Chokkhu

A Professional Statistical EDA Toolkit for Computer Vision & Tabular Datasets

Industry-grade data analysis and visual reports generation.

Overview

Chokkhu is a high-performance Python package designed to streamline Exploratory Data Analysis (EDA).

Designed to be incredibly lightweight without relying on massive machine learning frameworks (No TensorFlow, PyTorch, or Scikit-Learn dependencies), it perfectly generates unified, modular statistical reports.

Installation

Install Chokkhu via pip.

pip install chokkhu

Quick Start: Exploratory Data Analysis (EDA)

Chokkhu provides a unified, clean API for generating EDA reports.

1. Tabular EDA

Generate a comprehensive suite of statistical charts (Univariate, Bivariate, Multivariate, Outliers) to understand your CSV datasets in one simple call:

import chokkhu as ck

csv_dataset_path = '/kaggle/input/datasets/laveshjadon/ai-impact-on-students/ai_student_impact_dataset (1).csv'

tabular_eda = ck.eda.tabular(
    dataset_path=csv_dataset_path, 
    save_reports=True, 
    save_dir='/kaggle/working/tabular_reports',
    target_col='Pre_Semester_GPA'
)

Parameters:

dataset_path (str): Path to your CSV file.
save_reports (bool): Whether to save the plots to the disk.
save_dir (str): Directory where reports will be saved automatically.
target_col (str, optional): If provided, Chokkhu performs target-based analysis.

2. Image EDA

Analyze image data quality, structural integrity, spatial textures, and color profiles for your datasets. Each topic metric is plotted cleanly and saved directly.

import chokkhu as ck

image_dataset_path = '/kaggle/input/datasets/nirmalsankalana/sugarcane-leaf-disease-dataset'

image_eda = ck.eda.image(
    dataset_path=image_dataset_path, 
    save_reports=True, 
    save_dir='/kaggle/working/image_reports'
)

Parameters:

dataset_path (str): Path to the root folder of your image dataset.
save_reports (bool): Whether to save the plots to the disk.
save_dir (str): Directory where all EDA reports will be saved automatically.

Contributing

We welcome contributions! Please check out the Issues page if you'd like to help improve Chokkhu.

License

Distributed under the MIT License. See LICENSE for more information.

Metadata

Release files for chokkhu 0.3.31

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for chokkhu 0.3.31
File	Size	Uploaded
chokkhu-0.3.31.tar.gz	39.1 kB	Aug 20, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for chokkhu 0.3.31
File	Interpreter	ABI	Platform	Reset
chokkhu-0.3.31-py3-none-any.whl 49.7 kB Aug 20, 2026	Python 3	none	any	Details

Total release size: 88.8 kB

Release files / chokkhu-0.3.31.tar.gz

Download URL	chokkhu-0.3.31.tar.gz
Size	39.1 kB
Tags	Source
SHA-256 checksum How to use checksums	`06f7f4f5f8376469dce22ac6263994257e0b08bb77b25d1000b12c1bc5d29076`
BLAKE2b-256 checksum How to use checksums	`e13541fca16fe22beb2e2b9a76080c727b65185c19ca47863ac525724c9e8196`
Upload date	Aug 20, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Release files / chokkhu-0.3.31-py3-none-any.whl

Download URL	chokkhu-0.3.31-py3-none-any.whl
Size	49.7 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`d4bb9fb325db9e03c4cdfe11353df0ef93b0da5b3017d65708dfe08ea2329196`
BLAKE2b-256 checksum How to use checksums	`921a93316446509ff39c0d8dd1e8c08985e048452666b4ad9ba59c4c1233839d`
Upload date	Aug 20, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.13.14`