khadee-eda 1.0.1

Deep Insights EDA — Comprehensive data profiling with global AI techniques

🔬 Khadee EDA — Deep Insights Data Profiling & Cleaning

khadee-eda is a next-generation, high-performance exploratory data analysis (EDA) and data cleaning library. It generates stunning, glassmorphism-themed interactive HTML profiling reports from any dataset and provides a robust, lightweight suite of cleaning tools equivalent to dataprep.clean.

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⚡ Quick Start

1. Generating a Profiling Report

Auto-detects and loads data from CSV, Excel, JSON, Parquet, SQLite, and 10+ other formats.

from khadee_eda import ProfileReport

# Method A: Direct one-liner from file path
report = ProfileReport("train.csv", title="E-Commerce Analysis")
report.to_html("report.html")

# Method B: From an existing Pandas DataFrame
import pandas as pd
df = pd.read_csv("train.csv")
report = ProfileReport(df, title="Customer Profiles")
report.to_html("report.html")

2. High-Performance Data Cleaning (khadee_eda.clean)

Direct, unified API for cleaning, standardizing, and preparing data (a lightweight alternative to dataprep.clean).

from khadee_eda import clean

# Clean column headers (standardize to snake_case, PascalCase, camelCase, etc.)
df_clean = clean.clean_headers(df, case="snake")

# Impute missing values with mean, median, mode, or constant value
df_clean = clean.clean_missing(df_clean, columns=["age", "income"], strategy="median")

# Handle outliers by clipping (winsorization) or dropping rows
df_clean = clean.clean_outliers(df_clean, columns=["fare"], method="iqr", strategy="clip")

# Normalize and clean text columns (strip spaces, lowercase, remove special characters)
df_clean = clean.clean_text(df_clean, columns=["product_desc"], lowercase=True, remove_special=True)

# Remove duplicate rows
df_clean = clean.clean_duplicates(df_clean, columns=["user_id"])

# Run a complete, standard cleanup pass
df_clean = clean.clean_df(df)

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📊 10 Structured Analysis Sections

Each HTML report is divided into 10 structured, deeply interactive sections:

1. 🏠 Overview: High-level dataset shapes, reproduction metadata, alerts (missing cells, zero values, extreme correlations, duplicates), and detected data types.
2. 📊 Variables (Interactive Dropdown Explorer): Detailed statistics per variable (quantiles, descriptives, frequencies, categories). Includes a custom select dropdown menu to show/hide column details and dynamically resize Plotly visualizations.
3. 📈 Distributions: Visual analysis of distributions via histogram grids, kernel density estimations (KDE), skewness, kurtosis, and normality tests.
4. 🔗 Correlations: Pairwise comparison matrices using Pearson, Spearman, Kendall, and Cramér's V metrics represented as interactive heatmaps.
5. ❓ Missing Values: Visual representation of missing data via matrices, counts, and imputation recommendations.
6. 🎯 Outliers: Deep outlier diagnostic detailing detection using IQR, Z-score, Median Absolute Deviation (MAD), and Isolation Forest.
7. 🔄 Interactions: Interactive bivariate scatter plots and grouped box plots.
8. 📐 Advanced Stats (Global AI Hub Methodologies): Unique statistical and machine learning frameworks tailored after analytical cultures across the globe (see below).
9. 🤖 Model Readiness: Preprocessing checklists, ML model suitability rankings, and code recommendation generators.
10. 📋 Sample: Interactive data table viewer showing the head, tail, duplicates, and data dictionary.

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🌍 Global EDA Techniques

The Advanced Stats section includes 4 distinct regional analytical philosophies:

• 🇺🇸 US (ML-Readiness & Feature Engineering): Identifies feature importance, flags target leakage, and proposes engineered features.
• 🇮🇳 India (Statistical Foundations & Hypothesis Testing): Evaluates confidence intervals, conducts hypothesis testing, and fits target distributions.
• 🇯🇵 Japan (Quality Control & Process Analytics — Kaizen): Implements Shewhart control charts, calculates Process Capability Indexes ($C_p$/$C_{pk}$), checks stability indicators, and generates Pareto charts.
• 🇨🇳 China (Large-Scale Pattern Recognition): Generates PCA projections, evaluates Hopkins clustering statistics, provides K-Means elbow curves, and profiles data density.

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📂 Supported Formats

No need to write separate loading code. khadee-eda automatically detects your dataset extension and uses optimized engines to parse it:

Format	Extensions	Parser
CSV / TSV	.csv, .tsv, .txt	Pandas optimized parser with latin-1 fallback
Excel	.xlsx, .xls, .xlsm, .xlsb	openpyxl / xlrd engine
JSON	.json	Standard and JSON-lines parsed dynamically
Parquet / Feather	.parquet, .feather	PyArrow engine
SQLite	.db, .sqlite, .sqlite3	Built-in SQLite connection reader
Pickle	.pkl, .pickle	Standard Python pickle serializer
Others	.h5, .hdf5, .xml, .dta, .sas7bdat, .sav	Supporting PyTables, XML, Stata, SAS, SPSS

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💾 Package Footprint & Download Size

Unlike heavier packages that bundle thick C++ binaries, khadee-eda is designed to be incredibly lightweight and fast to download.

1. Download Size (Pip / UV)

• Wheel Size (.whl): ~85 KB
• Source Distribution (.tar.gz): ~90 KB
• Package Source Size: ~170 KB (Clean, pure Python logic + minimal glassmorphism style assets)

2. Dependency Size

If your machine already has standard data science packages (like pandas, numpy, scipy) cached, the installation completes instantly (~85 KB download). If installing into a blank virtual environment, pip/uv will download the scientific stack:

Dependency	Purpose	Download Size (Approx.)
pandas	Data manipulation & structure	~12 - 15 MB
numpy	Array computations	~14 - 18 MB
scipy	Advanced statistics & tests	~35 - 40 MB
scikit-learn	Machine learning engines & PCA	~7 - 9 MB
plotly	Dynamic SVG visualizations	~7 - 8 MB
pyarrow	High-performance Parquet storage	~30 - 35 MB
openpyxl	Excel read/write compatibility	~2 - 3 MB
jinja2	HTML templating engine	~0.2 MB
Total Dependencies	Full Scientific Stack	~110 - 130 MB

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⚙️ Selective Reports

Save compute time and reduce HTML sizes for large datasets by only rendering the sections or techniques you need:

from khadee_eda import ProfileReport

# Profile only Specific Sections
report = ProfileReport(
    "dataset.csv", 
    sections=["overview", "variables", "model_readiness"]
)

# Render only Specific Global Techniques
report = ProfileReport(
    "dataset.csv",
    techniques=["japan", "us"]
)

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💎 Design & Visual Performance Excellence

• Glassmorphism Dark Theme: Standard EDA reports often look like boring 2010 tables. khadee-eda features a high-end, dark glassmorphism dashboard with neon accents, dynamic hover states, and smooth CSS micro-animations.
• Instant PDF Export: Features a beautiful floating "Download PDF" button that triggers browser printing. The custom media print styles automatically expand all hidden column cards, expand all tabs, hide navigational elements/dropdowns, and switch to a crisp ink-saving light template for a clean, professional corporate report.
• WebGL Crash Mitigation: Rendering dozens of ScatterGL plots on a single page causes modern browsers to exceed their WebGL context limit, crash, and display blank charts. khadee-eda compiles Scatter plots to optimized vector SVG path strings, ensuring 100% chart rendering reliability without sacrificing interactive zoom or hover features.
• Smart Dropdown Selectors: Instead of scrolling endlessly through dozens of columns, the report includes a dynamic select element to view one variable card at a time, instantly resizing the embedded Plotly chart to prevent layout distortions.
• Copyable Preprocessing Recommender: When the library suggests cleaning operations (e.g., standardizing headers or imputing values), it displays a syntax-highlighted code block with a one-click copy button, generating context-aware code ready for your pipeline.

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📦 Installation

To install khadee-eda in development mode locally:

git clone https://github.com/khadee/khadee-eda.git
cd khadee_EDA
pip install -e .

To install directly using uv (recommended for extreme speed):

uv pip install -e .

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📄 License

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