Advanced Data Analysis Pipeline

A comprehensive, object-oriented data analysis pipeline for professional data scientists and analysts. This all-in-one Python script provides end-to-end capabilities for data extraction, cleaning, analysis, visualization, and reporting.

🚀 Features

• Multi-source Data Extraction: Import data from CSV, JSON, APIs, and databases
• Robust Data Cleaning: Comprehensive preprocessing with configurable options
• Exploratory Data Analysis: Automated statistical summaries and data profiling
• Advanced Statistical Analysis: T-tests, ANOVA, chi-square, correlation, and regression
• Visualization Generation: Multiple chart types with customization options
• Automated Reporting: Generate comprehensive HTML reports with findings
• Multiple Export Formats: Share results as CSV, Excel, JSON, or HTML

📋 Requirements

• Python 3.7+
• pandas
• numpy
• matplotlib
• seaborn
• scipy
• statsmodels
• scikit-learn
• requests (for API data extraction)

🔧 Installation

# Create a requirements.txt file with the following content:
pandas>=1.3.0
numpy>=1.20.0
matplotlib>=3.4.0
seaborn>=0.11.0
scipy>=1.7.0
statsmodels>=0.12.0
scikit-learn>=0.24.0
requests>=2.25.0

# Install dependencies
pip install -r requirements.txt

# Download the DataAnalysisPipeline.py file
# No installation needed - just import and use!

📊 Usage Examples

Basic Pipeline Setup

from DataAnalysisPipeline import DataAnalysisPipeline

# Initialize the pipeline
pipeline = DataAnalysisPipeline()

# Extract data from multiple sources
data_sources = {
    'sales_data': {
        'type': 'csv',
        'path': 'data/sales_data.csv'
    },
    'customer_info': {
        'type': 'json',
        'path': 'data/customers.json'
    }
}
pipeline.extract_data(data_sources)

Data Cleaning

# Define cleaning steps
cleaning_steps = {
    'sales_data': [
        {'type': 'drop_duplicates'},
        {'type': 'drop_na', 'subset': ['date', 'product', 'revenue']},
        {'type': 'fill_na', 'columns': {'quantity': 0}},
        {'type': 'convert_types', 'conversions': {'revenue': 'float', 'quantity': 'int'}}
    ]
}

# Apply cleaning steps
pipeline.clean_data(cleaning_steps)

Statistical Analysis

# Perform exploratory data analysis
pipeline.perform_eda('sales_data')

# Run statistical tests
analysis_config = {
    'revenue_by_region': {
        'type': 'anova',
        'dataset': 'sales_data',
        'group_column': 'region',
        'value_column': 'revenue'
    },
    'sales_correlation': {
        'type': 'correlation',
        'dataset': 'sales_data',
        'columns': ['revenue', 'quantity', 'customer_satisfaction']
    }
}
pipeline.perform_statistical_analysis(analysis_config)

Visualization and Reporting

# Create visualizations
visualization_config = {
    'monthly_sales': {
        'type': 'line',
        'dataset': 'sales_data',
        'x_column': 'month',
        'y_column': 'revenue',
        'title': 'Monthly Sales Performance',
        'output_file': 'visualizations/monthly_sales.png'
    },
    'region_comparison': {
        'type': 'bar',
        'dataset': 'sales_data',
        'x_column': 'region',
        'y_column': 'revenue',
        'title': 'Revenue by Region',
        'output_file': 'visualizations/region_comparison.png'
    }
}
pipeline.create_visualizations(visualization_config)

# Generate a comprehensive report
report_config = {
    'title': 'Sales Analysis Report - Q1 2024',
    'introduction': 'This report analyzes sales performance across regions and products.',
    'sections': [
        {
            'type': 'datasets',
            'title': 'Data Overview',
            'datasets': ['sales_data']
        },
        {
            'type': 'eda',
            'title': 'Exploratory Analysis',
            'dataset': 'sales_data'
        },
        {
            'type': 'statistical_analysis',
            'title': 'Statistical Findings',
            'analyses': ['revenue_by_region', 'sales_correlation']
        },
        {
            'type': 'visualizations',
            'title': 'Visual Insights',
            'visualizations': ['monthly_sales', 'region_comparison']
        },
        {
            'type': 'conclusion',
            'title': 'Conclusion',
            'content': 'Based on our analysis, the Western region shows the highest revenue growth...'
        }
    ]
}
pipeline.generate_report(report_config, 'reports/q1_sales_analysis.html')

📝 Configuration Options

The pipeline uses configuration dictionaries to control its behavior. Here are some key configuration options:

Data Source Types

• csv: CSV files with customizable parameters
• json: JSON files or arrays
• api: RESTful API endpoints with authentication
• database: SQL database connections

Cleaning Operations

• drop_duplicates: Remove duplicate rows
• drop_na: Remove rows with missing values
• fill_na: Fill missing values (mean, median, mode, or custom)
• rename_columns: Rename dataframe columns
• convert_types: Convert column data types
• filter_rows: Filter based on conditions
• transform_column: Apply transformations (log, sqrt, standardize, etc.)

Statistical Tests

• ttest: Independent t-tests
• anova: One-way ANOVA with post-hoc tests
• chi_square: Chi-square test of independence
• correlation: Pearson, Spearman, or Kendall correlations
• regression: OLS regression analysis

Visualization Types

• histogram: Distribution visualization
• scatter: Relationship between variables
• bar: Category comparisons
• line: Time series or trend analysis
• heatmap: Correlation visualization
• box: Distribution and outlier analysis
• pie: Composition visualization

🔍 Advanced Usage

Using External Configuration Files

import json

# Load configuration from JSON file
with open('config/analysis_config.json', 'r') as f:
    config = json.load(f)

# Initialize pipeline
pipeline = DataAnalysisPipeline()

# Run the complete pipeline using configuration
pipeline.extract_data(config['data_sources'])
pipeline.clean_data(config['cleaning_steps'])
pipeline.perform_statistical_analysis(config['analyses'])
pipeline.create_visualizations(config['visualizations'])
pipeline.generate_report(config['report'], config['output_file'])

Error Handling and Logging

The pipeline provides comprehensive logging to help debug issues:

# Logs are automatically saved to data_pipeline.log
# You can check specific log levels:
import logging
logging.getLogger('data_pipeline').setLevel(logging.DEBUG)

🚩 Future Improvements

A professional implementation would split this single file into a proper Python package structure:

data_analysis_pipeline/
├── __init__.py
├── pipeline.py          # Main pipeline class
├── extractors/          # Data extraction modules
├── transformers/        # Data cleaning operations
├── analyzers/           # Statistical analysis modules
├── visualizers/         # Visualization generators
└── reporters/           # Report generation utilities

This modular architecture would improve maintainability and extensibility, making it easier to:

• Add new data source types
• Implement additional statistical tests
• Create new visualization types
• Extend reporting capabilities

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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This data analysis pipeline was created as part of a professional portfolio project demonstrating advanced Python and data analysis skills.