insightfulpy 0.1.8

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

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.

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

pip install insightfulpy

Quick Start

import pandas as pd
import insightfulpy as ipy

# Load your data
df = pd.read_csv('your_data.csv')

# Basic data exploration
ipy.columns_info('My Dataset', df)
ipy.num_summary(df)
ipy.cat_summary(df)

# Data quality checks
ipy.missing_inf_values(df)
ipy.detect_outliers(df)

# Visualization
ipy.show_missing(df)
ipy.plot_boxplots(df)
ipy.kde_batches(df, batch_num=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:

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+
• pandas >= 1.3.0
• numpy >= 1.20.0
• matplotlib >= 3.3.0
• seaborn >= 0.11.0
• scipy >= 1.7.0
• Additional dependencies: researchpy, tableone, missingno, tabulate

Contributing

Contributions are welcome! Please read contributing guidelines and submit pull requests to GitHub repository.

Related links

• For detailed documentation and examples, visit GitHub repository.

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

• If you encounter any issues or have questions, please open an issue on GitHub Issues page.

InsightfulPy makes data exploration intuitive and comprehensive. Start exploring your data with confidence today.