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.
1 maintainer
Metadata
1 maintainer
Release files for chokkhu 0.3.38
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| chokkhu-0.3.38.tar.gz | 36.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| chokkhu-0.3.38-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 84.2 kB
Release files / chokkhu-0.3.38.tar.gz
| Download URL | chokkhu-0.3.38.tar.gz |
|---|---|
| Size | 36.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
2f272f020a63737595825436927aa57bc25bd368609181515a5afe07bb735ba9
|
|
BLAKE2b-256 checksum How to use checksums |
6c36dc10eb6981533f6be68ec75f3533bce7df5767c26010413b26f18eee589f
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Release files / chokkhu-0.3.38-py3-none-any.whl
| Download URL | chokkhu-0.3.38-py3-none-any.whl |
|---|---|
| Size | 47.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
ffde01a4c3f6c8b9ab8995b5d7818761e9e4d53ee1a9836c7c6e5258560ddcaa
|
|
BLAKE2b-256 checksum How to use checksums |
1d1838db3c9b2bbe930bdd7138d797452648314265329ee5414c77fe3010be79
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
