myeda 0.1.5

One-line Exploratory Data Analysis (EDA) library

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