📊 Data Analysis — Python Data Analysis Toolkit Demo

• Data Loading & Cleaning — Multi-format loaders with automatic type inference and validation
• Exploratory Data Analysis — Automated profiling, distribution analysis, correlation matrices
• Statistical Testing — t-tests, chi-square, ANOVA, normality checks with plain-English interpretation
• Visualization Recipes — 15+ chart templates for Matplotlib/Seaborn, ready for reports and presentations
• Sample Data — Included CSV dataset for testing all modules immediately

pip install -r requirements.txt
python -m src.datkit.main

src/datkit/
├── __init__.py         # Package exports
├── main.py             # Demo runner showcasing all modules
├── loading.py          # Data loading utilities (CSV, Excel, JSON, Parquet)
├── cleaning.py         # Missing values, type coercion, deduplication
├── eda.py              # Automated EDA profiling and summaries
├── distributions.py    # Distribution fitting and normality testing
├── correlations.py     # Correlation analysis with significance testing
├── statistics.py       # Hypothesis testing helpers
├── visualization.py    # Chart recipes and styling
notebooks/
├── 01_loading_and_cleaning.py   # Guided walkthrough of data loading
├── 02_eda_workflow.py           # Complete EDA workflow example
├── 03_statistical_testing.py    # Hypothesis testing examples
tests/
├── test_cleaning.py    # Unit tests for cleaning module
├── test_statistics.py  # Unit tests for statistical functions
data/
├── sample_saas_metrics.csv  # 1000-row sample dataset
guides/
├── choosing_the_right_chart.md  # Decision framework for visualization
├── statistical_tests_cheatsheet.md  # When to use which test