InsightfulPy

A comprehensive Python toolkit for exploratory data analysis with advanced visualization and statistical analysis capabilities.

Overview

InsightfulPy simplifies the process of exploring and understanding your data through intuitive functions for statistical analysis, data quality assessment, and professional visualization. Whether you're a data scientist, analyst, or researcher, this package provides the tools you need for thorough data exploration.

Documentation Navigation

| Document | Description | |--------------------------------------------------------------------------|----------------------------------------------------| | Overview | Package introduction, features, and architecture | | Installation Guide | Installation instructions and setup verification | | Quick Start | Basic workflow and essential functions tutorial | | User Guide | Complete workflow tutorial with step-by-step examples | | API Reference | Detailed function documentation and parameters | | Contributing | Guidelines for contributing to the project |

Examples

• Quick Examples - Additional usage scenarios
• Output Results Gallery - Some output results
• diagram Gallery - diagram collection

Key Features

• Statistical Analysis: Comprehensive statistics, distribution analysis, and normality testing
• Data Quality Assessment: Missing value detection, outlier identification, and data type validation
• Professional Visualization: Box plots, distribution plots, correlation analysis, and categorical charts
• Dataset Comparison: Multi-dataset analysis and column linking capabilities
• Batch Processing: Handle large datasets with intelligent batching for visualizations
• Easy Integration: Works seamlessly with pandas DataFrames

Installation

bash pip install insightfulpy

Quick Start

```python import pandas as pd import insightfulpy as ipy

Load your data

df = pd.readcsv('yourdata.csv')

Basic data exploration

ipy.columnsinfo('My Dataset', df) ipy.numsummary(df) ipy.cat_summary(df)

Data quality checks

ipy.missinginfvalues(df) ipy.detect_outliers(df)

Visualization

ipy.showmissing(df) ipy.plotboxplots(df) ipy.kdebatches(df, batchnum=1) ```

Core Functions

Basic Analysis

• num_summary(df) - Statistical summary of numerical columns
• cat_summary(df) - Analysis of categorical columns
• columns_info(title, df) - Dataset structure overview
• missing_inf_values(df) - Missing and infinite value detection
• detect_outliers(df) - Outlier identification using IQR method

Visualization

• show_missing(df) - Missing data pattern visualization
• plot_boxplots(df) - Box plots for all numerical columns
• kde_batches(df) - Distribution plots organized in batches
• cat_bar_batches(df) - Bar charts for categorical data
• cat_pie_chart_batches(df) - Pie charts for categorical analysis

Advanced Analysis

• grouped_summary(df, groupby) - Statistical analysis by groups
• compare_df_columns() - Multi-dataset comparison
• interconnected_outliers() - Cross-column outlier analysis
• num_vs_num_scatterplot_pair_batch() - Numerical correlation plots
• cat_vs_cat_pair_batch() - Categorical relationship heatmaps

Statistical Tools

• calc_stats(series) - Comprehensive statistical calculations
• calculate_skewness_kurtosis(df) - Distribution shape analysis
• iqr_trimmed_mean(data) - Robust mean calculation
• mad(data) - Mean absolute deviation

Help System

InsightfulPy includes a built-in help system for easy reference: ```python import insightfulpy as ipy

Get help overview

ipy.help()

List all functions

ipy.list_all()

Quick start guide

ipy.quick_start()

Usage examples

ipy.examples() ```

Requirements

Python 3.8+ with pandas (≥1.3), numpy (≥1.20), matplotlib (≥3.3), seaborn (≥0.11), scipy (≥1.7), plus researchpy, tableone, missingno, and tabulate.

Related links:

• For detailed documentation and examples, visit GitHub repository.
• Contributions are welcome! Please read contributing guidelines and submit pull requests to GitHub repository.
• If you encounter any issues or have questions, please open an issue on GitHub Issues page.
• This project is licensed under the MIT License - see the LICENSE file for details.

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Package Information:

Version: 0.1.8 | Author: Dhanesh B. B. | License: MIT | Python: 3.8+

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InsightfulPy makes data exploration intuitive and comprehensive.