myeda 0.1.8

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

How users can import EVERYTHING (README-ready)

Dataset overview

from myeda import dataset_overview

dataset_overview(df)

Missing-value analysis

from myeda import missing_overview, missing_summary

missing_overview(df)
missing_summary(df)

Statistical summaries

from myeda import numeric_summary, categorical_summary

numeric_summary(df)
categorical_summary(df)

Visualizations (explicit & optional)

from myeda 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)

Full EDA (recommended)

from myeda import EDAReport

eda = EDAReport(df)
results = eda.run()

Testing

pytest

License

This project is licensed under the MIT License.

Author

Khaja Mubashir Arsalan