bizlens 2.2.3

Integrated Analytics Platform - Descriptive, Diagnostic, and now Advanced Predictive ML with XGBoost & SHAP

BizLens 📊

Fast business descriptive analytics — Polars-first, beautiful charts, and normality comparison

BizLens is a Python analytics library designed for business analysts, teachers, and students. It provides:

• ⚡ Zero-copy performance with Polars (10-100x faster than Pandas)
• 📈 Instant descriptive statistics with Mean, Median, Mode, Skewness, IQR
• 📉 Beautiful visualizations with Matplotlib & Seaborn
• 🔬 Normality comparison — see how your data deviates from normal distribution
• 🎓 Educational-friendly — Interactive demos for learning analytics concepts
• 🌍 Framework-agnostic with Narwhals (works with Polars, Pandas, Dask, etc.)
• 🤖 Advanced ML (v2.2.2) — Full support for XGBoost, SARIMA time-series, and SHAP explainability!

---

Quick Start

Installation

pip install bizlens

Basic Usage

import bizlens as bl
import polars as pl

# Create or load data
df = pl.read_csv("sales_data.csv")

# Instant analysis
bl.describe(df, plots=True, norm_compare=True)

Interactive Demo

import bizlens as bl

# Generate realistic business dataset
demo_data = bl.create_interactive_demo()

# Analyze with visualizations
bl.describe(demo_data, plots=True)

For Teachers & Students

import bizlens as bl

# Load pre-built sample data
data = bl.load_sample_business_data()

# Analyze and save plots as PNG files
bl.describe(data, plots=True, save_plots=True)

---

Why BizLens?

The Problem

Business analysts often assume revenue is "normally distributed," but it's usually log-normal (skewed). Standard averages mislead managers.

The Solution

BizLens shows side-by-side comparisons:

• Left: Your actual data distribution
• Right: How it compares to a standard normal curve

This teaches the "why" behind skewed data in a visually intuitive way.

---

Features

📊 Core Analytics

bl.describe(df, plots=True, bins=30, norm_compare=True)

Returns:

• Shape: Dataset dimensions
• Numeric Stats: Mean, Median, Mode, Std Dev, Range, IQR, Skewness
• Missing Values: Count of NaN per column
• Visualizations: Distribution + Normality plots (optional)

💾 Export Support

# Save analysis to CSV or Excel
bl.describe(df, export="output.csv")
bl.describe(df, export="output.xlsx")

🖼️ Chart Control

# Display plots in Jupyter/interactive mode
bl.describe(df, plots=True, save_plots=False)

# Save plots as PNG files (great for reports)
bl.describe(df, plots=True, save_plots=True)

---

Advanced Usage

Using the BizDesc Class

from bizlens import BizDesc

# Initialize with file path or DataFrame
bd = BizDesc("data.csv")

# Get detailed results
results = bd.summary(
    include_plots=True,
    bins=50,
    norm_compare=True,
    export="analysis.csv"
)

print(results['numeric_stats'])

Working with Different Data Sources

import polars as pl
import pandas as pd
import narwhals as nw

# Polars (zero-copy)
df_pl = pl.read_csv("data.csv")
bl.describe(df_pl)

# Pandas
df_pd = pd.read_csv("data.csv")
bl.describe(df_pd)

# From file path
bl.describe("data.csv")  # Auto-detects CSV
bl.describe("data.xlsx")  # Auto-detects Excel
bl.describe("data.parquet")  # Auto-detects Parquet

---

API Reference

bizlens.describe()

bl.describe(
    data,              # DataFrame or file path
    plots=False,       # Generate visualizations
    export=None,       # Export to file
    bins=30,           # Histogram bins
    norm_compare=True, # Compare to standard normal
    save_plots=False   # Save PNG instead of display
)

bizlens.BizDesc

from bizlens import BizDesc

bd = BizDesc(data)
result = bd.summary(include_plots=True)

bizlens.create_interactive_demo()

Generates sample business dataset with:

• Transaction IDs
• Geographic regions (East, West, North, South)
• Revenue (exponential/skewed distribution)
• Customer satisfaction scores (normal distribution)
• Units sold (Poisson distribution)
• Priority levels (categorical)

bizlens.load_sample_business_data()

Pre-loaded sample dataset (500 rows) for quick demos and testing.

---

Why Polars + Narwhals?

Feature	Pandas	Polars	BizLens
Speed (1M rows)	1x (baseline)	10-100x	⚡ 10-100x
Memory	High	Low	Low
Null handling	⚠️ Complex	✅ Simple	✅ Simple
Type safety	Weak	Strong	Strong
Framework support	Just Pandas	Polars/Dask	All (Narwhals)

---

Installation from Source

git clone https://github.com/yourusername/bizlens.git
cd bizlens
pip install -e ".[all]"

---

Requirements

• Python 3.9+
• polars >= 0.18.0
• narwhals >= 0.8.0
• pandas >= 1.5.0
• numpy >= 1.23.0
• scipy >= 1.9.0
• matplotlib >= 3.6.0
• seaborn >= 0.12.0
• rich >= 13.0.0

---

Testing

# Install with test dependencies
pip install -e ".[all]"

# Run tests
pytest tests/

# Run with coverage
pytest --cov=bizlens tests/

---

Contributing

We welcome contributions! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

---

License

MIT License © 2026 Sudhanshu Singh

---

Acknowledgments

Built with:

• Polars for blazing-fast data processing
• Narwhals for dataframe interoperability
• Rich for beautiful terminal output
• SciPy for statistical functions
• Matplotlib & Seaborn for visualization

---

Support

For questions, issues, or feature requests:

• 🐛 GitHub Issues
• 📧 Email: [e-mail]

---

Happy analyzing! 📊