quickinsights 0.2.0

A creative and innovative Python library for data analysis with single command

QuickInsights

A creative and innovative Python library for data analysis that goes beyond basic libraries like NumPy and Pandas. Provides advanced features for big data analysis with a single command.

What is it?

QuickInsights is a Python package that provides comprehensive data analysis capabilities through an intuitive interface. It aims to be a powerful tool for data scientists, analysts, and researchers who need to perform complex data analysis tasks efficiently.

Main Features

• Comprehensive Data Analysis: Single-command data set analysis with detailed insights
• Advanced Visualization: Integration with Matplotlib, Seaborn and Plotly for professional charts
• Performance Optimization: Lazy evaluation, caching, parallel processing for large datasets
• Big Data (Dask): Intelligent distributed analysis and pipelines
• Unique Modules: Neural pattern mining, quantum-inspired sampling and correlation, holographic 3D projections
• Cloud Integration: Support for AWS S3, Azure Blob, Google Cloud Storage
• AI-Powered Insights: Automatic pattern detection and trend analysis using machine learning
• Real-time Pipeline: Streaming data processing capabilities
• Modular Architecture: Easily extensible and customizable framework

Installation

From PyPI (Recommended)

pip install quickinsights

From Test PyPI (Developer Version)

pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ quickinsights

From Source

git clone https://github.com/erena6466/quickinsights.git
cd quickinsights
pip install -e .

Quick Start

import quickinsights as qi
import pandas as pd

# Sample dataset
df = pd.DataFrame({
    'A': [1, 2, 3, 4, 5],
    'B': [4, 5, 6, 7, 8],
    'C': ['a', 'b', 'a', 'b', 'a']
})

# Comprehensive analysis with single command
result = qi.analyze(df, show_plots=True, save_plots=True)

# Dataset information
info = qi.get_data_info(df)

# Outlier detection
outliers = qi.detect_outliers(df)

# Performance optimization
optimized_df = qi.memory_optimize(df)

Advanced Usage

AI-Powered Analysis

from quickinsights.ai_insights import AIInsightEngine

ai_engine = AIInsightEngine(df)
insights = ai_engine.get_insights()
trends = ai_engine.predict_trends()

Cloud Integration

# Upload to AWS S3
qi.upload_to_cloud('data.csv', 'aws', 'my-bucket/data.csv', bucket_name='my-bucket')

# Process data from cloud
result = qi.process_cloud_data('aws', 'my-bucket/data.csv', processor_func, bucket_name='my-bucket')

Real-time Pipeline

from quickinsights.realtime_pipeline import RealTimePipeline

pipeline = RealTimePipeline()
pipeline.add_transformation(lambda x: x * 2)
pipeline.add_filter(lambda x: x > 10)
results = pipeline.process_stream(data_stream)

New Unique Modules (Highlights)

Neural Patterns

from quickinsights import neural_pattern_mining, autoencoder_anomaly_scores, sequence_signature_extract
patterns = neural_pattern_mining(df, n_patterns=5)
anoms = autoencoder_anomaly_scores(df)
sigs = sequence_signature_extract(df.select_dtypes(float).iloc[:, 0], window=128, step=32, n_components=3)

Quantum-Inspired

from quickinsights import quantum_superposition_sample, amplitude_pca, quantum_correlation_map
sample = quantum_superposition_sample(df, n_samples=5000)
pca = amplitude_pca(df, n_components=8)
qc = quantum_correlation_map(df, n_blocks=3)

Holographic (3D, non‑VR)

from quickinsights import embed_3d_projection, plotly_embed_3d
emb = embed_3d_projection(df)
fig_res = plotly_embed_3d(emb["embedding"])  # {"success": True, "figure": fig}

Acceleration (GPU/Memory)

from quickinsights import gpu_available, gpu_corrcoef, memmap_array, chunked_apply
print("GPU usable:", gpu_available())
corr = gpu_corrcoef(df.to_numpy())
mmap = memmap_array('./quickinsights_output/tmp.mmap', 'float32', (1_000_000, 8))
parts = chunked_apply(lambda x: x.sum(), df.to_numpy(), chunk_rows=50_000)

Dependencies

• Core: pandas>=1.3.0, numpy>=1.20.0, matplotlib>=3.3.0
• Visualization: seaborn>=0.11.0, plotly>=5.0.0
• Scientific: scipy>=1.7.0
• Optional: numba, dask, cupy, boto3, azure-storage-blob, google-cloud-storage

Documentation

For detailed API documentation, see docs/API_REFERENCE.md.

For command list, see COMMANDS.md.

Contributing

To contribute, please read CONTRIBUTING.md.

License

This project is licensed under the MIT License.

Support

• GitHub Issues: https://github.com/erena6466/quickinsights/issues
• Documentation: docs/ folder
• Examples: examples/ folder

Project Status

• Core Library: Completed
• Modular Architecture: Completed
• Test Suite: 100% success rate
• PyPI Release: Version 0.1.1 available
• Documentation: Comprehensive documentation

Future Plans

• Enhanced ML algorithms
• Web dashboard interface
• Performance benchmarks
• Community building
• Additional data sources

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QuickInsights - Simplifying data analysis and enhancing performance with Python 🚀