# myeda

A lightweight Exploratory Data Analysis (EDA) library that provides one-line statistical summaries and optional visualizations for faster data understanding.

# Features

One-line EDA summaries

Missing value analysis

Descriptive statistics

Optional visualizations (explicit, not automatic)

Clean, modular API

Beginner-friendly and extensible

# Installation
pip install myeda



# Basic Usage
import pandas as pd
from myeda import overview, report

df = pd.read_csv("titanic_dataset.csv")

overview(df)
report(df)

# Visualizations (Explicit & Optional)

from myeda.viz import (
    plot_numeric_distribution,
    plot_boxplot,
    plot_categorical_counts,
    plot_correlation_heatmap
)

plot_numeric_distribution(df, "Age")
plot_boxplot(df, "Fare")
plot_categorical_counts(df, "Sex")
plot_correlation_heatmap(df)


Visualizations are never automatic — you control when to plot.

# Project Structure

EDA/
|-- examples/
|   |-- titanic_dataset.csv
|   `-- titanic_demo.ipynb
|
|-- myeda/
|   |-- __init__.py
|   |-- report.py
|   |
|   |-- core/
|   |   |-- overview.py
|   |   |-- missing.py
|   |   `-- statistics.py
|   |
|   `-- viz/
|       `-- visualization.py
|
|-- tests/
|   `-- test_statistics.py
|
|-- setup.py
|-- pyproject.toml
|-- requirements.txt
|-- README.md
|-- LICENSE
`-- .gitignore




# Module Responsibilities
### core/overview.py

Dataset shape

Column types

Basic dataset information

### core/missing.py

Missing value counts

Missing percentage per column

### core/statistics.py

Mean, median, mode

Variance, standard deviation

Numerical summaries

### viz/visualization.py

Numeric distributions

Boxplots

Categorical counts

Correlation heatmaps

# Examples

Check the examples/ directory for:

Titanic dataset

Jupyter notebook demonstrating full EDA workflow

# Testing
pytest

# License

This project is licensed under the MIT License.

# Author

Khaja Mubashir Arsalan