, predictions
Logistic Regression		Binary classification, ROC, confusion matrix
Decision Trees & Random Forests		Tree models, feature importance, ensembles
PCA & Clustering		Dimensionality reduction, K-Means, DBSCAN
Conjoint Analysis		Preference modeling, attribute utilities
Q-Learning		Reinforcement learning basics, Q-table

Process Mining

Notebook	Colab Link	What You'll Learn
Process Mining		Case metrics, bottlenecks, variants, resources
Process Mining (Advanced)		BizLens + optional pm4py integration

All notebooks auto-install BizLens on first run — just click the Colab badge and run the first cell.

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💾 Installation

pip install bizlens

# With process mining extras (pm4py)
pip install bizlens[process-mining]

# Latest from source
git clone https://github.com/solutiongate-learn/bizlens.git
cd bizlens && pip install -e .

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📦 Modules at a Glance

Module	Key Functions
bl.tables	frequency_table, percentile_table, contingency_table, summary_statistics
bl.diagnostic	detect_outliers, normality_test, correlation_analysis, missing_value_analysis
bl.inference	confidence_interval, two_sample_ttest, anova_test, paired_ttest, correlation_test
bl.process_mining	case_metrics, variant_discovery, bottleneck_analysis, resource_analysis
bl.quality	data_profile, completeness_report, consistency_check
bl.describe()	Smart all-in-one data exploration

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💡 Quick Examples

import bizlens as bl
import pandas as pd

# Load a built-in teaching dataset (returns pandas DataFrame)
df = bl.load_dataset('titanic')   # or 'tips', 'iris', 'penguins', 'diamonds', 'mpg'

# Frequency distribution
bl.tables.frequency_table(df['sex'])

# Summary statistics
bl.tables.summary_statistics(df[['age', 'fare']])

# Contingency table with chi-square test
table, stats = bl.tables.contingency_table(df, 'sex', 'survived')
print(f"Chi² = {stats['chi2']:.3f}, p = {stats['p_value']:.4f}, Cramér's V = {stats['cramers_v']:.3f}")

# Data quality profile
bl.quality.data_profile(df)

# Confidence interval
import numpy as np
bl.inference.confidence_interval(df['age'].dropna(), confidence=0.95)

# Two-sample t-test
bl.inference.two_sample_ttest(df[df['sex']=='male']['fare'], df[df['sex']=='female']['fare'])

Process Mining

# Load a built-in event log (returns pandas DataFrame)
event_log = bl.generate_hr_onboarding_event_log()

bl.process_mining.case_metrics(event_log)
bl.process_mining.bottleneck_analysis(event_log)
bl.process_mining.variant_discovery(event_log)
bl.process_mining.resource_analysis(event_log, resource_col='resource')

Polars Users

# BizLens also accepts polars DataFrames
import polars as pl
df_pl = pl.from_pandas(bl.load_dataset('titanic'))
bl.tables.frequency_table(df_pl['sex'])     # works transparently
bl.quality.data_profile(df_pl)              # works transparently

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🔄 Supported Environments

Environment	Status
Google Colab	✅ Recommended
Jupyter Notebook / JupyterLab	✅ Full
VS Code (Jupyter extension)	✅ Full
Terminal / scripts	✅ Full

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📊 Version

• Current: 2.2.13
• Python: 3.9+
• License: MIT

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📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• PyPI: pypi.org/project/bizlens

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