bvista 0.1.30

B-Vista: A powerful data visualization and exploration tool for pandas DataFrames.

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📊 B-vista

Visual, Scalable, and Real-Time Exploratory Data Analysis — Built for modern notebooks and the browser.

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What is it?

B-vista is a full-stack Exploratory Data Analysis (EDA) interface for pandas DataFrames. It connects a Flask + WebSocket backend to a dynamic React frontend, offering everything from descriptive stats to missing data diagnostics — in real-time.

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Testing
Package
Meta

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🎯 Designed for
Data Scientists · Analysts · Educators
Teams collaborating over datasets

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📚 Contents

• ✨ Main Features
• 🚀 Quickstart
• 📦 Installation
• 🛠️ Environment & Compatibility
• 📘 Documentation
• 📈 Usage Examples
• 💡 In the News
• 🧑‍💻 Developer Setup
• 📂 Project Structure
• 🤝 Contributing
• 🔖 Versioning
• 📄 License

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✨ Main Features

B-vista transforms how you explore and clean pandas DataFrames. With just a few clicks or lines of code, you get a comprehensive, interactive EDA experience tailored for effecient workflows.

• 📊 Descriptive Statistics
  Summarize distributions with enhanced stats including skewness, kurtosis, Shapiro-Wilk normality, and z-scores—beyond standard .describe().

• 🔗 Correlation Matrix Explorer
  Instantly visualize relationships using Pearson, Spearman, Kendall, Mutual Info, Partial, Robust, and Distance correlations.

• 📈 Distribution Analysis
  Generate histograms, KDE plots, box plots (with auto log-scaling), and QQ plots for deep insight into variable spread and outliers.

• 🧼 Missing Data Diagnostics
  Visualize missingness (matrix, heatmap, dendrogram), identify patterns, and classify gaps using MCAR/MAR/NMAR inference methods.

• 🛠️ Smart Data Cleaning
  Drop or impute missing values with Mean, Median, Mode, Forward/Backward Fill, Interpolation, KNN, Iterative, Regression, or Autoencoder.

• 🔁 Data Transformation Engine
  Cast column types, format as time or currency, normalize/standardize, rename or reorder columns—all with audit-safe tracking.

• 🧬 Duplicate Detection & Resolution
  Automatically detect, isolate, or remove duplicate rows with real-time filtering.

• 🔄 Inline Cell Editing & Updates
  Update any cell in-place and sync live across sessions via WebSocket-powered pipelines.

• 📂 Seamless Dataset Upload
  Drag-and-drop or API-based DataFrame ingestion using secure, session-isolated pickle transport.

🔍 See full feature breakdown →

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Where to get it

the source code is currently hosted on Github at → Source code.

Binary installers for the latest released version are available at the → Python Package Index (PyPI)

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📦 Installation

#PYPI
pip install bvista

#Conda
conda install -c conda-forge bvista

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🚀 Quickstart

The fastest way to get started (in a notebook):

import bvista

df = pd.read_csv("dataset.csv")
bvista.show(df)

Or from the terminal:

git clone https://github.com/Baci-Ak/b-vista.git
cd b-vista
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
pip install -e .
python backend/app.py

Frontend runs separately:

cd frontend
npm install
npm start

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📦 Installation

🧪 From Source (Editable Mode)

git clone https://github.com/Baci-Ak/b-vista.git
cd b-vista
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
pip install -e .

💡 You must also start the frontend — see Frontend Setup

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📦 Conda Environment (Optional)

conda create -n bvista python=3.10
conda activate bvista
pip install -r requirements.txt
pip install -e .

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🔹 PyPI (coming soon)

pip install bvista

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🐳 Docker (planned)

Future releases will support Dockerized deployment.

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🛠️ Environment & Compatibility

Tool	Version
Python	≥ 3.7 (tested on 3.10)
Node.js	^18.x
npm	^9.x

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🧩 Common Setup Fixes

• npm start fails:

  rm -rf node_modules package-lock.json
  npm install
  

• Flask not reachable: check localhost:5050, free port, or restart backend

• WebSocket not connecting: ensure both backend and frontend are live

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📘 Documentation

• Getting Started
• Installation
• Web UI Guide
• Notebook Integration
• API Endpoints
• WebSocket Events
• EDA Features
• Dev Architecture

🔗 See /docs folder for all guides

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📈 Usage Examples

1. Notebook + UI

import pandas as pd
import bvista

df = pd.read_csv("data.csv")
bvista.show(df)

2. API Upload

curl -X POST http://localhost:5050/api/upload \
     -F 'file=@your_file.csv'

3. Trigger WebSocket

socketio.emit("data_update", {"status": "DataFrame updated"})

Full API listed here

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💡 In the News / Inspiration

"B-vista solves the problem of static pandas outputs — it makes DataFrames interactive, shareable, and explorable."
— Community Contributor, Beta Tester

• Inspired by the gaps in tools like D-Tale, Lux, and pandas-profiling
• Designed for real-world data workflows, not just pretty plots
• UI built from scratch for speed, clarity, and scalability

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🔗 Related Resources

• pandas
• D-Tale (Comparative Tool)
• Lux (EDA Assistant)
• Flask-SocketIO
• Vite
• Plotly

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🧑‍💻 Developer Setup & Contributing

Run the Backend

cd backend
python app.py

Run the Frontend

cd frontend
npm install
npm start

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📂 Project Structure

📦 b-vista
├── backend/            → Flask API, WebSocket, models/
├── frontend/           → React app (Vite)
├── bvista/             → Notebook integration module
├── docs/               → Markdown documentation
├── tests/              → Unit & integration tests
├── datasets/           → Sample CSVs for demos
├── requirements.txt
├── setup.py
└── README.md

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🤝 Contributing

We welcome PRs and feedback!
Start here → docs/development/contributing.md

• Dev setup instructions
• Code style & linting
• GitHub Actions (planned)
• Test suite guide

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🔖 Versioning

Follows Semantic Versioning

Current: v0.1.0 (pre-release)

Expect fast iteration and breaking changes until 1.0.0

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📄 License

B-vista is open-source and released under the BSD 3.

Contributions, forks, and usage are welcome — just credit the project 💛

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